{"meta":{"query_hash":"a62c199a1ef5","filters":{"topic":"Sentiment Analysis and Opinion Mining"},"cohort_total":797,"direct_labels_cover":0,"predictions_cover":797,"exported":797,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/a62c199a1ef5","api":"https://metacan.xera.ac/api/v1/cohort?topic=Sentiment+Analysis+and+Opinion+Mining"},"results":[{"id":"W1055035554","doi":"10.1007/s00500-015-1812-4","title":"Hierarchical classification in text mining for sentiment analysis of online news","year":2015,"lang":"en","type":"article","venue":"Soft Computing","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Sentiment analysis; Class (philosophy); Task (project management); Artificial intelligence; Binary classification; Polarity (international relations); Tone (literature); Data mining; Information retrieval; Natural language processing; Machine learning; Support vector machine","score_opus":0.07647598286024078,"score_gpt":0.3373545197430311,"score_spread":0.26087853688279034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1055035554","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11305495,0.0017803605,0.87118626,0.00087938015,0.00031053042,0.00089678547,0.002802379,0.0033111274,0.005778259],"genre_scores_gemma":[0.4263814,0.00062256405,0.5607311,0.0003103852,0.0003607876,0.00076740154,0.0054039573,0.00026471945,0.0051576425],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997993,0.00069197407,0.00026832358,0.00031709915,0.00046235084,0.00026733996],"domain_scores_gemma":[0.9957783,0.0022774318,0.00035100395,0.00038575762,0.0010177346,0.00018961806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002306637,0.0007287751,0.0011360764,0.004817311,0.0018154538,0.0017452419,0.0008662393,0.00072294346,0.0037863425],"category_scores_gemma":[0.0075909,0.0004119286,0.001655081,0.004179657,0.0004633993,0.0017936467,0.0012018112,0.0015795572,0.0024202967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005705438,0.00094376237,0.018938558,0.0005662933,0.0003145957,0.0002190862,0.0007178513,0.016644215,0.023397187,0.012247648,0.017435713,0.9080045],"study_design_scores_gemma":[0.00010336468,0.0003912992,0.022696493,0.00014251706,0.0003743287,0.00021288492,0.00066262245,0.89468855,0.018083667,0.051067512,0.011496849,0.00007996282],"about_ca_topic_score_codex":0.008263696,"about_ca_topic_score_gemma":0.012926403,"teacher_disagreement_score":0.008263696,"about_ca_system_score_codex":0.0011626557,"about_ca_system_score_gemma":0.0019475938,"threshold_uncertainty_score":0.016431212},"labels":[],"label_agreement":null},{"id":"W135937222","doi":"10.1007/978-3-642-21043-3_8","title":"Using a Heterogeneous Dataset for Emotion Analysis in Text","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":150,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Disgust; Sadness; Surprise; Computer science; Support vector machine; Anger; Happiness; Emotion classification; Artificial intelligence; Sentiment analysis; Classifier (UML); Emotion detection; Machine learning; Natural language processing; Emotion recognition; Psychology; Social psychology","score_opus":0.0635249034249282,"score_gpt":0.3016810333679441,"score_spread":0.2381561299430159,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W135937222","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5666075,0.002815135,0.16757946,0.0023004175,0.002745432,0.0019858005,0.23215854,0.008845051,0.014962701],"genre_scores_gemma":[0.44874987,0.00073358737,0.16112673,0.000579681,0.0009026116,0.0017295616,0.38022596,0.0004674965,0.005484497],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998161,0.00041621638,0.00022556844,0.00051861716,0.0005492226,0.00012925787],"domain_scores_gemma":[0.9969421,0.001109685,0.00022918917,0.00054281135,0.00093823293,0.00023797723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015239917,0.0011342986,0.0007991258,0.0040896637,0.0009870097,0.001784952,0.00086648035,0.0012493493,0.0029066755],"category_scores_gemma":[0.0049347975,0.0002384819,0.0009642733,0.0033027472,0.00024186222,0.0021049418,0.001633393,0.0011012092,0.0024531365],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025851766,0.0029733458,0.051672023,0.0017547727,0.0009924815,0.001794997,0.0010402744,0.009658718,0.13726792,0.0031119385,0.21176332,0.5753849],"study_design_scores_gemma":[0.0007415943,0.001703582,0.22880226,0.00047536773,0.0014294959,0.0028359273,0.004411711,0.42660755,0.09704387,0.0146860955,0.22080271,0.0004598546],"about_ca_topic_score_codex":0.002012625,"about_ca_topic_score_gemma":0.004369305,"teacher_disagreement_score":0.0040896637,"about_ca_system_score_codex":0.0006104454,"about_ca_system_score_gemma":0.00044633832,"threshold_uncertainty_score":0.009723842},"labels":[],"label_agreement":null},{"id":"W137786571","doi":"10.1007/978-3-319-07983-7_16","title":"A Joint Topic Viewpoint Model for Contention Analysis","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Viewpoints; Joint (building); Cluster analysis; Artificial intelligence; Topic model; Task (project management); Machine learning; Natural language processing; Data mining","score_opus":0.04561638270394379,"score_gpt":0.27152724685294616,"score_spread":0.22591086414900236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W137786571","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013019092,0.0008001111,0.9803257,0.0006691101,0.00017549611,0.0001566989,0.000774645,0.0009830368,0.0030960015],"genre_scores_gemma":[0.48704895,0.0012355845,0.49142656,0.00034199803,0.0007159817,0.000755137,0.0040050754,0.00066220574,0.0138084395],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980566,0.0007375061,0.000100282494,0.00045656803,0.00041247878,0.0002365144],"domain_scores_gemma":[0.99385047,0.0042445445,0.00021887645,0.0006050188,0.00081264245,0.00026847498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037890326,0.0010989546,0.0016890874,0.0025385765,0.0009629462,0.0029263047,0.0041468837,0.0020487488,0.009480114],"category_scores_gemma":[0.013502113,0.0007294272,0.00217469,0.0035236194,0.00080769096,0.0048655192,0.0022167293,0.00310307,0.002735736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00095190026,0.0005215695,0.006181108,0.00035201042,0.00047973503,0.00034077468,0.0008696329,0.27913618,0.004870463,0.1877351,0.03384424,0.48471725],"study_design_scores_gemma":[0.000021050473,0.00001982135,0.0002082356,0.000010893225,0.000041328534,0.000022417666,0.0000263714,0.9637432,0.00019210309,0.034354504,0.0013491422,0.000010961536],"about_ca_topic_score_codex":0.012077487,"about_ca_topic_score_gemma":0.017338749,"teacher_disagreement_score":0.012077487,"about_ca_system_score_codex":0.0018439078,"about_ca_system_score_gemma":0.002442521,"threshold_uncertainty_score":0.03171414},"labels":[],"label_agreement":null},{"id":"W139446895","doi":"10.1007/978-3-319-10160-6_13","title":"Discovering Community Preference Influence Network by Social Network Opinion Posts Mining","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Popularity; Friendship; Similarity (geometry); Preference; Social network (sociolinguistics); Sentiment analysis; Service (business); Product (mathematics); Information retrieval; World Wide Web; Maximization; Social network service; Data science; Data mining; Social media; Artificial intelligence; Psychology","score_opus":0.027814156645366118,"score_gpt":0.25953015294893816,"score_spread":0.23171599630357204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W139446895","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4397316,0.0018187481,0.5368179,0.0006280766,0.00018755339,0.00041810403,0.0036505822,0.0012789189,0.015468539],"genre_scores_gemma":[0.8586048,0.0007500415,0.13101049,0.00009150552,0.00023343478,0.00019834189,0.0041479813,0.000086667496,0.0048767906],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994473,0.000101337726,0.00002685715,0.00014699204,0.0002170761,0.000060434584],"domain_scores_gemma":[0.99908185,0.00036547243,0.00013605987,0.00005795844,0.00029247903,0.00006620287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043458393,0.00066001265,0.00054066256,0.0033671549,0.0006386452,0.000931017,0.0005857389,0.00050357555,0.0014982048],"category_scores_gemma":[0.0024729148,0.00026516835,0.00073936686,0.0025980289,0.00022371927,0.0015080724,0.00052189914,0.0005607041,0.0010111762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005764283,0.00068794825,0.076410234,0.00060106604,0.0005378783,0.0011705654,0.0007672627,0.021878721,0.050667237,0.013302076,0.022781024,0.8106195],"study_design_scores_gemma":[0.000038973805,0.0002009868,0.0272285,0.000060476406,0.00031615954,0.0006781545,0.000581008,0.9266354,0.015732013,0.01682208,0.011646481,0.00005968582],"about_ca_topic_score_codex":0.002694446,"about_ca_topic_score_gemma":0.0056702956,"teacher_disagreement_score":0.0033671549,"about_ca_system_score_codex":0.0004305729,"about_ca_system_score_gemma":0.00041698254,"threshold_uncertainty_score":0.005357504},"labels":[],"label_agreement":null},{"id":"W1498362350","doi":"10.1007/11766247_29","title":"Sentiment Tagging of Adjectives at the Meaning Level","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Meaning (existential); Natural language processing; Artificial intelligence; Linguistics; Information retrieval; Philosophy; Epistemology","score_opus":0.029198624072144092,"score_gpt":0.2574771449464802,"score_spread":0.22827852087433612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1498362350","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1744831,0.0028962847,0.71234,0.002082767,0.0035385669,0.00049342366,0.006364459,0.003687579,0.09411379],"genre_scores_gemma":[0.59208584,0.001974535,0.36014318,0.0006276121,0.0010420023,0.00031550182,0.010648111,0.0013365693,0.03182666],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999204,0.00020449255,0.00008175557,0.00015980103,0.00026135374,0.000088647044],"domain_scores_gemma":[0.9981359,0.00056420924,0.00014987879,0.00021397012,0.00085087656,0.00008512223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094976305,0.00063005066,0.0005366668,0.0019401725,0.00096877955,0.0021396454,0.00048098352,0.00059389236,0.0059541333],"category_scores_gemma":[0.0040270616,0.00037200376,0.00073103054,0.0023416448,0.00048808576,0.0024313407,0.00096405926,0.0012696752,0.0046899687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049895333,0.00023121777,0.0074536116,0.0009105692,0.000120819845,0.00057501026,0.0017133498,0.0013922544,0.20598593,0.033168256,0.054332424,0.6936177],"study_design_scores_gemma":[0.00011694068,0.00056645734,0.061409608,0.0010661883,0.00081816915,0.00393741,0.003800171,0.17286283,0.1989913,0.19623943,0.35983795,0.0003535623],"about_ca_topic_score_codex":0.0008078168,"about_ca_topic_score_gemma":0.0013869639,"teacher_disagreement_score":0.0059541333,"about_ca_system_score_codex":0.0003919317,"about_ca_system_score_gemma":0.00055725616,"threshold_uncertainty_score":0.01991862},"labels":[],"label_agreement":null},{"id":"W1498449133","doi":"10.1007/978-3-642-13059-5_38","title":"A Novel Approach for Recommending Ranked User-Generated Reviews","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Helpfulness; Computer science; Benchmark (surveying); Quality (philosophy); Fraction (chemistry); Population; Baseline (sea); Information retrieval; Data mining","score_opus":0.06033900035495961,"score_gpt":0.29081915128815794,"score_spread":0.23048015093319835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1498449133","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05538324,0.004446487,0.9207504,0.0009958989,0.00087163615,0.0009470097,0.003072528,0.0071014822,0.0064312574],"genre_scores_gemma":[0.32305434,0.001474404,0.6524034,0.0006115695,0.0011567478,0.0006108444,0.0039888755,0.00022404027,0.016475841],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980702,0.00027635865,0.00014674429,0.00044324182,0.00094783824,0.00011574756],"domain_scores_gemma":[0.9968496,0.0010655422,0.0001768422,0.00023760865,0.0015581768,0.00011225079],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014531147,0.0012579784,0.001863596,0.0050222436,0.00076808984,0.0017574398,0.001832792,0.0017506351,0.0029048587],"category_scores_gemma":[0.0054336702,0.0005699087,0.0011382592,0.0044530174,0.0003000597,0.0016256657,0.00073485746,0.001088387,0.002682123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005497996,0.0007054727,0.0069394517,0.0004234799,0.0005853239,0.00027231517,0.00021354212,0.010639174,0.019167203,0.0024181702,0.033631377,0.9244547],"study_design_scores_gemma":[0.00014188417,0.0005327717,0.0056348243,0.00007211773,0.00042306428,0.0008037205,0.000117111,0.95786726,0.012760506,0.004696507,0.016842304,0.00010788812],"about_ca_topic_score_codex":0.008253444,"about_ca_topic_score_gemma":0.024296124,"teacher_disagreement_score":0.008253444,"about_ca_system_score_codex":0.0006713748,"about_ca_system_score_gemma":0.0012260374,"threshold_uncertainty_score":0.016410828},"labels":[],"label_agreement":null},{"id":"W1546405367","doi":"10.1609/icwsm.v7i2.14468","title":"Using Nuances of Emotion to Identify Personality","year":2021,"lang":"en","type":"article","venue":"Proceedings of the International AAAI Conference on Web and Social Media","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Admiration; Affect (linguistics); Psychology; Personality; Big Five personality traits; Cognitive psychology; Social psychology; Communication","score_opus":0.10261901747937799,"score_gpt":0.3464134560927968,"score_spread":0.2437944386134188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1546405367","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95686567,0.0006760219,0.03401019,0.00019252588,0.00006912027,0.000080238286,0.00080463465,0.00027894255,0.0070226537],"genre_scores_gemma":[0.98852634,0.00016670143,0.009475255,0.000031674386,0.00003678034,0.00003022861,0.0005023267,0.0000128827505,0.0012178494],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995189,0.0001440452,0.000042439293,0.00011097055,0.00012918214,0.00005458795],"domain_scores_gemma":[0.9971927,0.0012846281,0.0006116317,0.00020086636,0.00049531355,0.00021490175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092089165,0.00058611814,0.00035335537,0.0017418591,0.0003068618,0.0010561182,0.00015089323,0.00035725065,0.0018291902],"category_scores_gemma":[0.0039893575,0.000116561794,0.0003116148,0.0006535536,0.00020663062,0.00084852177,0.0005500021,0.0004634086,0.0008641046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061301456,0.00025632145,0.6158696,0.00023220116,0.00019991028,0.00021503164,0.0013289694,0.0019941027,0.054566268,0.00073202845,0.0024713422,0.3215212],"study_design_scores_gemma":[0.000016178372,0.00042217167,0.920238,0.00006080568,0.000097898665,0.0005908906,0.0012486031,0.06034305,0.010450475,0.002252178,0.0042208936,0.000058864334],"about_ca_topic_score_codex":0.0008089546,"about_ca_topic_score_gemma":0.0015622609,"teacher_disagreement_score":0.0018291902,"about_ca_system_score_codex":0.00016274245,"about_ca_system_score_gemma":0.00010694742,"threshold_uncertainty_score":0.0061191916},"labels":[],"label_agreement":null},{"id":"W1559175876","doi":"10.3765/exabs.v0i0.2391","title":"Discourse structure and attitudinal valence of opinion words in sentiment extraction","year":2014,"lang":"en","type":"article","venue":"LSA Annual Meeting Extended Abstracts","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Sentiment analysis; Treebank; Polarity (international relations); Natural language processing; Computer science; Valence (chemistry); Artificial intelligence; Linguistics; Chemistry; Philosophy","score_opus":0.01157416258838695,"score_gpt":0.29522786242118737,"score_spread":0.2836536998328004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1559175876","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62691355,0.0030181555,0.3272541,0.0011686874,0.00035483245,0.0010032471,0.005370335,0.001315386,0.033601765],"genre_scores_gemma":[0.8749341,0.0007054833,0.11742854,0.00009863017,0.00015427743,0.00047188005,0.0034733778,0.00010558157,0.0026281907],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998965,0.0004527718,0.00012565605,0.0001738531,0.0002064462,0.000076246746],"domain_scores_gemma":[0.996912,0.001861744,0.00028197942,0.000108662054,0.0007709766,0.000064627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001535559,0.00057454436,0.00040024746,0.0027897698,0.0006958257,0.001422166,0.00026544457,0.00041202776,0.002711347],"category_scores_gemma":[0.008110131,0.0003350274,0.00054416736,0.0018868903,0.00033730458,0.0017591513,0.0005721699,0.00065032544,0.0011305597],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016805943,0.00038339646,0.052599203,0.001452086,0.00024407908,0.0005044724,0.006765327,0.003942916,0.19011669,0.014357239,0.01000803,0.71794593],"study_design_scores_gemma":[0.00035603414,0.0010040044,0.3684954,0.0010388503,0.001117353,0.0011577883,0.010378095,0.3104273,0.1727456,0.045378514,0.08761124,0.00028971676],"about_ca_topic_score_codex":0.0016887698,"about_ca_topic_score_gemma":0.002487986,"teacher_disagreement_score":0.0027897698,"about_ca_system_score_codex":0.0006739355,"about_ca_system_score_gemma":0.00051351404,"threshold_uncertainty_score":0.009070396},"labels":[],"label_agreement":null},{"id":"W1562192992","doi":"10.1007/978-3-642-01187-0_1","title":"Helping E-Commerce Consumers Make Good Purchase Decisions: A User Reviews-Based Approach","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in business information processing","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Helpfulness; Product (mathematics); Order (exchange); Computer science; E-commerce; Ranking (information retrieval); Task (project management); Reading (process); Advertising; Marketing; World Wide Web; Business; Information retrieval; Engineering; Psychology","score_opus":0.035288465205174646,"score_gpt":0.2737156629012034,"score_spread":0.23842719769602877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1562192992","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48087436,0.01335391,0.40239948,0.013620796,0.00049514434,0.0019586422,0.0026673896,0.0037897278,0.08084043],"genre_scores_gemma":[0.80685234,0.0019764751,0.17762193,0.00080620765,0.00034253352,0.0002697437,0.00094313803,0.00012585167,0.011061805],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974916,0.001052093,0.00012407731,0.00024858347,0.0009931795,0.00009059753],"domain_scores_gemma":[0.99045116,0.0053251176,0.0006533518,0.00022845258,0.003144312,0.00019756409],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029646845,0.0009168879,0.0011653224,0.002317998,0.0007052846,0.0027937721,0.0008941534,0.0015290573,0.0021469523],"category_scores_gemma":[0.010847,0.00048868975,0.0005838347,0.0016029463,0.00033797635,0.0028854131,0.0006094054,0.0008494889,0.0012383774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012794631,0.0021114748,0.037877835,0.0010572325,0.0006700277,0.00039050123,0.0017848967,0.008709916,0.016264208,0.0038813527,0.046152465,0.8798207],"study_design_scores_gemma":[0.00033966804,0.0018402768,0.076491214,0.0003808185,0.0021287785,0.0010184086,0.005166574,0.80406564,0.035766367,0.019485308,0.052909326,0.00040753363],"about_ca_topic_score_codex":0.004111134,"about_ca_topic_score_gemma":0.011394455,"teacher_disagreement_score":0.004111134,"about_ca_system_score_codex":0.00064210355,"about_ca_system_score_gemma":0.00086118944,"threshold_uncertainty_score":0.015678942},"labels":[],"label_agreement":null},{"id":"W1569507287","doi":"10.1111/coin.12024","title":"Using Hashtags to Capture Fine Emotion Categories from Tweets","year":2014,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":412,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Lexicon; Social media; Computer science; Emotion classification; Sentiment analysis; Big Five personality traits; Word (group theory); Microblogging; Admiration; Natural language processing; Artificial intelligence; Emotion detection; Personality; Psychology; Emotion recognition; Linguistics; Social psychology; World Wide Web","score_opus":0.06287620001503862,"score_gpt":0.31803797880602186,"score_spread":0.25516177879098323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1569507287","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8168144,0.00091875746,0.1412355,0.00047964026,0.000558344,0.0008311642,0.024613092,0.003066441,0.0114826895],"genre_scores_gemma":[0.888793,0.00039050088,0.08808088,0.00019614656,0.0002523765,0.000588224,0.016502183,0.00021825582,0.0049783355],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99926895,0.0001706232,0.00011171274,0.00018501763,0.00019135553,0.000072357565],"domain_scores_gemma":[0.9950537,0.0023067205,0.0010485958,0.00051716424,0.00088774133,0.00018603116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075762137,0.0006389436,0.00027074208,0.0035947196,0.00042756443,0.00087800145,0.0002291846,0.0004849207,0.0017947614],"category_scores_gemma":[0.004611471,0.00021517949,0.00030355222,0.0025112366,0.0003621774,0.0018843828,0.0007454214,0.00058602344,0.0016981886],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017141138,0.00041844996,0.24069348,0.001635342,0.00023377255,0.00081758836,0.0069791693,0.004307527,0.18306358,0.0053313305,0.021992091,0.53281343],"study_design_scores_gemma":[0.00011742135,0.0009422607,0.67853945,0.00023457194,0.00026490798,0.0013347993,0.0057272925,0.13045068,0.08696342,0.020864438,0.07429033,0.0002704749],"about_ca_topic_score_codex":0.0015193832,"about_ca_topic_score_gemma":0.004796027,"teacher_disagreement_score":0.0035947196,"about_ca_system_score_codex":0.0003258093,"about_ca_system_score_gemma":0.00020788092,"threshold_uncertainty_score":0.0060040355},"labels":[],"label_agreement":null},{"id":"W1576326591","doi":"10.1145/2488388.2488467","title":"The FLDA model for aspect-based opinion mining","year":2013,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":78,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Latent Dirichlet allocation; Topic model; Computer science; Artificial intelligence; Set (abstract data type); Machine learning; Probabilistic logic; Natural language processing; Cold start (automotive); Information retrieval; Data mining","score_opus":0.036776317088838065,"score_gpt":0.2753862306716626,"score_spread":0.23860991358282457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1576326591","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01888635,0.0019379429,0.9731135,0.0009862747,0.00017762782,0.00012827003,0.001225649,0.0009948376,0.0025496152],"genre_scores_gemma":[0.7301798,0.002034868,0.2497839,0.00083069934,0.0006170614,0.0006535115,0.0039001473,0.00022733587,0.011772725],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984022,0.0005440939,0.00011380337,0.0005434991,0.0002632881,0.00013322181],"domain_scores_gemma":[0.9974848,0.0014415727,0.00028107548,0.00016105134,0.00055687694,0.00007461686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020254354,0.0013296151,0.0013908903,0.0019312882,0.0004931225,0.0015468207,0.0021422552,0.0015096148,0.003212246],"category_scores_gemma":[0.007306239,0.00064563076,0.001985355,0.0018945778,0.0007894081,0.0022645,0.00082022784,0.0018870097,0.002211946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052251265,0.00022910461,0.011169962,0.0005439965,0.0005708309,0.0005711325,0.0009255281,0.3890321,0.007913643,0.06293402,0.02714919,0.498438],"study_design_scores_gemma":[0.000013236339,0.000027198135,0.0006422597,0.000013132558,0.000028976354,0.0000739796,0.000018726014,0.9807719,0.0002636683,0.01588115,0.0022497722,0.000016000828],"about_ca_topic_score_codex":0.011860859,"about_ca_topic_score_gemma":0.014449733,"teacher_disagreement_score":0.011860859,"about_ca_system_score_codex":0.0014179635,"about_ca_system_score_gemma":0.0007212349,"threshold_uncertainty_score":0.023583591},"labels":[],"label_agreement":null},{"id":"W1577585001","doi":"10.1609/icwsm.v4i1.14073","title":"Generating Domain-Specific Clues Using News Corpus for Sentiment Classification","year":2010,"lang":"en","type":"article","venue":"Proceedings of the International AAAI Conference on Web and Social Media","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Sentence; Sentiment analysis; Domain (mathematical analysis); Natural language processing; Collocation (remote sensing); Set (abstract data type); Artificial intelligence; Event (particle physics); Subject (documents); Expression (computer science); Machine learning; World Wide Web; Mathematics","score_opus":0.06962750853515684,"score_gpt":0.293273848033807,"score_spread":0.22364633949865018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1577585001","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3247955,0.0022966894,0.6327946,0.0011700934,0.0011105502,0.0015034373,0.0123146605,0.01478548,0.009228921],"genre_scores_gemma":[0.32562935,0.0011034097,0.6340766,0.00018680743,0.0004977566,0.0008443902,0.032937087,0.00059292483,0.0041316906],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991406,0.00021979424,0.00009408128,0.00022922237,0.00025786101,0.000058412665],"domain_scores_gemma":[0.9945304,0.0026355116,0.00031152501,0.0004984844,0.0017982315,0.00022580368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014268747,0.001481554,0.0011387862,0.006572106,0.0012067058,0.0012574999,0.00080135785,0.0009766272,0.0039490345],"category_scores_gemma":[0.008787495,0.00067885686,0.00080732396,0.0036308528,0.00036770012,0.0022765421,0.0011361219,0.001269884,0.003013429],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013565384,0.00079774205,0.012745877,0.0014186836,0.00018432047,0.0017627839,0.0013344926,0.013550213,0.13147731,0.005522595,0.038121354,0.79172814],"study_design_scores_gemma":[0.00041602895,0.0009636102,0.020507477,0.00038854606,0.00052894093,0.00176837,0.002187216,0.7442001,0.12595212,0.01826138,0.08456463,0.00026158468],"about_ca_topic_score_codex":0.002159663,"about_ca_topic_score_gemma":0.004336455,"teacher_disagreement_score":0.006572106,"about_ca_system_score_codex":0.00056471134,"about_ca_system_score_gemma":0.0011128093,"threshold_uncertainty_score":0.013210893},"labels":[],"label_agreement":null},{"id":"W1584559404","doi":"10.1007/978-3-540-72665-4_39","title":"Query-Based Summarization of Customer Reviews","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Automatic summarization; sort; Information retrieval; Context (archaeology); Information extraction; Architecture; Natural language processing; Product (mathematics); World Wide Web; Artificial intelligence","score_opus":0.03991587832786806,"score_gpt":0.29241929730605815,"score_spread":0.25250341897819006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1584559404","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34089482,0.018284936,0.57990086,0.0025854013,0.0016848182,0.0019011503,0.022598963,0.020757433,0.011391688],"genre_scores_gemma":[0.5701742,0.004221227,0.3511545,0.00044950028,0.0019804277,0.00070401264,0.05704249,0.00083241187,0.013441194],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985122,0.0004123612,0.00017072764,0.00025920733,0.0005031775,0.00014239262],"domain_scores_gemma":[0.9957826,0.001347476,0.0003669083,0.00023610404,0.0021447944,0.00012212897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015306291,0.0012417046,0.001927751,0.0039081615,0.0004904374,0.0016247596,0.0010219851,0.0006969635,0.0033139845],"category_scores_gemma":[0.0057727154,0.0003969349,0.00084398274,0.0033058801,0.00015890964,0.0013838144,0.000619165,0.0006007751,0.003060593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017671228,0.00038482854,0.004349058,0.0011928887,0.00039354106,0.00038781977,0.00044366525,0.0106506115,0.06461773,0.0010110224,0.071108535,0.84369314],"study_design_scores_gemma":[0.00029747057,0.0020201048,0.033662602,0.00016527546,0.0014925186,0.0012548182,0.001086501,0.81308824,0.086013526,0.00473105,0.05599735,0.0001905152],"about_ca_topic_score_codex":0.0027068818,"about_ca_topic_score_gemma":0.0058122175,"teacher_disagreement_score":0.0039081615,"about_ca_system_score_codex":0.00045461356,"about_ca_system_score_gemma":0.0009360648,"threshold_uncertainty_score":0.011086404},"labels":[],"label_agreement":null},{"id":"W1588719833","doi":"10.1007/978-3-540-71496-5_37","title":"Ad Hoc Retrieval of Documents with Topical Opinion","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Information retrieval; Computer science; Query expansion; Term (time); Document retrieval; Post hoc; Domain (mathematical analysis); Web query classification; Search engine; Web search query; World Wide Web; Mathematics","score_opus":0.025819386245428934,"score_gpt":0.28641662074595575,"score_spread":0.2605972345005268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1588719833","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23724905,0.012771611,0.6855689,0.0026426194,0.0035492976,0.002020263,0.009081325,0.009193385,0.037923526],"genre_scores_gemma":[0.44993994,0.005964368,0.46291482,0.0009496098,0.0039131287,0.0009007657,0.022351624,0.0006901308,0.052375574],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877304,0.00019766766,0.00015334765,0.00020869683,0.00051375246,0.0001535266],"domain_scores_gemma":[0.9982761,0.0004893787,0.00015576463,0.0002345679,0.0007526595,0.00009150854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001108512,0.0013759119,0.002022569,0.0055691903,0.0009487166,0.0026429384,0.001227874,0.0013008497,0.008253101],"category_scores_gemma":[0.003683801,0.00042254172,0.0011252398,0.005811735,0.00056163955,0.002982629,0.0013958185,0.00073552306,0.007865703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011356593,0.00055886875,0.0016796974,0.0011958744,0.0002412391,0.00040358782,0.0003074476,0.0030594233,0.11431606,0.0039055813,0.06082575,0.8123708],"study_design_scores_gemma":[0.0006505676,0.0035446994,0.011190342,0.00043613897,0.0020091669,0.004002826,0.0024530373,0.51380354,0.29009682,0.03381645,0.13773836,0.0002581801],"about_ca_topic_score_codex":0.00090770144,"about_ca_topic_score_gemma":0.0021519216,"teacher_disagreement_score":0.008253101,"about_ca_system_score_codex":0.0006199421,"about_ca_system_score_gemma":0.0011311738,"threshold_uncertainty_score":0.027609348},"labels":[],"label_agreement":null},{"id":"W1589554437","doi":"10.48550/arxiv.1308.6242","title":"NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets","year":2013,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":460,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Lexicon; Sentiment analysis; Task (project management); Word (group theory); Variety (cybernetics); Natural language processing; Term (time); Artificial intelligence; State (computer science); Information retrieval; Linguistics","score_opus":0.029291413637012355,"score_gpt":0.17014099458580287,"score_spread":0.14084958094879052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1589554437","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.078338295,0.010281574,0.59042233,0.0076687695,0.0026141126,0.0034765203,0.060578678,0.16248628,0.08413352],"genre_scores_gemma":[0.15013695,0.0040054224,0.70760554,0.0014499092,0.00039035798,0.0013452838,0.08338673,0.009496625,0.042183228],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99390334,0.001130539,0.00027967125,0.0011760824,0.0029384317,0.0005718694],"domain_scores_gemma":[0.98607606,0.0015270043,0.00028337043,0.0013204286,0.009995261,0.0007979293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0075713666,0.0022862582,0.0014883463,0.0068248184,0.003224473,0.003920916,0.0032865673,0.001434833,0.01315561],"category_scores_gemma":[0.015677234,0.0011615361,0.001787228,0.0042533004,0.0011273597,0.0046693026,0.0035510713,0.002756419,0.0146453185],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073496025,0.00032797703,0.013833509,0.0009111307,0.00030716884,0.00023117103,0.0010635144,0.005959657,0.026658481,0.006746707,0.25670227,0.68652344],"study_design_scores_gemma":[0.0003673373,0.00042401667,0.025438989,0.00062780606,0.00042467087,0.00029843076,0.0022707714,0.44384044,0.046990354,0.011835434,0.4670581,0.0004236014],"about_ca_topic_score_codex":0.7050651,"about_ca_topic_score_gemma":0.7127748,"teacher_disagreement_score":0.7050651,"about_ca_system_score_codex":0.0082888855,"about_ca_system_score_gemma":0.022500964,"threshold_uncertainty_score":0.59334373},"labels":[],"label_agreement":null},{"id":"W1629406447","doi":"10.1007/978-3-319-02750-0_25","title":"Lexical-Syntactical Patterns for Subjectivity Analysis of Social Issues","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Subjectivity; Automatic summarization; Computer science; Natural language processing; Sentiment analysis; Artificial intelligence; Feeling; Object (grammar); Sentence; Task (project management); Noun; Information retrieval; Linguistics; Psychology; Social psychology; Epistemology","score_opus":0.0298964560723557,"score_gpt":0.3045912512701854,"score_spread":0.2746947951978297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1629406447","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.159073,0.0027067475,0.77422714,0.0016042715,0.00045915614,0.0010350125,0.028137561,0.009054731,0.023702381],"genre_scores_gemma":[0.43725464,0.001268535,0.5212939,0.00022042423,0.0003060826,0.0009357542,0.030243084,0.0011325057,0.007345159],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985361,0.00035837365,0.00026944533,0.0003173174,0.0003973671,0.000121357065],"domain_scores_gemma":[0.99769896,0.0012014477,0.00024343065,0.00025530564,0.00048334626,0.00011744446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012025898,0.00082176673,0.0005759468,0.0054715835,0.00095305126,0.0024008467,0.0008183039,0.0007402047,0.008782512],"category_scores_gemma":[0.0045885355,0.0005207009,0.0013274205,0.0057740672,0.0005993279,0.0033064475,0.0014939578,0.0014870273,0.004909343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004956937,0.00041979086,0.017726937,0.0012154711,0.00024065215,0.0006892535,0.0020842873,0.0022508788,0.06703259,0.035106968,0.029412035,0.84332544],"study_design_scores_gemma":[0.00025168844,0.0005617142,0.08168079,0.001116002,0.00089242787,0.0034272792,0.0057338513,0.39835095,0.06600097,0.26602736,0.17565522,0.00030177276],"about_ca_topic_score_codex":0.001747097,"about_ca_topic_score_gemma":0.0030789785,"teacher_disagreement_score":0.008782512,"about_ca_system_score_codex":0.0006015913,"about_ca_system_score_gemma":0.0012303073,"threshold_uncertainty_score":0.029380381},"labels":[],"label_agreement":null},{"id":"W1667895860","doi":"10.5220/0004745301780186","title":"Context-Specific Sentiment Lexicon Expansion via Minimal User Interaction","year":2014,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Lexicon; Computer science; Sentiment analysis; Context (archaeology); Natural language processing; Polarity (international relations); Process (computing); Domain (mathematical analysis); Task (project management); Artificial intelligence; Visualization; Quality (philosophy); User interface; Human–computer interaction; Information retrieval","score_opus":0.02349723462266059,"score_gpt":0.26103674406862604,"score_spread":0.23753950944596544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1667895860","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.076430745,0.00028251586,0.83537513,0.0003869264,0.00012441046,0.001371598,0.0013346123,0.07138505,0.0133089535],"genre_scores_gemma":[0.28386715,0.00020102551,0.6999642,0.00035178522,0.000058872996,0.0024787392,0.0026834349,0.0036085586,0.0067861937],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985904,0.00071597775,0.00010573309,0.00027186333,0.00025449772,0.000061500825],"domain_scores_gemma":[0.9945268,0.0038789597,0.00016655488,0.0006561879,0.0006466554,0.00012479917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019098788,0.001777382,0.00075374346,0.0011129158,0.00040686852,0.0013342795,0.0011084799,0.0006735271,0.021062486],"category_scores_gemma":[0.011507532,0.00047706606,0.0006862201,0.0006031794,0.00031662555,0.002153531,0.0023771294,0.00066966924,0.0075227506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017096126,0.0007372566,0.0025951336,0.0015191034,0.00009339259,0.0009110539,0.005015825,0.0027124977,0.2209696,0.0045106984,0.03843022,0.72079563],"study_design_scores_gemma":[0.0010102364,0.0014703025,0.01334071,0.0006266118,0.00029813076,0.0030550184,0.0036292272,0.47275856,0.20935267,0.032853678,0.26119244,0.00041243125],"about_ca_topic_score_codex":0.00030756975,"about_ca_topic_score_gemma":0.00087172614,"teacher_disagreement_score":0.021062486,"about_ca_system_score_codex":0.00018577123,"about_ca_system_score_gemma":0.00035007563,"threshold_uncertainty_score":0.070461035},"labels":[],"label_agreement":null},{"id":"W1692844682","doi":"10.1016/j.procs.2015.07.295","title":"Ontology-based Sentiment Analysis Process for Social Media Content","year":2015,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Sentiment analysis; Ontology; Social media; Process (computing); World Wide Web; Service (business); Content analysis; Customer service; Classifier (UML); Identification (biology); Data science; Artificial intelligence","score_opus":0.1419450223484216,"score_gpt":0.33566194448213704,"score_spread":0.19371692213371544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1692844682","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025505524,0.000109318236,0.9645131,0.00072139956,0.0001336825,0.0008571596,0.0013651312,0.0022057854,0.004588857],"genre_scores_gemma":[0.21247858,0.00025988425,0.77762866,0.00018760026,0.0000878001,0.00091869885,0.003436386,0.00033088538,0.004671488],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99629694,0.00074868416,0.0004517109,0.00057904853,0.001695947,0.0002276044],"domain_scores_gemma":[0.9953004,0.0016888997,0.00044110513,0.00034385113,0.0020923838,0.00013331525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003713879,0.0008366685,0.0006445733,0.0041546356,0.0016705317,0.0026508071,0.00081824855,0.00065034255,0.0032213265],"category_scores_gemma":[0.00929008,0.00036358438,0.002529269,0.0025176695,0.0007631959,0.0027259989,0.0015257748,0.0014306178,0.0019357639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040872625,0.00059390254,0.010806387,0.0009843142,0.00033897848,0.0011316041,0.007888303,0.016462322,0.08877134,0.045175113,0.021378618,0.80606043],"study_design_scores_gemma":[0.000061684594,0.00021414932,0.015285422,0.00024666166,0.000355491,0.0005320925,0.0053850873,0.70796514,0.09067725,0.0809777,0.09811577,0.00018353936],"about_ca_topic_score_codex":0.004880431,"about_ca_topic_score_gemma":0.005019719,"teacher_disagreement_score":0.004880431,"about_ca_system_score_codex":0.0017266367,"about_ca_system_score_gemma":0.0026015094,"threshold_uncertainty_score":0.019641101},"labels":[],"label_agreement":null},{"id":"W169877145","doi":"10.13140/rg.2.1.5178.3445","title":"PARAMETRICAL WORDS IN THE SENTIMENT LEXICON","year":2016,"lang":"en","type":"article","venue":"International Journal of Cognitive Research in Science Engineering and Education","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"College of Veterinary Medicine, Cornell University; Atomic Energy of Canada Limited; University of Cambridge","keywords":"Lexicon; Classifier (UML); Computer science; Artificial intelligence; Natural language processing; Class (philosophy); Domain (mathematical analysis); Naive Bayes classifier; Polarity (international relations); Mathematics; Support vector machine","score_opus":0.06753411484959285,"score_gpt":0.4257374507948544,"score_spread":0.35820333594526155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W169877145","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23828064,0.0021516436,0.65984756,0.001577183,0.00097202923,0.0011226936,0.012632906,0.0038495576,0.07956585],"genre_scores_gemma":[0.7267609,0.00091959443,0.24857314,0.00042756813,0.00025372734,0.0008563473,0.013110439,0.00060077105,0.008497485],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985625,0.00032149593,0.0003381511,0.00030643318,0.00038516775,0.00008621282],"domain_scores_gemma":[0.99829537,0.0006572065,0.00020367454,0.00020144011,0.000591329,0.00005099381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059737207,0.0006366786,0.0005233281,0.0025579566,0.0009276925,0.0028143132,0.00054234185,0.000568253,0.0048099468],"category_scores_gemma":[0.00471213,0.00047243925,0.0007185723,0.002622462,0.0010586089,0.003669079,0.0009818429,0.00097516645,0.002760224],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008727395,0.00016830789,0.020713637,0.0014995411,0.00016043226,0.0020834142,0.008114758,0.0059955907,0.07903252,0.26066476,0.029850448,0.590844],"study_design_scores_gemma":[0.00014207051,0.00052267336,0.054963116,0.00085046457,0.00042566538,0.0074279075,0.008074821,0.10875173,0.031736124,0.27282012,0.51380986,0.00047543232],"about_ca_topic_score_codex":0.0020877537,"about_ca_topic_score_gemma":0.0017975081,"teacher_disagreement_score":0.0048099468,"about_ca_system_score_codex":0.001047666,"about_ca_system_score_gemma":0.000979823,"threshold_uncertainty_score":0.01609087},"labels":[],"label_agreement":null},{"id":"W1808640434","doi":"10.1007/978-3-540-68825-9_3","title":"A Comparison of Sentiment Analysis Techniques: Polarizing Movie Blogs","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":172,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Popularity; Sentiment analysis; Polarity (international relations); Social media; Set (abstract data type); Support vector machine; Visualization; Task (project management); The Internet; Information retrieval; World Wide Web; Data science; Service (business); Artificial intelligence; Machine learning","score_opus":0.03004770387279265,"score_gpt":0.30358412287881853,"score_spread":0.2735364190060259,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1808640434","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64639395,0.023417154,0.25880802,0.0020179919,0.0018198015,0.0011123349,0.00777291,0.0040114997,0.054646306],"genre_scores_gemma":[0.6676213,0.0123876445,0.29986092,0.0003245595,0.0010912595,0.00046579633,0.00924283,0.0006547198,0.00835093],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99759173,0.0009884769,0.0001637101,0.00023521707,0.0008848355,0.00013612007],"domain_scores_gemma":[0.988485,0.0076715113,0.0004096671,0.0003988739,0.0027406297,0.0002942297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035486945,0.000999455,0.0010564895,0.0069502727,0.00067975867,0.0026011865,0.000597952,0.00076168025,0.0025088668],"category_scores_gemma":[0.01056956,0.0003143139,0.0010081284,0.0058809128,0.00030689544,0.0029889073,0.0010497521,0.0007176234,0.0019187871],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026503513,0.00039084,0.019397015,0.0016954379,0.00070684525,0.00009598213,0.0012615982,0.0016078559,0.025412878,0.0015958023,0.01213833,0.93304706],"study_design_scores_gemma":[0.0011780167,0.0058664824,0.37441677,0.0013547405,0.00391878,0.0021588663,0.0177266,0.41099107,0.07717949,0.019866847,0.08463454,0.00070783065],"about_ca_topic_score_codex":0.0012795499,"about_ca_topic_score_gemma":0.003607293,"teacher_disagreement_score":0.0069502727,"about_ca_system_score_codex":0.00031210034,"about_ca_system_score_gemma":0.0004872202,"threshold_uncertainty_score":0.018767536},"labels":[],"label_agreement":null},{"id":"W1822239915","doi":"10.1146/annurev-linguistics-011415-040518","title":"Sentiment Analysis: An Overview from Linguistics","year":2015,"lang":"en","type":"article","venue":"Annual Review of Linguistics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":402,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Sentiment analysis; Computer science; Natural language processing; Context (archaeology); Field (mathematics); Intersection (aeronautics); Computational linguistics; Linguistics; Social media; Repertoire; Corpus linguistics; Artificial intelligence; Linguistic analysis; World Wide Web; History","score_opus":0.08045031633417499,"score_gpt":0.3777416211088148,"score_spread":0.29729130477463983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1822239915","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001716939,0.93028927,0.035698727,0.007920843,0.0024154775,0.00017573392,0.0002639352,0.00033203507,0.021187028],"genre_scores_gemma":[0.011672079,0.94648504,0.029423803,0.0025844667,0.0046668574,0.00028397972,0.00045606648,0.0001541061,0.0042735096],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99791676,0.00061027484,0.00028506457,0.0003165042,0.00077570137,0.00009569854],"domain_scores_gemma":[0.99659806,0.0022148245,0.00019069761,0.00010239022,0.00078493526,0.00010915902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032301976,0.0017099971,0.0018543926,0.009020203,0.0012600158,0.004892177,0.0014236864,0.0024133734,0.0035858455],"category_scores_gemma":[0.004866475,0.00086303736,0.001334734,0.00887838,0.0022299434,0.008028846,0.0020953184,0.00310371,0.004297051],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000063134474,0.00008031613,0.001464179,0.009120155,0.00012553562,0.0003586416,0.0012609188,0.0007284555,0.0022034403,0.041391652,0.073952,0.86925155],"study_design_scores_gemma":[0.000018259303,0.000079895864,0.0026887513,0.0050120708,0.000086067164,0.0009284847,0.001022907,0.0019863015,0.0009050295,0.08253077,0.9046589,0.00008263014],"about_ca_topic_score_codex":0.0017024105,"about_ca_topic_score_gemma":0.0015918055,"teacher_disagreement_score":0.009020203,"about_ca_system_score_codex":0.0019527875,"about_ca_system_score_gemma":0.0021433034,"threshold_uncertainty_score":0.017083108},"labels":[],"label_agreement":null},{"id":"W187058510","doi":"","title":"Analyzing Appraisal Automatically","year":2004,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":180,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Natural language processing; Orientation (vector space); Product (mathematics); Information retrieval; Artificial intelligence; Content (measure theory); Mathematics","score_opus":0.01577781097010012,"score_gpt":0.28993734860875997,"score_spread":0.27415953763865986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W187058510","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.110095896,0.0011674644,0.7860301,0.0014509709,0.0009792919,0.0013291609,0.011011205,0.008490715,0.0794452],"genre_scores_gemma":[0.35959637,0.00079924223,0.5938525,0.00031295986,0.00089575147,0.0016665664,0.01673181,0.0014374909,0.02470729],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958099,0.0008985005,0.00032212213,0.00084788847,0.0019420561,0.00017939505],"domain_scores_gemma":[0.98196363,0.006520789,0.0016010648,0.0011512424,0.008528258,0.00023511774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023603376,0.0009984395,0.0005950057,0.005615801,0.000777479,0.0021280765,0.00071137573,0.0005762022,0.009327335],"category_scores_gemma":[0.026764527,0.00036626696,0.0005111753,0.0035386933,0.0004160358,0.0031570797,0.0009893841,0.0011283396,0.0066712312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033327233,0.0001429658,0.016018424,0.001134373,0.00006428034,0.0005130822,0.0033354259,0.0025533815,0.031733204,0.02241462,0.05612251,0.86563444],"study_design_scores_gemma":[0.00013525959,0.00045414193,0.08241735,0.00081815437,0.00024206746,0.001378767,0.004024413,0.2932485,0.062321298,0.10545801,0.44917977,0.0003223664],"about_ca_topic_score_codex":0.0015946863,"about_ca_topic_score_gemma":0.0020961412,"teacher_disagreement_score":0.009327335,"about_ca_system_score_codex":0.0009018277,"about_ca_system_score_gemma":0.0009610888,"threshold_uncertainty_score":0.031202972},"labels":[],"label_agreement":null},{"id":"W1879819860","doi":"10.3917/riges.402.0095","title":"Gérer sa réputation à l’heure des réseaux sociaux : un nouveau défi pour les entreprises","year":2015,"lang":"fr","type":"article","venue":"Gestion","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"World Federation of Science Journalists; HEC Montréal; Polytechnique Montréal","funders":"","keywords":"Humanities; Political science; Art","score_opus":0.09228035237773276,"score_gpt":0.3090689794685417,"score_spread":0.21678862709080898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1879819860","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20503542,0.08222088,0.074709795,0.47208703,0.007918109,0.000095699266,0.0029046834,0.0013214531,0.15370694],"genre_scores_gemma":[0.9205547,0.017776305,0.017804895,0.008284588,0.0043926393,0.000060521783,0.0008539723,0.00043632986,0.029836066],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9926232,0.0032137916,0.00035105294,0.0011068658,0.0022877844,0.00041729538],"domain_scores_gemma":[0.9735728,0.010220055,0.0041465275,0.0029907934,0.0074474504,0.0016222574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009187823,0.0008235376,0.0010878273,0.003849336,0.0037296538,0.013177338,0.0013065876,0.0035825146,0.007869574],"category_scores_gemma":[0.043825082,0.0005587954,0.00061959616,0.004400048,0.004965611,0.022571063,0.0031549237,0.0039570048,0.005299006],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062440126,0.00016976573,0.099160105,0.001427187,0.00048425718,0.0006287449,0.016660685,0.0017103694,0.004482495,0.15846658,0.17910251,0.5370829],"study_design_scores_gemma":[0.00005459842,0.00039155548,0.12345205,0.0019568293,0.0003892423,0.0015251288,0.031486247,0.017496796,0.0049146996,0.21105042,0.6068011,0.00048136798],"about_ca_topic_score_codex":0.01744734,"about_ca_topic_score_gemma":0.018189978,"teacher_disagreement_score":0.01744734,"about_ca_system_score_codex":0.0030139864,"about_ca_system_score_gemma":0.0022169666,"threshold_uncertainty_score":0.04859042},"labels":[],"label_agreement":null},{"id":"W1885684381","doi":"10.1177/0165551515595742","title":"Discovering aspects of online consumer reviews","year":2015,"lang":"en","type":"article","venue":"Journal of Information Science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Cluster analysis; Process (computing); Similarity (geometry); Class (philosophy); Hierarchical clustering; Information retrieval; Product (mathematics); Data mining; Cluster (spacecraft); Artificial intelligence; Data science; Image (mathematics); Mathematics","score_opus":0.059063299643634215,"score_gpt":0.3312492729396795,"score_spread":0.27218597329604527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1885684381","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67681813,0.01055643,0.2774412,0.0011995822,0.00047536517,0.0009674157,0.008241232,0.001536078,0.022764448],"genre_scores_gemma":[0.9062362,0.0022948934,0.08088603,0.0001843619,0.0007170356,0.00028907022,0.0064068297,0.000140578,0.0028450761],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9973907,0.000647529,0.00026172947,0.0005189261,0.0010499695,0.00013115403],"domain_scores_gemma":[0.99313086,0.0024980842,0.0014978418,0.00040572064,0.0022758301,0.00019164136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010873247,0.0010117727,0.0008856306,0.0080970265,0.0006033378,0.0021565608,0.0006224547,0.0007431156,0.00066752627],"category_scores_gemma":[0.009028782,0.00047756106,0.001223112,0.005454324,0.00035251153,0.002500607,0.00080646353,0.00074214145,0.00054021337],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010513692,0.00054504175,0.2549917,0.001822642,0.0010317864,0.0025356093,0.0043745474,0.008127502,0.03652422,0.008239297,0.019958343,0.6607979],"study_design_scores_gemma":[0.00009609321,0.0005618611,0.5429879,0.00044035207,0.0012615204,0.004503219,0.003908638,0.3012745,0.019472077,0.035318993,0.08991214,0.0002627502],"about_ca_topic_score_codex":0.0033614298,"about_ca_topic_score_gemma":0.007302256,"teacher_disagreement_score":0.0080970265,"about_ca_system_score_codex":0.0007132911,"about_ca_system_score_gemma":0.00076102116,"threshold_uncertainty_score":0.0066837072},"labels":[],"label_agreement":null},{"id":"W1930223417","doi":"10.1109/tmm.2015.2482228","title":"Deep Multimodal Learning for Affective Analysis and Retrieval","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":155,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Modalities; Social media; Upload; Representation (politics); Multimodal learning; Artificial intelligence; Emotion classification; Feature learning; Multimodality; Information retrieval; Automatic summarization; Natural language processing; Machine learning; World Wide Web","score_opus":0.025153127007339293,"score_gpt":0.28263479325970464,"score_spread":0.25748166625236535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1930223417","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030245595,0.0051261387,0.9527043,0.0010852906,0.00019086427,0.00008879975,0.0006512976,0.0040390883,0.005868631],"genre_scores_gemma":[0.7954228,0.0029342754,0.17640144,0.0012045015,0.00035924467,0.00031983526,0.0025803186,0.00040725266,0.02037026],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996704,0.00009546856,0.000015413767,0.00009182242,0.00005746215,0.00006952172],"domain_scores_gemma":[0.9997131,0.00012317617,0.000028566852,0.000043091983,0.00007104952,0.000020931593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067308825,0.0009776065,0.00079680886,0.0007319768,0.0003032569,0.00086821924,0.0011150207,0.0011096626,0.0065242113],"category_scores_gemma":[0.002177099,0.0003747465,0.00096217677,0.0006816185,0.00041703918,0.0013383293,0.0011384316,0.001645646,0.0023333272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004714043,0.00029455504,0.0011999515,0.000344168,0.00024107234,0.00018642932,0.00018848194,0.18333937,0.024873268,0.0146324355,0.023541277,0.7506876],"study_design_scores_gemma":[0.000009355235,0.000034390116,0.00037065672,0.000018623568,0.000025277232,0.000029551402,0.000028999428,0.9843126,0.002672591,0.010644935,0.0018426215,0.000010491103],"about_ca_topic_score_codex":0.004763507,"about_ca_topic_score_gemma":0.0058696023,"teacher_disagreement_score":0.0065242113,"about_ca_system_score_codex":0.0010676861,"about_ca_system_score_gemma":0.0005326145,"threshold_uncertainty_score":0.021825612},"labels":[],"label_agreement":null},{"id":"W1943450596","doi":"10.1002/cpe.1785","title":"Real‐time helpfulness prediction based on voter opinions","year":2011,"lang":"en","type":"article","venue":"Concurrency and Computation Practice and Experience","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Helpfulness; Computer science; Machine learning; Data mining; Convergence (economics); Artificial intelligence; Algorithm","score_opus":0.040745024782646744,"score_gpt":0.31074389322423623,"score_spread":0.2699988684415895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1943450596","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5256775,0.00027332033,0.46883592,0.0005690602,0.00007106956,0.00009264809,0.00035705208,0.0013835029,0.002739968],"genre_scores_gemma":[0.96137244,0.000039353174,0.03779487,0.000026484346,0.000036672624,0.000024311958,0.0002003366,0.000021430462,0.00048414647],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911696,0.0003611399,0.000050973224,0.00018507667,0.00021524151,0.000070651644],"domain_scores_gemma":[0.99236065,0.004326509,0.00094742776,0.0005747777,0.0015692725,0.00022137456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018711151,0.00043166077,0.0006633556,0.00093388354,0.0002205647,0.0007812894,0.0006572257,0.0004905524,0.000987392],"category_scores_gemma":[0.010077682,0.00020750644,0.0002747251,0.0005513432,0.00023766112,0.0012147231,0.00036315844,0.0007799006,0.0007625482],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012306615,0.00033104618,0.08670157,0.00021423251,0.00017391509,0.00018340921,0.00034403958,0.48884326,0.022375666,0.004851574,0.0068754824,0.38787517],"study_design_scores_gemma":[0.000005695407,0.000026904623,0.0029895126,0.0000033899641,0.0000052868513,0.000016017264,0.000017913268,0.99324715,0.0023962343,0.0010951138,0.00018997306,0.0000067454653],"about_ca_topic_score_codex":0.0016040739,"about_ca_topic_score_gemma":0.0018709537,"teacher_disagreement_score":0.0018711151,"about_ca_system_score_codex":0.00039878386,"about_ca_system_score_gemma":0.00025947668,"threshold_uncertainty_score":0.0098955035},"labels":[],"label_agreement":null},{"id":"W1961993270","doi":"10.1002/asi.23533","title":"A machine‐learning approach to negation and speculation detection for sentiment analysis","year":2015,"lang":"en","type":"article","venue":"Journal of the Association for Information Science and Technology","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":96,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Negation; Speculation; Scope (computer science); Computer science; Artificial intelligence; Baseline (sea); Task (project management); Sentiment analysis; Natural language processing; Machine learning; Identification (biology); Programming language","score_opus":0.015812222297129364,"score_gpt":0.2586530080846184,"score_spread":0.24284078578748902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1961993270","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052446254,0.0013717435,0.92785275,0.00093795394,0.0004080212,0.0006116225,0.0013202627,0.00822704,0.006824408],"genre_scores_gemma":[0.36420923,0.00056496036,0.6266094,0.00031957147,0.00037869572,0.0004273911,0.0022794646,0.00013707696,0.005074163],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989366,0.0002848578,0.00012102334,0.00028263748,0.00030914912,0.00006580188],"domain_scores_gemma":[0.99780494,0.00089751056,0.00025538148,0.00017810163,0.00079520565,0.00006891425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001732745,0.00083444244,0.0007323397,0.0022268454,0.0005612858,0.0011338628,0.0010073334,0.0009210619,0.0032966232],"category_scores_gemma":[0.004755773,0.0002847668,0.0008227614,0.0009985527,0.0003068735,0.0014302704,0.0006195407,0.001106992,0.0023320287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029024165,0.0003787097,0.005281884,0.00039890083,0.00015039154,0.00024998214,0.0003508078,0.0059412583,0.07930043,0.003036698,0.013451847,0.89116895],"study_design_scores_gemma":[0.000057506943,0.00041496,0.01005672,0.00013151487,0.0001376622,0.0005259072,0.00023482252,0.89649457,0.058739096,0.011020678,0.022101603,0.000084882515],"about_ca_topic_score_codex":0.0012374559,"about_ca_topic_score_gemma":0.0020301251,"teacher_disagreement_score":0.0032966232,"about_ca_system_score_codex":0.0005266091,"about_ca_system_score_gemma":0.00068072625,"threshold_uncertainty_score":0.01102829},"labels":[],"label_agreement":null},{"id":"W1963657722","doi":"10.1007/s10726-006-9022-1","title":"Word from the Guest Editors","year":2006,"lang":"en","type":"article","venue":"Group Decision and Negotiation","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Word (group theory); Computer science; Linguistics; Natural language processing; World Wide Web; Philosophy","score_opus":0.009375185397906924,"score_gpt":0.22795892132791193,"score_spread":0.21858373593000502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1963657722","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011049155,0.007987621,0.0011542904,0.057187323,0.7606144,0.00009459148,0.00062679447,0.00045915812,0.17077087],"genre_scores_gemma":[0.008409315,0.0036992182,0.00038642727,0.010991284,0.15834448,0.000053283624,0.00036932976,0.00040084173,0.81734574],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991916,0.00011059302,0.00006722189,0.00015122473,0.00039194385,0.00008747017],"domain_scores_gemma":[0.9959418,0.00060362084,0.00026475973,0.00023942992,0.0018943202,0.0010561051],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095181173,0.0008662422,0.0008729205,0.0016056387,0.0012367371,0.0050553,0.00073864846,0.0017573902,0.21043223],"category_scores_gemma":[0.008466024,0.00023151684,0.00041274482,0.0011027666,0.0004129026,0.0021793314,0.001599053,0.0025330526,0.13930917],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026603322,0.0000067931273,0.00004716255,0.000064928776,0.0000020387838,0.00008064873,0.000028075618,0.000014120094,0.00020635642,0.0008557348,0.97781926,0.020848246],"study_design_scores_gemma":[0.0000056293334,0.00001451202,0.00016506169,0.000033612218,0.0000028186948,0.00009181769,0.000044186392,0.00004796273,0.00014724751,0.00041392344,0.9990289,0.0000043041405],"about_ca_topic_score_codex":0.000511352,"about_ca_topic_score_gemma":0.0009971154,"teacher_disagreement_score":0.21043223,"about_ca_system_score_codex":0.0006396037,"about_ca_system_score_gemma":0.000908301,"threshold_uncertainty_score":0.7039659},"labels":[],"label_agreement":null},{"id":"W1967097654","doi":"10.1007/s13278-014-0193-5","title":"Predicting political preference of Twitter users","year":2014,"lang":"en","type":"article","venue":"Social Network Analysis and Mining","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":99,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Preference; Computer science; Variety (cybernetics); Context (archaeology); Politics; Sentiment analysis; Artificial intelligence; Work (physics); Natural language processing; Information retrieval; Political science; Law; Mathematics; Statistics","score_opus":0.02924801817375477,"score_gpt":0.2670297265927906,"score_spread":0.23778170841903584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967097654","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99395186,0.00010001971,0.0015640112,0.00014083507,0.000025645768,0.00001846623,0.0014778606,0.000037415753,0.002683947],"genre_scores_gemma":[0.9969645,0.000066106935,0.0008555411,0.000018106823,0.00004196342,0.0000110948995,0.0011475947,0.000003676674,0.0008913158],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998233,0.000059011734,0.000013135252,0.000021253607,0.000045709407,0.000037638278],"domain_scores_gemma":[0.99901736,0.00046982322,0.00015161479,0.00003480426,0.00021008817,0.00011632672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002946061,0.00025095505,0.0001914001,0.0016507241,0.00026253553,0.00050996884,0.000118089214,0.00027305153,0.0025796965],"category_scores_gemma":[0.0019201337,0.00007017185,0.00024166997,0.0011768877,0.000065220316,0.0005196802,0.00017351205,0.00025743537,0.0011631531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010584284,0.00039163482,0.89074755,0.00011942023,0.00014680564,0.00017775566,0.00023769285,0.00278938,0.0104899155,0.0007595254,0.0064599104,0.08662203],"study_design_scores_gemma":[0.000047317313,0.0004964303,0.79906225,0.0000258895,0.0001705212,0.0003146554,0.0014892264,0.18554932,0.006304026,0.0015669467,0.0049391687,0.00003427362],"about_ca_topic_score_codex":0.002770951,"about_ca_topic_score_gemma":0.006687879,"teacher_disagreement_score":0.002770951,"about_ca_system_score_codex":0.00019735676,"about_ca_system_score_gemma":0.00017066419,"threshold_uncertainty_score":0.008629918},"labels":[],"label_agreement":null},{"id":"W1967153118","doi":"10.1115/detc2014-35288","title":"Towards Extracting Affordances From Online Consumer Product Reviews","year":2014,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Affordance; Computer science; Product (mathematics); Sentiment analysis; Cluster analysis; World Wide Web; Data science; Natural language processing; Human–computer interaction; Artificial intelligence","score_opus":0.05118526424080541,"score_gpt":0.3095638549778207,"score_spread":0.2583785907370153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967153118","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8105733,0.0066810483,0.15595448,0.0008379057,0.00021994465,0.0010138578,0.013398305,0.0026268833,0.008694419],"genre_scores_gemma":[0.7931497,0.0014257542,0.19257762,0.00008947691,0.00018856794,0.00046910057,0.010157239,0.00019732032,0.0017453086],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977285,0.0005137612,0.00042956413,0.00051975925,0.00067539077,0.00013305144],"domain_scores_gemma":[0.9900129,0.0045676115,0.0021046167,0.0005861541,0.002515418,0.0002132316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015518396,0.0013338812,0.0007700103,0.013837779,0.00065595604,0.0019468535,0.0005345721,0.0007111341,0.0009664057],"category_scores_gemma":[0.016665641,0.00045497876,0.0010219499,0.0064992243,0.000546151,0.0030493175,0.0012408663,0.00057379843,0.0007767558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00091071124,0.00037540626,0.17842789,0.005972428,0.00048173516,0.004036713,0.013084715,0.0050315643,0.09093324,0.0055695884,0.014029569,0.68114644],"study_design_scores_gemma":[0.00012694734,0.0010539452,0.5130713,0.0013047871,0.0009959571,0.008063134,0.017974816,0.23616646,0.046571996,0.023149284,0.15103424,0.0004871195],"about_ca_topic_score_codex":0.005557375,"about_ca_topic_score_gemma":0.011901271,"teacher_disagreement_score":0.013837779,"about_ca_system_score_codex":0.0007773923,"about_ca_system_score_gemma":0.0009921839,"threshold_uncertainty_score":0.011050105},"labels":[],"label_agreement":null},{"id":"W1967274749","doi":"10.1145/2396761.2396863","title":"On the design of LDA models for aspect-based opinion mining","year":2012,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":133,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Latent Dirichlet allocation; Computer science; Probabilistic logic; Topic model; Set (abstract data type); Identification (biology); Data mining; Machine learning; Artificial intelligence; Data science","score_opus":0.11607900377579357,"score_gpt":0.2963864049507564,"score_spread":0.18030740117496286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967274749","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002714242,0.00022997684,0.99574727,0.00032691943,0.000030629355,0.00007953307,0.00007333753,0.00023848673,0.0005595624],"genre_scores_gemma":[0.18170261,0.0009092001,0.8124949,0.00052416365,0.00024769353,0.0011628752,0.0008866623,0.00017258368,0.001899374],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.993396,0.004463565,0.00040367353,0.0006729747,0.00085229304,0.00021151575],"domain_scores_gemma":[0.9876887,0.00821473,0.00055920676,0.0009708493,0.0022904135,0.00027611948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009180655,0.001141471,0.0014239997,0.0019576345,0.0011185213,0.0027525325,0.0019916734,0.0014045695,0.0017242366],"category_scores_gemma":[0.027658856,0.0010723431,0.0021144424,0.0021307361,0.001102378,0.0034593148,0.00239977,0.0026049737,0.0020089603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005959031,0.00038489825,0.009692388,0.0004713534,0.00044378944,0.0002542082,0.0015736208,0.2331672,0.009201062,0.20182976,0.013670449,0.5287154],"study_design_scores_gemma":[0.000024876672,0.00004202062,0.00040342394,0.00002808169,0.000028958484,0.00006369752,0.000052228825,0.94660103,0.00094090763,0.048646294,0.0031398889,0.000028620563],"about_ca_topic_score_codex":0.0039970707,"about_ca_topic_score_gemma":0.0070501626,"teacher_disagreement_score":0.009180655,"about_ca_system_score_codex":0.0014902991,"about_ca_system_score_gemma":0.0013003381,"threshold_uncertainty_score":0.048552513},"labels":[],"label_agreement":null},{"id":"W1977273430","doi":"10.1558/lhs.v6i1-3.275","title":"Contrastive analyses of evaluation in text","year":2012,"lang":"en","type":"article","venue":"Linguistics and the Human Sciences","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Annotation; Corpus linguistics; Linguistics; Computer science; Subcategory; Coding (social sciences); Natural language processing; Artificial intelligence; Scheme (mathematics); Selection (genetic algorithm); Text corpus; British National Corpus; Contrastive analysis; Psychology; Sociology; Mathematics; Philosophy","score_opus":0.12805098283164124,"score_gpt":0.41075937428764636,"score_spread":0.2827083914560051,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977273430","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3500021,0.0035966176,0.5364913,0.002437859,0.0007618197,0.0017468653,0.00633725,0.001416873,0.097209334],"genre_scores_gemma":[0.8994246,0.00034696757,0.09061885,0.00023248649,0.00031652028,0.0017415715,0.0027286976,0.0004810913,0.0041092252],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9737379,0.016618893,0.0014097708,0.0027825315,0.0049112868,0.0005396018],"domain_scores_gemma":[0.8744581,0.10040036,0.0059490334,0.004887525,0.01372366,0.00058128784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014353678,0.0008709291,0.00060730823,0.006904283,0.0012582815,0.0051454683,0.0009672596,0.00070738647,0.0076286243],"category_scores_gemma":[0.09046687,0.0003525586,0.0007234776,0.0046840943,0.0027179115,0.007252148,0.0028860494,0.0019328735,0.0010095821],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028453872,0.00038089015,0.02351705,0.003945226,0.00049135875,0.0010913295,0.08035794,0.0035094116,0.047719315,0.39755386,0.016798861,0.42178944],"study_design_scores_gemma":[0.00039509655,0.0011141559,0.13067693,0.0018789623,0.000570038,0.0017541351,0.049353797,0.10489742,0.06352369,0.41380408,0.23158072,0.00045088923],"about_ca_topic_score_codex":0.0011709593,"about_ca_topic_score_gemma":0.0011534551,"teacher_disagreement_score":0.014353678,"about_ca_system_score_codex":0.0034820605,"about_ca_system_score_gemma":0.00073161564,"threshold_uncertainty_score":0.07591039},"labels":[],"label_agreement":null},{"id":"W1977788175","doi":"10.1145/1277741.1277956","title":"Retrieval of discussions from enterprise mailing lists","year":2007,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Argumentative; Computer science; Task (project management); Context (archaeology); World Wide Web; Information retrieval; Subjectivity; Engineering; Linguistics","score_opus":0.014237112286898617,"score_gpt":0.2759822884872022,"score_spread":0.2617451762003036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977788175","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6821079,0.01686506,0.20824061,0.006438002,0.0014950719,0.0017146561,0.015897987,0.011757026,0.055483766],"genre_scores_gemma":[0.79369855,0.005897754,0.13709165,0.00063176977,0.0029481628,0.0007553453,0.030473474,0.00067828794,0.027824951],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977063,0.00059992494,0.00018778523,0.00020468359,0.0010239851,0.0002772844],"domain_scores_gemma":[0.99401444,0.002712651,0.0007279195,0.00046050307,0.0018413053,0.00024319987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001983874,0.0013457035,0.0014495648,0.01280564,0.0019521927,0.003354435,0.0009386967,0.0017257303,0.005418909],"category_scores_gemma":[0.013158192,0.00048837985,0.0009047303,0.007982065,0.00033995195,0.0038531993,0.0018312878,0.00088650873,0.005802573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015437366,0.00063370535,0.0102627035,0.0024374768,0.0003147314,0.0020803073,0.0063961614,0.0025536062,0.07817537,0.007348304,0.07213489,0.816119],"study_design_scores_gemma":[0.000753352,0.0028375217,0.082374044,0.0017594656,0.0017244442,0.0048314687,0.024983866,0.1964276,0.2338289,0.060836196,0.38896435,0.00067885336],"about_ca_topic_score_codex":0.0010102417,"about_ca_topic_score_gemma":0.0014542304,"teacher_disagreement_score":0.01280564,"about_ca_system_score_codex":0.0008883228,"about_ca_system_score_gemma":0.0010403488,"threshold_uncertainty_score":0.018128037},"labels":[],"label_agreement":null},{"id":"W1978299585","doi":"10.3166/ria.20.529-551","title":"De la construction du corpus émotionnel au système de détection. Le point de vue applicatif de la surveillance dans les lieux publics","year":2006,"lang":"fr","type":"article","venue":"Revue d intelligence artificielle","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Political science; Humanities; Philosophy","score_opus":0.020667148955521155,"score_gpt":0.2554933564650063,"score_spread":0.23482620750948516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978299585","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15106845,0.002711686,0.7844071,0.0023157655,0.0012454083,0.002914649,0.018390035,0.009276464,0.027670376],"genre_scores_gemma":[0.2598609,0.0011590059,0.665658,0.00053147273,0.00039175063,0.0072857966,0.036772326,0.0017895671,0.026551189],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9945503,0.002516069,0.00034759621,0.0013467285,0.0010625443,0.00017671939],"domain_scores_gemma":[0.9931798,0.0034774179,0.00026010475,0.0006222457,0.0022930184,0.00016743476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030141298,0.0017936493,0.0009901705,0.0021802657,0.0015235244,0.0026873515,0.0009837403,0.0017150776,0.0124123],"category_scores_gemma":[0.015947467,0.0005974196,0.0011241778,0.0014301853,0.0012047866,0.0023357626,0.0015403361,0.0024358763,0.0077028056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014115949,0.000617117,0.0042223623,0.0032767367,0.00024458923,0.0011261087,0.0069719097,0.011297623,0.26233876,0.016117558,0.05549058,0.63688505],"study_design_scores_gemma":[0.0005376114,0.000997997,0.03910766,0.0009202241,0.0003641031,0.002019335,0.0056283325,0.2855717,0.19850498,0.013029555,0.45299694,0.00032158534],"about_ca_topic_score_codex":0.009757694,"about_ca_topic_score_gemma":0.0054885563,"teacher_disagreement_score":0.0124123,"about_ca_system_score_codex":0.0013360432,"about_ca_system_score_gemma":0.0019169353,"threshold_uncertainty_score":0.041523337},"labels":[],"label_agreement":null},{"id":"W1981498954","doi":"10.1016/j.inffus.2014.04.002","title":"An approach to rank reviews by fusing and mining opinions based on review pertinence","year":2014,"lang":"en","type":"article","venue":"Information Fusion","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"Fundamental Research Funds for the Central Universities","keywords":"Automatic summarization; Relevance (law); Computer science; Information retrieval; Ranking (information retrieval); Similarity (geometry); Rank (graph theory); Sentiment analysis; Filter (signal processing); Metric (unit); Data science; Data mining; Artificial intelligence; Image (mathematics); Mathematics","score_opus":0.02001360541945994,"score_gpt":0.2723351873363451,"score_spread":0.25232158191688514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981498954","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06432805,0.004694998,0.915913,0.00197729,0.00058217713,0.0008307737,0.0018273083,0.0024515458,0.007394786],"genre_scores_gemma":[0.3771208,0.0012389515,0.61362535,0.0003124802,0.0007489343,0.00038278828,0.0015127909,0.000092680886,0.004965222],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950669,0.0009787241,0.00053892564,0.0007297765,0.002431913,0.00025371803],"domain_scores_gemma":[0.99192715,0.0022010126,0.0009139197,0.00030678467,0.0044337865,0.00021735874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005172467,0.0015226658,0.0018069922,0.011434138,0.0010526783,0.0031365498,0.0012489675,0.0012596239,0.0016934356],"category_scores_gemma":[0.0116688935,0.00046785912,0.001612087,0.00751035,0.00048106493,0.0028000227,0.0011653539,0.0011007425,0.0014016778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053314323,0.00044874087,0.010310316,0.00058613776,0.0008273893,0.00017758357,0.0005642065,0.0049011987,0.022665443,0.006240631,0.013350795,0.9393943],"study_design_scores_gemma":[0.00017609465,0.0016278973,0.03032526,0.00029927352,0.0019124092,0.00086648104,0.0011942183,0.85300285,0.036891885,0.042998046,0.030358328,0.00034731833],"about_ca_topic_score_codex":0.0036599368,"about_ca_topic_score_gemma":0.0071521522,"teacher_disagreement_score":0.011434138,"about_ca_system_score_codex":0.00095965544,"about_ca_system_score_gemma":0.001708823,"threshold_uncertainty_score":0.027354896},"labels":[],"label_agreement":null},{"id":"W1983286042","doi":"10.1145/1277741.1277845","title":"ARSA","year":2007,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":309,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Probabilistic latent semantic analysis; Computer science; Popularity; Sentiment analysis; Product (mathematics); Information retrieval; Feature selection; Topic model; Selection (genetic algorithm); Artificial intelligence; Data science; Psychology","score_opus":0.013678393659251501,"score_gpt":0.26569393274207775,"score_spread":0.25201553908282626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983286042","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019394044,0.0019979205,0.67675054,0.0019067962,0.0017753982,0.0009921727,0.01290486,0.060748924,0.22352931],"genre_scores_gemma":[0.22528216,0.0027335568,0.47864577,0.0019323936,0.0008373207,0.001388941,0.04463604,0.005413741,0.23913005],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99801564,0.00046364716,0.00016365071,0.00049160386,0.00068501703,0.00018036326],"domain_scores_gemma":[0.9974451,0.00051119464,0.00017839229,0.0009339354,0.0008326048,0.00009884735],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0018893094,0.0013158664,0.00085613027,0.0013339492,0.0007332014,0.0027983936,0.0025915045,0.0014963569,0.08071102],"category_scores_gemma":[0.0062108235,0.0004829984,0.0018521671,0.0014280777,0.00041179135,0.0027745743,0.0019274106,0.0011254541,0.08275122],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058387354,0.00040384536,0.0033092967,0.00078451185,0.00015976393,0.00030805,0.00028110636,0.031055905,0.0076308823,0.06408467,0.20089515,0.69050294],"study_design_scores_gemma":[0.00016373055,0.00037361783,0.0015847192,0.00018883178,0.00012014765,0.0006911601,0.00023849636,0.19135422,0.009012527,0.040949237,0.75523394,0.000089327375],"about_ca_topic_score_codex":0.0021452755,"about_ca_topic_score_gemma":0.002184969,"teacher_disagreement_score":0.919289,"about_ca_system_score_codex":0.0006479726,"about_ca_system_score_gemma":0.0013763837,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W1984900812","doi":"10.1109/icdmw.2011.85","title":"Fine-Grained Opinion Mining Using Conditional Random Fields","year":2011,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Conditional random field; Product (mathematics); Zoom; Focus (optics); Task (project management); Set (abstract data type); Information retrieval; Data mining; Data science; Sentiment analysis; Artificial intelligence; Machine learning","score_opus":0.08335766545174889,"score_gpt":0.2858484398381418,"score_spread":0.2024907743863929,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984900812","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10404699,0.0008150616,0.889825,0.00051731546,0.00014235736,0.00023032891,0.0010625992,0.0021950584,0.0011653666],"genre_scores_gemma":[0.76481223,0.00044435306,0.22870126,0.0002841188,0.00039731016,0.00022422444,0.0037904354,0.00008678076,0.0012592513],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986815,0.00037206963,0.000117486605,0.00038820435,0.00030184982,0.00013897459],"domain_scores_gemma":[0.9932856,0.004640805,0.0006103417,0.00030374568,0.0010390207,0.000120424345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003053607,0.0009471357,0.0011759144,0.0032317825,0.0006466869,0.000811642,0.0012879419,0.0009868894,0.00097217574],"category_scores_gemma":[0.006897362,0.000333065,0.0014159576,0.0017543999,0.00040224224,0.0012557793,0.0004924106,0.0011714686,0.0005995524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00093451265,0.0006691035,0.03518898,0.00046670434,0.00047884113,0.00095335697,0.00057236996,0.16912903,0.03249255,0.0063550286,0.013524721,0.7392348],"study_design_scores_gemma":[0.000026724962,0.00007658871,0.0031391624,0.000019364938,0.000056772944,0.000094721654,0.000050442053,0.9876133,0.0027267807,0.0052050157,0.00097231695,0.000018641058],"about_ca_topic_score_codex":0.0060868906,"about_ca_topic_score_gemma":0.006052351,"teacher_disagreement_score":0.0060868906,"about_ca_system_score_codex":0.0007314438,"about_ca_system_score_gemma":0.00076130807,"threshold_uncertainty_score":0.016149223},"labels":[],"label_agreement":null},{"id":"W1989239157","doi":"10.1007/s12559-015-9327-y","title":"What Goes Around Comes Around: Learning Sentiments in Online Medical Forums","year":2015,"lang":"en","type":"article","venue":"Cognitive Computation","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Gratitude; Confusion; Sentiment analysis; Context (archaeology); Online discussion; Agreement; Computer science; Class (philosophy); Psychology; Artificial intelligence; Natural language processing; Social psychology; World Wide Web; Linguistics","score_opus":0.057047348998307594,"score_gpt":0.3477357303283753,"score_spread":0.2906883813300677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989239157","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96939677,0.0007520633,0.019819956,0.002835833,0.00032057017,0.00005225723,0.0008031178,0.00012554498,0.0058939476],"genre_scores_gemma":[0.9926968,0.00017452701,0.005665406,0.00019493674,0.00024414874,0.00001824316,0.00041920072,0.000018592935,0.0005680993],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9990658,0.00051431934,0.00005366178,0.00013278455,0.00014233834,0.00009107507],"domain_scores_gemma":[0.9880156,0.008913288,0.0012675818,0.00023528119,0.0010223737,0.00054595224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025170185,0.0003279405,0.00023625426,0.0011070637,0.0007754346,0.0022864433,0.00028171318,0.0007986786,0.001348262],"category_scores_gemma":[0.01636022,0.00014185527,0.0003038803,0.0008374949,0.00046049658,0.0031310574,0.00071934547,0.00081150274,0.00039290474],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0029169659,0.0011745931,0.43543488,0.00082796067,0.00045241346,0.001252182,0.01610894,0.008539213,0.026208442,0.015788283,0.035618767,0.45567736],"study_design_scores_gemma":[0.0001515245,0.0009326096,0.43099034,0.00057923474,0.00063296594,0.00086132565,0.0312425,0.38689157,0.014982904,0.090140596,0.04239734,0.00019712432],"about_ca_topic_score_codex":0.0014007844,"about_ca_topic_score_gemma":0.0027767613,"teacher_disagreement_score":0.0025170185,"about_ca_system_score_codex":0.00048774644,"about_ca_system_score_gemma":0.00035154566,"threshold_uncertainty_score":0.013311386},"labels":[],"label_agreement":null},{"id":"W1993077754","doi":"10.1109/icdmw.2011.34","title":"AQA: Aspect-based Opinion Question Answering","year":2011,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Question answering; Sentiment analysis; Computer science; Quality (philosophy); Sentence; Strengths and weaknesses; Product (mathematics); Information retrieval; Data science; Artificial intelligence; Psychology; Epistemology","score_opus":0.04746127664266067,"score_gpt":0.26855104565668786,"score_spread":0.2210897690140272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993077754","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015419275,0.0009315753,0.9415701,0.0013291789,0.00028058555,0.0017757328,0.005767441,0.02826344,0.004662592],"genre_scores_gemma":[0.1611772,0.0007794783,0.8162008,0.0014753283,0.000445658,0.0017531649,0.013891842,0.0006154649,0.0036611245],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9939281,0.0024517428,0.00061449024,0.0010056297,0.0017564453,0.00024361258],"domain_scores_gemma":[0.9917509,0.0048490358,0.00063419127,0.00063430105,0.0018756933,0.00025592386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045213914,0.0015556655,0.0011823474,0.002878118,0.0008666744,0.0020786033,0.0024418368,0.0023653824,0.00802779],"category_scores_gemma":[0.018303487,0.0005467711,0.0019069468,0.0018788144,0.0006032863,0.00418998,0.0026910934,0.0016070458,0.0042846445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010446022,0.00066843355,0.0070547415,0.0021527896,0.00029170106,0.0006978458,0.0022245045,0.01096164,0.03731079,0.018841498,0.090061426,0.82869005],"study_design_scores_gemma":[0.00033859402,0.0006261413,0.0054145497,0.00020332793,0.0002340199,0.00094617123,0.0010556219,0.7666361,0.026350066,0.075242266,0.12278734,0.00016576416],"about_ca_topic_score_codex":0.0028740647,"about_ca_topic_score_gemma":0.0032399937,"teacher_disagreement_score":0.00802779,"about_ca_system_score_codex":0.0006805274,"about_ca_system_score_gemma":0.0009324245,"threshold_uncertainty_score":0.026855648},"labels":[],"label_agreement":null},{"id":"W1993158653","doi":"10.3115/1699648.1699681","title":"Predicting subjectivity in multimodal conversations","year":2009,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Conversation; Computer science; Natural language processing; Artificial intelligence; Subjectivity; Word (group theory); Speech recognition; Linguistics","score_opus":0.013788548681085958,"score_gpt":0.2597892602851458,"score_spread":0.24600071160405987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993158653","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90460885,0.0009819664,0.084655926,0.00026656705,0.00008566803,0.00016147076,0.0018023435,0.0003680229,0.007069174],"genre_scores_gemma":[0.9868644,0.0002883956,0.010180877,0.000043271117,0.00013732306,0.00009638637,0.0011406696,0.00003300396,0.0012157642],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899787,0.00045531918,0.00007395569,0.00022031997,0.00017879221,0.00007381857],"domain_scores_gemma":[0.99429613,0.0036778955,0.00081631413,0.00019169942,0.0007646865,0.0002533871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013498714,0.0006903411,0.00041609604,0.0022289527,0.00037643127,0.0011146851,0.00024025633,0.00054890336,0.0014838574],"category_scores_gemma":[0.008363859,0.00020147196,0.00042584643,0.0009068098,0.00027565207,0.0012442222,0.00068297266,0.00057303574,0.0007697029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017231392,0.0005139819,0.51554877,0.0007556407,0.00045869133,0.0006957199,0.0052979877,0.0136901885,0.091723114,0.0021393113,0.004790119,0.36266336],"study_design_scores_gemma":[0.00004991193,0.0009248342,0.6343864,0.00015334523,0.00034346478,0.0009398221,0.0048762187,0.31076115,0.028995192,0.0088848,0.009535344,0.00014959343],"about_ca_topic_score_codex":0.001475532,"about_ca_topic_score_gemma":0.0025078454,"teacher_disagreement_score":0.0022289527,"about_ca_system_score_codex":0.0003339021,"about_ca_system_score_gemma":0.00024850786,"threshold_uncertainty_score":0.0071388483},"labels":[],"label_agreement":null},{"id":"W1997860116","doi":"10.1109/wi-iat.2013.39","title":"Sentence Subjectivity Analysis in Social Domains","year":2013,"lang":"en","type":"article","venue":"2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Subjectivity; Automatic summarization; Computer science; Natural language processing; Sentiment analysis; Sentence; Artificial intelligence; Polarity (international relations); Feeling; Information retrieval; Recall; Product (mathematics); Linguistics; Psychology; Social psychology; Cognitive psychology; Epistemology; Mathematics","score_opus":0.05394313185231183,"score_gpt":0.29354262426562716,"score_spread":0.23959949241331532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997860116","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3653353,0.0042743497,0.5917958,0.0011579623,0.00042667318,0.0015911129,0.009772031,0.0025591112,0.023087682],"genre_scores_gemma":[0.8841808,0.0010132353,0.10214346,0.00015963544,0.00050252385,0.0008832869,0.007856205,0.00014063026,0.0031200945],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99567395,0.0019854559,0.00054849527,0.0006517435,0.00093321275,0.00020720794],"domain_scores_gemma":[0.9876552,0.0074879425,0.0016971332,0.00053181837,0.0023649484,0.000262858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004339596,0.0010499271,0.00085315463,0.007859591,0.00094802945,0.0026987623,0.0005431022,0.00069920655,0.0024741055],"category_scores_gemma":[0.013112723,0.00026897862,0.0012455134,0.0041742823,0.0008853842,0.0025260216,0.00103472,0.00075814466,0.0009291933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013222696,0.00067038665,0.08773469,0.0032318248,0.0010882176,0.0025380312,0.011567005,0.02033399,0.06467275,0.030402834,0.021061521,0.75537646],"study_design_scores_gemma":[0.00015711618,0.0010002721,0.28780466,0.00072799803,0.0011033835,0.0025935634,0.013986717,0.46189484,0.047966473,0.10971738,0.07263646,0.0004111189],"about_ca_topic_score_codex":0.0027825043,"about_ca_topic_score_gemma":0.002000617,"teacher_disagreement_score":0.007859591,"about_ca_system_score_codex":0.0011742701,"about_ca_system_score_gemma":0.00080573844,"threshold_uncertainty_score":0.022950232},"labels":[],"label_agreement":null},{"id":"W2001587475","doi":"10.1145/2009916.2010006","title":"ILDA","year":2011,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":202,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Latent Dirichlet allocation; Computer science; Interdependence; Probabilistic logic; Set (abstract data type); Product (mathematics); Task (project management); Topic model; Sentiment analysis; Component (thermodynamics); The Internet; Artificial intelligence; Data science; Interpretation (philosophy); Information retrieval; Natural language processing; Machine learning; World Wide Web; Mathematics; Engineering","score_opus":0.06601077421307201,"score_gpt":0.22621445826722755,"score_spread":0.16020368405415553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001587475","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0066701877,0.004752877,0.2526715,0.0050348593,0.003833041,0.0012268657,0.098812126,0.14917147,0.477827],"genre_scores_gemma":[0.06375135,0.0039631715,0.20912927,0.0063848165,0.0013897347,0.0018811844,0.30361196,0.020327307,0.38956124],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9964683,0.0006815991,0.0003418006,0.0009192733,0.0011913778,0.00039776057],"domain_scores_gemma":[0.9955664,0.0007412248,0.00021704184,0.0017749334,0.0013356656,0.00036471876],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0026509066,0.0016488401,0.0010173948,0.0034655372,0.0017622429,0.0060998304,0.0030077128,0.002428487,0.24598947],"category_scores_gemma":[0.010382536,0.0009152444,0.0018747769,0.003359475,0.000632923,0.0063609323,0.005635225,0.0022923977,0.31529427],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025298298,0.00012024882,0.0020738384,0.0006273858,0.000059759608,0.00021177049,0.00022042033,0.0012617855,0.0023705105,0.018207572,0.66511095,0.30948284],"study_design_scores_gemma":[0.000030874555,0.000032452645,0.00077626144,0.000102576065,0.000019868898,0.00028940954,0.00010559767,0.0041099726,0.0013410709,0.009239686,0.9839203,0.000032017564],"about_ca_topic_score_codex":0.003621458,"about_ca_topic_score_gemma":0.0050916644,"teacher_disagreement_score":0.75401056,"about_ca_system_score_codex":0.0013960241,"about_ca_system_score_gemma":0.0021783786,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2015126859","doi":"10.1109/aina.2015.253","title":"Consumers' Sentiment Analysis of Popular Phone Brands and Operating System Preference Using Twitter Data: A Feasibility Study","year":2015,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Sentiment analysis; Computer science; Phone; Social media; Competitor analysis; Product (mathematics); Smart phone; World Wide Web; Advertising; Business; Marketing; Telecommunications","score_opus":0.2885873013425486,"score_gpt":0.3682342263455372,"score_spread":0.07964692500298859,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015126859","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9959935,0.000015850665,0.0013220507,0.00011097923,0.000007729367,0.00026310788,0.00065676466,0.0000114154755,0.0016186538],"genre_scores_gemma":[0.99492115,0.000040014696,0.0028970314,0.0000683264,0.000025380828,0.0003862539,0.00086186826,0.000007662055,0.0007922521],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985936,0.0005890581,0.00010049044,0.000173474,0.00037784534,0.00016550023],"domain_scores_gemma":[0.9935336,0.003087669,0.0006360297,0.00030906548,0.0021394736,0.00029420806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027104176,0.00033858942,0.00033927642,0.0009047286,0.0005575059,0.00081372436,0.00025802635,0.00049135875,0.0023814747],"category_scores_gemma":[0.0074857906,0.00021452724,0.0005535782,0.0009923191,0.00031332867,0.0012001491,0.0005317045,0.00047644,0.00095328875],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018754238,0.0028236073,0.9064622,0.00031623172,0.00014389846,0.0005417269,0.006681302,0.0010184281,0.017527575,0.0006814911,0.0021673974,0.059760764],"study_design_scores_gemma":[0.00011195672,0.0031025698,0.94557065,0.00004640656,0.0001863652,0.00031759526,0.012437839,0.026370272,0.0075899824,0.0005684675,0.0036217032,0.00007621021],"about_ca_topic_score_codex":0.0055910703,"about_ca_topic_score_gemma":0.0046870937,"teacher_disagreement_score":0.0055910703,"about_ca_system_score_codex":0.00046471602,"about_ca_system_score_gemma":0.00044358385,"threshold_uncertainty_score":0.014334202},"labels":[],"label_agreement":null},{"id":"W2019374767","doi":"10.3115/1621474.1621496","title":"CLaC and CLaC-NB","year":2007,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Artificial intelligence; Task (project management); Recall; Domain (mathematical analysis); Annotation; Natural language processing; SemEval; Precision and recall; Machine learning; Information retrieval; Mathematics; Psychology","score_opus":0.010639926923885648,"score_gpt":0.2564107944565275,"score_spread":0.24577086753264182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019374767","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12296012,0.015874395,0.25145328,0.0070997565,0.012029134,0.0074583804,0.1935173,0.23542161,0.15418606],"genre_scores_gemma":[0.12996666,0.0011577866,0.4068117,0.0032187896,0.0011448439,0.0045260913,0.36863607,0.00961914,0.07491892],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9933663,0.001316838,0.00045774918,0.0021879163,0.0017928887,0.00087821943],"domain_scores_gemma":[0.9879291,0.001557936,0.00036412547,0.0040771356,0.005106704,0.000964923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007319604,0.004226184,0.0031213583,0.0101833325,0.0033217994,0.003476352,0.0066439006,0.006648742,0.03264604],"category_scores_gemma":[0.017139005,0.0015094918,0.0025279147,0.0046008136,0.0010891374,0.005207431,0.004863638,0.005048898,0.037807442],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0029483298,0.001052325,0.0036374978,0.0012697609,0.0006095977,0.00025090968,0.00017179472,0.0071340157,0.013473205,0.0073692077,0.5972003,0.36488307],"study_design_scores_gemma":[0.0021471935,0.0011782481,0.015448305,0.0004918072,0.00063969765,0.0011800653,0.00049064116,0.39237243,0.047777876,0.015627882,0.5221472,0.0004987734],"about_ca_topic_score_codex":0.047309443,"about_ca_topic_score_gemma":0.06484065,"teacher_disagreement_score":0.047309443,"about_ca_system_score_codex":0.0032272572,"about_ca_system_score_gemma":0.0041539157,"threshold_uncertainty_score":0.10921186},"labels":[],"label_agreement":null},{"id":"W2023736093","doi":"10.3758/s13428-012-0314-x","title":"Norms of valence, arousal, and dominance for 13,915 English lemmas","year":2013,"lang":"en","type":"article","venue":"Behavior Research Methods","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1978,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Psychology; Valence (chemistry); Stimulus (psychology); Taboo; Cognitive psychology; Arousal; Linguistics; Social psychology; Sociology","score_opus":0.22356297190789665,"score_gpt":0.5275938138999604,"score_spread":0.30403084199206376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023736093","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9622657,0.0010968387,0.004144818,0.00014489383,0.0000955308,0.000103037324,0.01928642,0.00013991671,0.012722879],"genre_scores_gemma":[0.977536,0.00037734624,0.0036355972,0.000037866557,0.000034383916,0.00019934522,0.015909923,0.00009917166,0.0021703087],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99774253,0.00087138865,0.00048093247,0.00034285377,0.00044616926,0.00011626762],"domain_scores_gemma":[0.97481596,0.019812955,0.0015335187,0.0006141239,0.0029515722,0.00027193557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019519113,0.00033141414,0.0003496875,0.0020501718,0.00069521985,0.0012936612,0.00023175335,0.00026501468,0.004568661],"category_scores_gemma":[0.017321859,0.00012880542,0.00022622095,0.002466502,0.00053686486,0.0008428483,0.00051354297,0.00034239297,0.0014495137],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0057725,0.00038150584,0.6022362,0.0031610243,0.00042348664,0.0018638994,0.030691773,0.0018552078,0.040216953,0.012311819,0.03131493,0.26977077],"study_design_scores_gemma":[0.00013960972,0.0007411871,0.8824816,0.0004344442,0.00033247727,0.001974592,0.013307641,0.0053136228,0.013352892,0.0035907065,0.07820612,0.0001251919],"about_ca_topic_score_codex":0.0035806394,"about_ca_topic_score_gemma":0.0050222194,"teacher_disagreement_score":0.004568661,"about_ca_system_score_codex":0.00067262555,"about_ca_system_score_gemma":0.00045691023,"threshold_uncertainty_score":0.015283704},"labels":[],"label_agreement":null},{"id":"W2023817566","doi":"10.1145/2348283.2348533","title":"Aspect-based opinion mining from product reviews","year":2012,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Sentiment analysis; Computer science; Product (mathematics); Zoom; Process (computing); Data science; The Internet; Task (project management); Set (abstract data type); World Wide Web; Information retrieval; Engineering; Artificial intelligence","score_opus":0.06175961817010948,"score_gpt":0.3022948321280577,"score_spread":0.24053521395794822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023817566","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23679091,0.014956004,0.70546484,0.002932038,0.001307599,0.0020866687,0.01551199,0.004018141,0.016931921],"genre_scores_gemma":[0.63725877,0.004731955,0.32044652,0.0005550723,0.0014216838,0.0010559945,0.028069293,0.00021996253,0.0062408485],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99636877,0.0007383993,0.0004936008,0.0006979369,0.0015057125,0.00019557032],"domain_scores_gemma":[0.99352396,0.002630369,0.00085238577,0.00029365384,0.0025540465,0.00014547653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024457087,0.0015568644,0.001684285,0.0049298923,0.0005525611,0.0020910196,0.0013924026,0.0011389435,0.0013826729],"category_scores_gemma":[0.012925618,0.00038886914,0.0016989463,0.0035735774,0.0002839475,0.0017906416,0.0007265671,0.0011417728,0.0018377912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009789916,0.00046643717,0.02166249,0.0016258643,0.00066230516,0.0015145952,0.00092647254,0.019074712,0.016608011,0.0041319965,0.04739859,0.8849495],"study_design_scores_gemma":[0.0001744981,0.00049479026,0.020757142,0.0003120514,0.00055835827,0.0014999565,0.00093538355,0.9047657,0.012659301,0.016373418,0.041363265,0.00010610607],"about_ca_topic_score_codex":0.0020180936,"about_ca_topic_score_gemma":0.0031052923,"teacher_disagreement_score":0.0049298923,"about_ca_system_score_codex":0.00070780824,"about_ca_system_score_gemma":0.0007874592,"threshold_uncertainty_score":0.012934327},"labels":[],"label_agreement":null},{"id":"W2027417703","doi":"10.1007/s10115-010-0287-y","title":"An information gain-based approach for recommending useful product reviews","year":2010,"lang":"en","type":"article","venue":"Knowledge and Information Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Helpfulness; Computer science; Product (mathematics); Recommender system; Ranking (information retrieval); Quality (philosophy); Order (exchange); Task (project management); Information retrieval; Data science; Business; Engineering","score_opus":0.03250743570292643,"score_gpt":0.2886427697291577,"score_spread":0.2561353340262313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027417703","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14743298,0.006187667,0.8279379,0.002145537,0.0005627064,0.0010306265,0.0015171065,0.0025501705,0.010635312],"genre_scores_gemma":[0.7109576,0.0013721777,0.27876347,0.00040488646,0.0007892257,0.00039273754,0.001148431,0.0000721033,0.006099379],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974769,0.0004163119,0.00020977242,0.00033546373,0.0014462707,0.00011533023],"domain_scores_gemma":[0.99561876,0.0020369168,0.00024926657,0.00016206909,0.001839573,0.000093397255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023840931,0.0011173775,0.0018986452,0.0070535787,0.0008148514,0.0017484548,0.0013703495,0.001715071,0.0019738325],"category_scores_gemma":[0.008329907,0.00043065706,0.0012555544,0.0037789212,0.00047093528,0.0025647946,0.00071798015,0.0010161225,0.0008293616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011869675,0.0013395507,0.011914807,0.00068543013,0.00090004405,0.000337252,0.0002578143,0.027598675,0.018792374,0.005585945,0.015513563,0.9158876],"study_design_scores_gemma":[0.0001908849,0.0007892297,0.009662986,0.00007044531,0.00063302147,0.0005826137,0.0001069824,0.964583,0.009652946,0.008892618,0.0047327867,0.00010245032],"about_ca_topic_score_codex":0.003749022,"about_ca_topic_score_gemma":0.0074480623,"teacher_disagreement_score":0.0070535787,"about_ca_system_score_codex":0.0011012071,"about_ca_system_score_gemma":0.0010612807,"threshold_uncertainty_score":0.0126084685},"labels":[],"label_agreement":null},{"id":"W2029146157","doi":"10.1007/s10844-013-0273-4","title":"Dream sentiment analysis using second order soft co-occurrences (SOSCO) and time course representations","year":2013,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Dream; Computer science; Sentiment analysis; Representation (politics); Artificial intelligence; Natural language processing; Annotation; Scale (ratio); Domain (mathematical analysis); Feature (linguistics); Linguistics; Psychology; Mathematics","score_opus":0.02410377205739697,"score_gpt":0.3056176712115789,"score_spread":0.2815138991541819,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029146157","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43270347,0.00084027444,0.5411112,0.0004414746,0.0003337552,0.0003106846,0.0071649314,0.0030003858,0.014093902],"genre_scores_gemma":[0.91006273,0.00017364617,0.081641495,0.000026362568,0.00010358972,0.00013821629,0.0039559016,0.000111999834,0.0037859778],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995871,0.000084015504,0.00004403225,0.00009324641,0.000120477795,0.0000710241],"domain_scores_gemma":[0.99898154,0.00041016826,0.00014316832,0.00009721672,0.00029280432,0.000075043485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006156411,0.00041654176,0.00041873302,0.0040946603,0.00049371936,0.0010159255,0.00047507542,0.00035518725,0.0039285626],"category_scores_gemma":[0.0025442275,0.00013406863,0.00080444646,0.0034024687,0.00023149364,0.0010439178,0.0006652454,0.0005532493,0.00092415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022573967,0.00055008807,0.060228664,0.0004886604,0.00043019015,0.0006067328,0.0009964096,0.03703146,0.029667607,0.026419668,0.01584417,0.825479],"study_design_scores_gemma":[0.000044231543,0.0001618364,0.0379275,0.000043733453,0.00016730613,0.00033645498,0.0008420082,0.92604846,0.008667309,0.016889242,0.008798881,0.000073056144],"about_ca_topic_score_codex":0.005560923,"about_ca_topic_score_gemma":0.009040698,"teacher_disagreement_score":0.005560923,"about_ca_system_score_codex":0.0005069197,"about_ca_system_score_gemma":0.0006408347,"threshold_uncertainty_score":0.013142407},"labels":[],"label_agreement":null},{"id":"W203144251","doi":"10.1007/978-0-387-79420-4_13","title":"Blog Data Mining: The Predictive Power of Sentiments","year":2008,"lang":"en","type":"book-chapter","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Probabilistic latent semantic analysis; Sentiment analysis; Computer science; Predictive power; Product (mathematics); Information retrieval; Data mining; Data science; Artificial intelligence; Mathematics","score_opus":0.05895072006431685,"score_gpt":0.2711573217966683,"score_spread":0.21220660173235145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W203144251","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017466672,0.05985729,0.85073054,0.0087516485,0.0032234655,0.00022846894,0.0074173864,0.0071836025,0.045140963],"genre_scores_gemma":[0.16997278,0.06514503,0.69700855,0.0022439577,0.0054801246,0.00058018434,0.011165469,0.0018569147,0.046546984],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992995,0.00014741725,0.000051997653,0.00016323518,0.00031068543,0.000027145785],"domain_scores_gemma":[0.99686784,0.0023854186,0.0001146635,0.0002727579,0.00029886543,0.00006045812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017914651,0.0011453737,0.0010053666,0.002674808,0.00041699465,0.003055124,0.0012224265,0.00079240813,0.005434033],"category_scores_gemma":[0.007692928,0.00064307055,0.00084709714,0.0057033785,0.00055126724,0.004146738,0.0009374196,0.0021819077,0.006145881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004800247,0.00008072781,0.0025682834,0.00056277687,0.00008177667,0.00007123402,0.00016404282,0.0025219384,0.0020019435,0.022199545,0.0767546,0.8929452],"study_design_scores_gemma":[0.00004202492,0.00013339632,0.008823597,0.00084155955,0.00021051051,0.001056649,0.0003368388,0.25069115,0.012042303,0.4027554,0.32294112,0.00012542208],"about_ca_topic_score_codex":0.0008253119,"about_ca_topic_score_gemma":0.0012294463,"teacher_disagreement_score":0.005434033,"about_ca_system_score_codex":0.00040034385,"about_ca_system_score_gemma":0.0006301617,"threshold_uncertainty_score":0.018178642},"labels":[],"label_agreement":null},{"id":"W2031850347","doi":"10.1145/2502069.2502070","title":"Identifying purpose behind electoral tweets","year":2013,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Automatic summarization; Computer science; Popularity; Task (project management); Class (philosophy); Baseline (sea); Artificial intelligence; Natural language processing; Key (lock); Event (particle physics); Sentiment analysis; Information retrieval; Social media; Machine learning; World Wide Web; Computer security; Political science","score_opus":0.024062246839638406,"score_gpt":0.2599845094201968,"score_spread":0.2359222625805584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031850347","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.951974,0.0007510131,0.016605774,0.00096036843,0.0003176483,0.00019879702,0.0135613065,0.0011411768,0.014490038],"genre_scores_gemma":[0.9702588,0.00028370676,0.010833957,0.00014728511,0.00020839735,0.00011113434,0.013994695,0.00007992684,0.0040821196],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99925405,0.00019525371,0.00008981513,0.00015219476,0.00017330059,0.0001353008],"domain_scores_gemma":[0.9965636,0.0015629282,0.0006799158,0.00035617824,0.0006168648,0.00022062397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092645193,0.00037859153,0.00035507488,0.0032707169,0.0009068353,0.00131584,0.00026524498,0.0005216214,0.001511745],"category_scores_gemma":[0.004729157,0.00020201711,0.00039479815,0.0015277112,0.00021804696,0.0013749014,0.0008847644,0.0004581635,0.0012824069],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010274271,0.00023090052,0.723106,0.00051901414,0.00014372848,0.0010366587,0.0046643056,0.0016566057,0.03232657,0.003889927,0.034966994,0.19643183],"study_design_scores_gemma":[0.00003881937,0.00017159643,0.8572998,0.00015829732,0.00011849252,0.0011223597,0.0065483055,0.049197327,0.023068782,0.0051831156,0.057016294,0.00007681216],"about_ca_topic_score_codex":0.0025225566,"about_ca_topic_score_gemma":0.005732157,"teacher_disagreement_score":0.0032707169,"about_ca_system_score_codex":0.00037108638,"about_ca_system_score_gemma":0.0003552865,"threshold_uncertainty_score":0.005057335},"labels":[],"label_agreement":null},{"id":"W2037974147","doi":"10.1109/icmecg.2011.46","title":"Study of the Relationship between the Emotional Tendencies of Web Financial Information and the Financial Crisis of Listed Companies","year":2011,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"The Internet; Business; Finance; Financial crisis; Quarter (Canadian coin); Financial ratio; Financial analysis; Computer science; Economics; World Wide Web","score_opus":0.0715192584620328,"score_gpt":0.26206314595486274,"score_spread":0.19054388749282994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037974147","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9986002,0.000067692294,0.00022198942,0.00012506034,0.0000061593437,0.000005958589,0.000049665927,0.0000020831035,0.00092116714],"genre_scores_gemma":[0.99953854,0.000059740796,0.000107957705,0.000022802058,0.000010827894,0.0000058561086,0.000053956337,6.2068005e-7,0.00019970273],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997187,0.00011983015,0.000022229435,0.000030616848,0.000068080604,0.00004049611],"domain_scores_gemma":[0.99623835,0.0021910877,0.0007956945,0.00006179486,0.00043058465,0.00028242538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005734562,0.00011282072,0.00010901016,0.0007079894,0.00024617394,0.0005784318,0.000074042415,0.0002131587,0.0012818486],"category_scores_gemma":[0.0040902817,0.000061720835,0.00018340925,0.00048959407,0.00018509086,0.00048308223,0.00024012467,0.00039546206,0.00013044363],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027618147,0.0002891482,0.971962,0.00007567423,0.00013018721,0.00029230228,0.0021962733,0.00040661375,0.0030634096,0.00046161292,0.00063774036,0.020208929],"study_design_scores_gemma":[0.0000039820966,0.00016203117,0.99431944,0.0000093897215,0.000040257815,0.00014145838,0.0021244453,0.0021126352,0.00046995492,0.0001978632,0.00041033395,0.000008117328],"about_ca_topic_score_codex":0.00089501805,"about_ca_topic_score_gemma":0.0012198496,"teacher_disagreement_score":0.0012818486,"about_ca_system_score_codex":0.00024931406,"about_ca_system_score_gemma":0.00015860371,"threshold_uncertainty_score":0.004288256},"labels":[],"label_agreement":null},{"id":"W2038680758","doi":"10.1007/s11280-012-0179-z","title":"Riding the tide of sentiment change: sentiment analysis with evolving online reviews","year":2012,"lang":"en","type":"article","venue":"World Wide Web","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Sentiment analysis; Revenue; Data science; Work (physics); Data mining; Information retrieval; Artificial intelligence","score_opus":0.050330644985061776,"score_gpt":0.29485799971546356,"score_spread":0.24452735473040177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038680758","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7331383,0.005695701,0.24923542,0.0026490767,0.0005403008,0.00020628265,0.0012101281,0.0007605922,0.006564242],"genre_scores_gemma":[0.9351543,0.0014887227,0.059767734,0.00019492776,0.00049449725,0.000067288056,0.00083804096,0.00009325358,0.0019011858],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847287,0.0007591845,0.00008723006,0.00020173838,0.00040814697,0.0000707552],"domain_scores_gemma":[0.9954726,0.0026699053,0.0006455465,0.00022676073,0.0008305051,0.00015467781],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024419192,0.0004862487,0.00061866036,0.0015746452,0.00044307316,0.0019623507,0.0004696164,0.0006876202,0.00071041775],"category_scores_gemma":[0.012673091,0.00026666978,0.0004925336,0.001863244,0.0003564033,0.0024038441,0.0005992788,0.00080041075,0.0004061938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00077015074,0.00056459085,0.071352705,0.0006857555,0.0006658085,0.0010307573,0.0018812293,0.028698718,0.029568281,0.005667593,0.023403818,0.83571064],"study_design_scores_gemma":[0.000039064482,0.00030664215,0.04529016,0.00008535633,0.00026903415,0.00044732567,0.0013067891,0.9153672,0.009137674,0.017275456,0.010414227,0.00006105846],"about_ca_topic_score_codex":0.0016078645,"about_ca_topic_score_gemma":0.0025259831,"teacher_disagreement_score":0.0024419192,"about_ca_system_score_codex":0.00034037288,"about_ca_system_score_gemma":0.00032333352,"threshold_uncertainty_score":0.01291424},"labels":[],"label_agreement":null},{"id":"W2040467972","doi":"10.1111/j.1467-8640.2012.00460.x","title":"CROWDSOURCING A WORD–EMOTION ASSOCIATION LEXICON","year":2012,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2584,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"National Research Council Canada","keywords":"Lexicon; Crowdsourcing; Computer science; Annotation; Word (group theory); Natural language processing; Polarity (international relations); Term (time); Sentiment analysis; Association (psychology); Artificial intelligence; Quality (philosophy); Psychology; Linguistics; World Wide Web","score_opus":0.038033010955243855,"score_gpt":0.29819672779789935,"score_spread":0.2601637168426555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040467972","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1321987,0.00086027035,0.7681553,0.0044390303,0.0014252653,0.0041715046,0.019991456,0.007718172,0.06104025],"genre_scores_gemma":[0.504193,0.000529061,0.43990242,0.0012892593,0.00051789865,0.0043379962,0.027917046,0.0013743885,0.019938936],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9925218,0.0032299317,0.0005862102,0.0012080267,0.0021858176,0.00026808155],"domain_scores_gemma":[0.9810643,0.0096487375,0.0011173314,0.002770295,0.0048491177,0.00055024103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006199582,0.0014068915,0.0009753044,0.005771454,0.0023362383,0.0028067492,0.0013023199,0.0011904524,0.006337135],"category_scores_gemma":[0.0290142,0.00057660684,0.0012599516,0.0040083225,0.001156701,0.00319832,0.004650285,0.0016898843,0.004142233],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016501955,0.0008002233,0.020157123,0.0025360603,0.00041115002,0.0018886314,0.011829038,0.02388756,0.10463715,0.050976604,0.14487909,0.6363473],"study_design_scores_gemma":[0.00047420192,0.0003897514,0.024684979,0.00057954394,0.00037291058,0.00094863673,0.010841203,0.35819522,0.051646136,0.19935684,0.35204664,0.000464031],"about_ca_topic_score_codex":0.0040632086,"about_ca_topic_score_gemma":0.0076778177,"teacher_disagreement_score":0.006337135,"about_ca_system_score_codex":0.0015808195,"about_ca_system_score_gemma":0.0025940507,"threshold_uncertainty_score":0.032786906},"labels":[],"label_agreement":null},{"id":"W2045333577","doi":"10.1080/17470218.2014.970204","title":"Avoid violence, rioting, and outrage; approach celebration, delight, and strength: Using large text corpora to compute valence, arousal, and the basic emotions","year":2014,"lang":"en","type":"article","venue":"Quarterly Journal of Experimental Psychology","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Valence (chemistry); Arousal; Psychology; Emotion classification; Generalization; Cognitive psychology; Natural language processing; Computer science; Artificial intelligence; Social psychology; Mathematics","score_opus":0.01782152587639769,"score_gpt":0.2957598772909407,"score_spread":0.27793835141454304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045333577","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9245593,0.0016625131,0.0594964,0.0011886323,0.00014679301,0.00014286472,0.0024675094,0.0004781991,0.009857742],"genre_scores_gemma":[0.95884204,0.00030548192,0.035641816,0.00010758805,0.000107963206,0.0001451412,0.0036758708,0.000055897395,0.0011181447],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991352,0.00046761887,0.00004958654,0.00015505838,0.00015777726,0.000034763743],"domain_scores_gemma":[0.99669194,0.0019444177,0.0005941019,0.0002462382,0.00037527506,0.00014811357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017906746,0.00055703736,0.00027717502,0.0018003157,0.00056087325,0.0014031997,0.00039953802,0.00046053607,0.0008596293],"category_scores_gemma":[0.010284369,0.00018470616,0.00032936616,0.0011442428,0.00073101243,0.0015609672,0.00082695315,0.0011249481,0.0006779313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016625144,0.00077254,0.38678545,0.000884705,0.00042126988,0.00033457822,0.005559798,0.023199663,0.013791033,0.014850488,0.029512662,0.52222526],"study_design_scores_gemma":[0.00016810815,0.0005052167,0.49871382,0.00025661266,0.00019396073,0.00042966357,0.005179923,0.41455188,0.01178213,0.03705771,0.031010844,0.00015013975],"about_ca_topic_score_codex":0.00220866,"about_ca_topic_score_gemma":0.0034628147,"teacher_disagreement_score":0.00220866,"about_ca_system_score_codex":0.00048821652,"about_ca_system_score_gemma":0.00025125267,"threshold_uncertainty_score":0.009470105},"labels":[],"label_agreement":null},{"id":"W2045584434","doi":"10.1016/j.eswa.2012.12.084","title":"Sentiment polarity detection in Spanish reviews combining supervised and unsupervised approaches","year":2012,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":142,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McGill University","keywords":"Computer science; Artificial intelligence; Machine learning; Classifier (UML); Sentiment analysis; Unsupervised learning; Polarity (international relations); Supervised learning; Natural language processing; Pattern recognition (psychology); Artificial neural network","score_opus":0.05847855461083074,"score_gpt":0.2670254764919242,"score_spread":0.20854692188109347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045584434","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91353935,0.0035054036,0.0557332,0.00065915857,0.00050587277,0.00043850671,0.0045419894,0.0010274043,0.020049134],"genre_scores_gemma":[0.95399004,0.001091492,0.032935116,0.00011513527,0.00045018425,0.00023592345,0.0049456772,0.00012310276,0.0061132144],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984249,0.0006694747,0.00013909081,0.00022035558,0.00042506136,0.00012118037],"domain_scores_gemma":[0.9927845,0.0016462826,0.0007368058,0.00022361241,0.004409327,0.00019947063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018111074,0.0006298225,0.0005755359,0.002768425,0.00042043193,0.001167509,0.00025399725,0.00036162333,0.0010681984],"category_scores_gemma":[0.0073311883,0.00013538968,0.00048249267,0.0013053152,0.00015038461,0.0005745712,0.00038319224,0.00030835276,0.0010106156],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001513151,0.00045192163,0.26203334,0.0017713673,0.00048559144,0.0008073703,0.002143784,0.0020181024,0.09973361,0.0011761029,0.030879684,0.596986],"study_design_scores_gemma":[0.00016992935,0.0008119774,0.7358437,0.00050283334,0.00096027215,0.0012526507,0.0047635348,0.12652245,0.061867677,0.0025417574,0.0646139,0.00014930684],"about_ca_topic_score_codex":0.0030000315,"about_ca_topic_score_gemma":0.005756351,"teacher_disagreement_score":0.0030000315,"about_ca_system_score_codex":0.00039432404,"about_ca_system_score_gemma":0.00074222207,"threshold_uncertainty_score":0.009578109},"labels":[],"label_agreement":null},{"id":"W2045736743","doi":"10.1145/2775441.2775472","title":"Using social media sentiment analysis for interaction design choices","year":2015,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Sentiment analysis; Computer science; Social media; Interaction design; Human–computer interaction; Data science; World Wide Web; Artificial intelligence","score_opus":0.2838293214497852,"score_gpt":0.38311558937698154,"score_spread":0.09928626792719636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045736743","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15039776,0.00055336644,0.7823056,0.0059224376,0.00030465767,0.0017599589,0.0005978758,0.0022339271,0.05592442],"genre_scores_gemma":[0.57553756,0.00031078266,0.416395,0.0005479089,0.00008901211,0.0013942878,0.00048569866,0.0007092061,0.0045304857],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98223317,0.012242229,0.0010176704,0.0010084526,0.0030451324,0.0004535084],"domain_scores_gemma":[0.9708302,0.017418288,0.001983791,0.0016242451,0.007339022,0.0008044558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016546804,0.0016517044,0.00074591633,0.0030876466,0.0021427497,0.007039759,0.0010502322,0.001012832,0.0050591966],"category_scores_gemma":[0.047553446,0.00082395354,0.0011320389,0.0011861082,0.0014411,0.0068204394,0.0020109084,0.0014708261,0.0017931517],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019650408,0.00078235037,0.037115313,0.0028312001,0.0006739193,0.001202797,0.04923608,0.010960349,0.11788413,0.12637077,0.029434519,0.6215436],"study_design_scores_gemma":[0.0005732734,0.0019206209,0.030835215,0.0018357077,0.0016384571,0.0010314336,0.042196997,0.2891598,0.10970543,0.23379196,0.28675437,0.0005567142],"about_ca_topic_score_codex":0.0007884158,"about_ca_topic_score_gemma":0.0020516133,"teacher_disagreement_score":0.016546804,"about_ca_system_score_codex":0.0019868123,"about_ca_system_score_gemma":0.0013609676,"threshold_uncertainty_score":0.08750892},"labels":[],"label_agreement":null},{"id":"W2047676437","doi":"10.1145/1871437.1871739","title":"Opinion digger","year":2010,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":194,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science","score_opus":0.011625284470422522,"score_gpt":0.261038368986786,"score_spread":0.2494130845163635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047676437","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059580427,0.0022090718,0.6511616,0.0028439981,0.0012678418,0.0024547654,0.097751014,0.13391832,0.04881297],"genre_scores_gemma":[0.19102167,0.0009970017,0.64164364,0.0017604925,0.00053562544,0.0018034113,0.12079903,0.0023508305,0.039088305],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987889,0.00021161625,0.00013228696,0.0003324901,0.00043989473,0.00009477857],"domain_scores_gemma":[0.9970385,0.0013443552,0.0002861057,0.00036705603,0.0008693665,0.000094723866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015693489,0.0009348366,0.0007541674,0.0043550692,0.00050555076,0.0012801259,0.001283303,0.0010932185,0.011048878],"category_scores_gemma":[0.007929564,0.00037747037,0.00079034443,0.0027671312,0.00019067731,0.0018224838,0.0010546217,0.0010784735,0.008130248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037849782,0.00027573816,0.007969287,0.0008789241,0.00015403982,0.00032106537,0.0002666273,0.0027842128,0.009230797,0.0052835094,0.26317382,0.70928335],"study_design_scores_gemma":[0.0003769234,0.0005694626,0.022225577,0.00024003774,0.00028946047,0.0012198909,0.00059824815,0.45475516,0.06292029,0.038121678,0.4185121,0.00017125793],"about_ca_topic_score_codex":0.0020264757,"about_ca_topic_score_gemma":0.0075063803,"teacher_disagreement_score":0.011048878,"about_ca_system_score_codex":0.00072297384,"about_ca_system_score_gemma":0.0007876269,"threshold_uncertainty_score":0.03696221},"labels":[],"label_agreement":null},{"id":"W204776251","doi":"10.1007/978-3-642-21043-3_18","title":"Compact Features for Sentiment Analysis","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.029125070847994126,"score_gpt":0.276080730006287,"score_spread":0.2469556591582929,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W204776251","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0145549,0.0012051801,0.969106,0.000227215,0.00039847003,0.00014512776,0.0028566986,0.007099667,0.0044066757],"genre_scores_gemma":[0.23272705,0.0013857489,0.73239493,0.00023636087,0.0007324608,0.0007644892,0.014541115,0.0014352709,0.015782584],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99953675,0.00007852975,0.0000506611,0.00010523194,0.00017856703,0.000050204188],"domain_scores_gemma":[0.99910444,0.00032690645,0.00008915468,0.00021137789,0.0002344531,0.000033668497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005385728,0.000920961,0.0008769404,0.0015876922,0.00045685473,0.0011481767,0.00068490626,0.00055519096,0.011152614],"category_scores_gemma":[0.0028653215,0.0003831367,0.00066033954,0.0021224236,0.00023082078,0.0021393837,0.0011288325,0.0010552915,0.0062542646],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028792312,0.00009108027,0.0004507878,0.0002049791,0.000050941937,0.00010265354,0.000056564677,0.005133556,0.03576137,0.012478551,0.031430695,0.9139509],"study_design_scores_gemma":[0.00014121165,0.00041629758,0.004796448,0.00014889044,0.00017574376,0.00072201935,0.00014362924,0.6927676,0.05964617,0.13665298,0.10429233,0.0000966618],"about_ca_topic_score_codex":0.0005773811,"about_ca_topic_score_gemma":0.0008435899,"teacher_disagreement_score":0.011152614,"about_ca_system_score_codex":0.00028291956,"about_ca_system_score_gemma":0.0003173366,"threshold_uncertainty_score":0.03730923},"labels":[],"label_agreement":null},{"id":"W2062794497","doi":"10.1080/19331680802149608","title":"Classifying Party Affiliation from Political Speech","year":2008,"lang":"en","type":"article","venue":"Journal of Information Technology & Politics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":140,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Dependency (UML); Ideology; Politics; Political science; Computer science; Artificial intelligence; Law","score_opus":0.022297556308323526,"score_gpt":0.2645276957349004,"score_spread":0.2422301394265769,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062794497","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9687029,0.0002484592,0.017771475,0.000322364,0.00019007763,0.00016155126,0.004816335,0.00036489943,0.0074218926],"genre_scores_gemma":[0.9856345,0.00008711491,0.007217027,0.00003091555,0.0001388298,0.0001014264,0.005570276,0.000022824019,0.0011970142],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875724,0.00046853206,0.0001227478,0.00018745098,0.00031112626,0.00015291806],"domain_scores_gemma":[0.9937831,0.0033539073,0.00066093006,0.00031981527,0.0016778135,0.00020427693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020319305,0.00034449913,0.0004484802,0.0036388398,0.00038917272,0.0010365798,0.0002496937,0.0005113466,0.0027679643],"category_scores_gemma":[0.009212417,0.00013154231,0.00032451382,0.0017834412,0.00016936958,0.0008619271,0.0004445738,0.0007013776,0.0021137483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012053982,0.0005226009,0.5470618,0.00033759934,0.00032635365,0.0003990926,0.00077805325,0.016298711,0.03534499,0.0020005181,0.016667861,0.37905708],"study_design_scores_gemma":[0.00004309068,0.00028216,0.49044785,0.00008568722,0.00015874922,0.0002272603,0.0015057871,0.46859416,0.02371998,0.0022367667,0.012626612,0.00007192709],"about_ca_topic_score_codex":0.0026941935,"about_ca_topic_score_gemma":0.0032060107,"teacher_disagreement_score":0.0036388398,"about_ca_system_score_codex":0.00053564215,"about_ca_system_score_gemma":0.0003256481,"threshold_uncertainty_score":0.010746062},"labels":[],"label_agreement":null},{"id":"W2063596712","doi":"10.1111/j.1467-8640.2006.00277.x","title":"SENTIMENT CLASSIFICATION of MOVIE REVIEWS USING CONTEXTUAL VALENCE SHIFTERS","year":2006,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":750,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bigram; Valence (chemistry); Negation; Natural language processing; Computer science; Artificial intelligence; Term (time); Sentiment analysis; Support vector machine; Pattern recognition (psychology); Physics; Trigram","score_opus":0.09465421034018377,"score_gpt":0.3375719904772647,"score_spread":0.24291778013708093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063596712","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8923436,0.0018530589,0.09623964,0.00023945981,0.0002887016,0.00030183012,0.0014130947,0.0014502291,0.0058703087],"genre_scores_gemma":[0.93748254,0.00027302813,0.05949343,0.000051795312,0.00020033734,0.00014540256,0.0012671187,0.000052210307,0.001034129],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990803,0.0001800886,0.00009981182,0.00018643463,0.0003499235,0.00010344409],"domain_scores_gemma":[0.9978649,0.0006745898,0.0003684662,0.00013829295,0.000861726,0.00009196759],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009841261,0.00057677197,0.00077617355,0.0030581262,0.0002924312,0.00081569236,0.0003181821,0.000391764,0.001297093],"category_scores_gemma":[0.0034751184,0.00016350212,0.00065946154,0.0014103135,0.00022907909,0.0005502528,0.0003688343,0.00048344547,0.0005932138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023298538,0.00043761465,0.08495704,0.00049078284,0.00054023875,0.0002888178,0.00045698724,0.014920684,0.07740962,0.0012184293,0.005026393,0.8119235],"study_design_scores_gemma":[0.0001657265,0.00089413824,0.16716802,0.000093873925,0.00035492206,0.0005197907,0.00041574845,0.7770928,0.044772208,0.0026470227,0.0057243127,0.00015142387],"about_ca_topic_score_codex":0.0013077148,"about_ca_topic_score_gemma":0.0019129971,"teacher_disagreement_score":0.0030581262,"about_ca_system_score_codex":0.00040582477,"about_ca_system_score_gemma":0.00028061148,"threshold_uncertainty_score":0.0052046776},"labels":[],"label_agreement":null},{"id":"W2066180975","doi":"10.1145/2664551.2664579","title":"Sentiment Analysis for Streams of Web Data","year":2014,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Sentiment analysis; Data stream mining; Lexicon; Variety (cybernetics); Data science; Feature selection; Social media; Web mining; World Wide Web; Data mining; Artificial intelligence; Web service","score_opus":0.05226352041101885,"score_gpt":0.3068885047244378,"score_spread":0.25462498431341896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066180975","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15144533,0.0017397655,0.82773423,0.0012099703,0.00061558426,0.0008591864,0.0072571775,0.0037721696,0.005366572],"genre_scores_gemma":[0.57261485,0.0015898588,0.40660766,0.00024535335,0.00075633934,0.00084017304,0.014032832,0.00029822174,0.0030147696],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840516,0.00046142668,0.000193628,0.00022392116,0.0005765876,0.00013934271],"domain_scores_gemma":[0.9964563,0.0017510245,0.00039848132,0.00022343337,0.0010826538,0.00008799651],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023664404,0.0007495261,0.00071896205,0.0031128502,0.00045226346,0.0016549944,0.00041159298,0.00046016526,0.001691974],"category_scores_gemma":[0.009242592,0.00022601527,0.0009385635,0.0019781797,0.00021628241,0.0016739531,0.0006339405,0.00076702365,0.0011117688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011279641,0.00044315256,0.024589758,0.00084093853,0.00041576845,0.0010765275,0.00072875683,0.047956806,0.044703066,0.01410697,0.022485107,0.84152514],"study_design_scores_gemma":[0.00003961011,0.00013601057,0.0130491415,0.00007010926,0.000063117375,0.00025841058,0.00046691275,0.94661736,0.0105096465,0.015801987,0.012945888,0.000041813277],"about_ca_topic_score_codex":0.0014761314,"about_ca_topic_score_gemma":0.0015707375,"teacher_disagreement_score":0.0031128502,"about_ca_system_score_codex":0.0006025525,"about_ca_system_score_gemma":0.00050830643,"threshold_uncertainty_score":0.012515068},"labels":[],"label_agreement":null},{"id":"W2066346333","doi":"10.1145/2602044.2602050","title":"Is the grass greener?","year":2014,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Engineering and Physical Sciences Research Council","keywords":"Product (mathematics); Perception; Order (exchange); Space (punctuation); Computer science; Field (mathematics); Marketing; Business; Internet privacy; Computer security; Mathematics; Psychology; Finance","score_opus":0.021432422332642392,"score_gpt":0.2533252945117079,"score_spread":0.23189287217906548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066346333","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12849616,0.022244371,0.01473785,0.22559252,0.018539663,0.00021566963,0.0040287115,0.0036294567,0.5825156],"genre_scores_gemma":[0.5619331,0.02416981,0.023635887,0.062943,0.0037925742,0.00010440102,0.0036532742,0.0024009005,0.3173671],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990773,0.00021186256,0.00003492378,0.00021007756,0.00034262918,0.00012317323],"domain_scores_gemma":[0.9987826,0.00027088917,0.00014700303,0.00009039466,0.00043204732,0.00027697135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012656889,0.00052141905,0.0004504669,0.00073067634,0.002174004,0.0037455559,0.00052425446,0.001259191,0.039815236],"category_scores_gemma":[0.003886444,0.00016449213,0.00033799798,0.0009969524,0.0015451602,0.004741868,0.0013013973,0.0014512395,0.0147601245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022921847,0.00006776907,0.019689018,0.00096247776,0.00006703672,0.00095396367,0.012408938,0.0002170678,0.0048660403,0.016712869,0.5476751,0.39615044],"study_design_scores_gemma":[0.000008169448,0.00003993303,0.010780552,0.00028194196,0.000024884219,0.00037426545,0.014811361,0.00035149197,0.00065831974,0.007388659,0.9652444,0.0000361852],"about_ca_topic_score_codex":0.008715536,"about_ca_topic_score_gemma":0.024664454,"teacher_disagreement_score":0.039815236,"about_ca_system_score_codex":0.0012693058,"about_ca_system_score_gemma":0.0012529048,"threshold_uncertainty_score":0.13319528},"labels":[],"label_agreement":null},{"id":"W2073988376","doi":"10.1109/socialcom.2013.44","title":"A Structure for Opinion in Social Domains","year":2013,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Sentiment analysis; Computer science; Natural language processing; Polarity (international relations); Sentence; Artificial intelligence; Subjectivity; Field (mathematics); Product (mathematics); Verb; Linguistics; Information retrieval; Mathematics","score_opus":0.023464073491040943,"score_gpt":0.29145436787952483,"score_spread":0.2679902943884839,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073988376","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012718381,0.00030786247,0.9755141,0.0009800874,0.00012820467,0.000439407,0.0013634249,0.00081987045,0.0077286055],"genre_scores_gemma":[0.16820775,0.0005014136,0.82184243,0.000384299,0.00036482094,0.0009076674,0.0030111065,0.00013927603,0.0046411916],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99816126,0.00060399633,0.00023273635,0.00045597076,0.00044830568,0.00009774015],"domain_scores_gemma":[0.9969013,0.0011789714,0.0003667665,0.00048296075,0.00092220394,0.00014789552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015779164,0.0005646319,0.00056259194,0.0027525744,0.0016198285,0.0021468326,0.00071998365,0.0010538142,0.0040809433],"category_scores_gemma":[0.0075185252,0.00039816237,0.0014553249,0.0024488114,0.001521318,0.005371771,0.0016022414,0.0014971509,0.0024488403],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001562896,0.00014817977,0.0043049054,0.0003490535,0.000071089424,0.00043119973,0.0021324712,0.0071389484,0.010296618,0.64331293,0.01630176,0.31535652],"study_design_scores_gemma":[0.000055599874,0.00024008508,0.003128442,0.00018947388,0.00009493588,0.0006276809,0.000813675,0.19433887,0.006367655,0.695157,0.09892355,0.00006311127],"about_ca_topic_score_codex":0.0013288207,"about_ca_topic_score_gemma":0.0017453757,"teacher_disagreement_score":0.0040809433,"about_ca_system_score_codex":0.0010451035,"about_ca_system_score_gemma":0.0009526504,"threshold_uncertainty_score":0.013652146},"labels":[],"label_agreement":null},{"id":"W2077683701","doi":"10.1075/lic.14.1.07tab","title":"Loving and hating the movies in English, German and Spanish","year":2014,"lang":"en","type":"article","venue":"Languages in Contrast","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Simon Fraser University","funders":"","keywords":"Argumentative; German; Linguistics; Style (visual arts); Sociocultural evolution; Psychology; Sociology; Literature; Art; Philosophy","score_opus":0.005761920735122669,"score_gpt":0.2523728581758457,"score_spread":0.24661093744072304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077683701","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9944885,0.00027074842,0.00018079819,0.0000539142,0.0000062764398,0.000019197647,0.0007288294,0.0000038027133,0.004247877],"genre_scores_gemma":[0.9959292,0.0002966132,0.0004570721,0.000046460056,0.000022380073,0.0000715528,0.0013855043,0.000009838075,0.0017812933],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998662,0.0006151483,0.00012343981,0.00011924408,0.00033696543,0.0001432174],"domain_scores_gemma":[0.984435,0.0097798705,0.002365463,0.00017985952,0.0029036829,0.00033608946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016471094,0.0002210618,0.00024186174,0.002627637,0.00035387135,0.0011580862,0.00016937683,0.0002321187,0.0018233332],"category_scores_gemma":[0.008027372,0.00007887052,0.00013585926,0.002490971,0.0004896844,0.0006409495,0.0005013351,0.00025754387,0.0003626803],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011107131,0.0004587846,0.7778266,0.0019707934,0.0001706392,0.0011298425,0.124634586,0.0004329355,0.01764176,0.0026107337,0.0058520506,0.06616051],"study_design_scores_gemma":[0.000007561694,0.00014236118,0.9501498,0.0000737721,0.000020945436,0.00032351376,0.0407044,0.00051064417,0.0011206965,0.00015065074,0.0067788186,0.000016943424],"about_ca_topic_score_codex":0.004775841,"about_ca_topic_score_gemma":0.008584377,"teacher_disagreement_score":0.004775841,"about_ca_system_score_codex":0.0005481437,"about_ca_system_score_gemma":0.0002595643,"threshold_uncertainty_score":0.009496093},"labels":[],"label_agreement":null},{"id":"W2083400649","doi":"10.1016/j.csl.2013.04.009","title":"Prior and contextual emotion of words in sentential context","year":2013,"lang":"en","type":"article","venue":"Computer Speech & Language","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Sentence; Context (archaeology); Natural language processing; Set (abstract data type); Word (group theory); Feature (linguistics); Focus (optics); Task (project management); Similarity (geometry); Artificial intelligence; Contrast (vision); Affect (linguistics); Function (biology); Linguistics; Image (mathematics)","score_opus":0.009483769740171108,"score_gpt":0.23875473576759712,"score_spread":0.22927096602742603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083400649","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.853341,0.001757172,0.10293531,0.0009336685,0.0005216326,0.000115208764,0.0010542183,0.0003412377,0.039000608],"genre_scores_gemma":[0.99132884,0.00024965196,0.0055492613,0.000043331984,0.00020118477,0.000029246135,0.00035880986,0.000042462027,0.0021971816],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99922776,0.00019363956,0.000065921064,0.00021536004,0.00019015482,0.000107263644],"domain_scores_gemma":[0.997414,0.001171502,0.00027698543,0.00024067641,0.00066813955,0.00022862971],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084911095,0.0003607833,0.0003353706,0.0012311555,0.00067325594,0.0019929325,0.00024249095,0.00053102,0.004652762],"category_scores_gemma":[0.005569848,0.00021435998,0.00040145635,0.0008382377,0.00087323954,0.0036504152,0.000900348,0.0011407422,0.0010658823],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005349018,0.000853456,0.087586276,0.001084805,0.0004102737,0.0026456832,0.015608605,0.009958797,0.35453516,0.12613551,0.009407692,0.38642478],"study_design_scores_gemma":[0.00014268397,0.0021461819,0.51764715,0.00058059784,0.001089214,0.0032693702,0.013281182,0.19551374,0.06768957,0.14046308,0.057802584,0.00037465236],"about_ca_topic_score_codex":0.0014132371,"about_ca_topic_score_gemma":0.0023532226,"teacher_disagreement_score":0.004652762,"about_ca_system_score_codex":0.0005835679,"about_ca_system_score_gemma":0.00032841924,"threshold_uncertainty_score":0.015565038},"labels":[],"label_agreement":null},{"id":"W2083633991","doi":"10.5539/cis.v2n4p64","title":"Reader Perspective Emotion Analysis in Text through Ensemble based Multi-Label Classification Framework","year":2009,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Sadness; Feature (linguistics); Artificial intelligence; Feature selection; Emotion classification; Natural language processing; Frame (networking); Semantic feature; Sentiment analysis; Pattern recognition (psychology); Word (group theory); Polarity (international relations); Perspective (graphical); Speech recognition; Anger; Linguistics; Psychology","score_opus":0.0485574798030812,"score_gpt":0.32790344913722136,"score_spread":0.27934596933414013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083633991","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15031148,0.0006959112,0.8437372,0.00043360677,0.00015982451,0.00011352596,0.00035233126,0.0018628185,0.0023333493],"genre_scores_gemma":[0.77135605,0.0002903892,0.2239702,0.00009091845,0.00027209998,0.00015765852,0.0010888526,0.0000958124,0.0026780255],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991233,0.00030253135,0.00006217995,0.00016043158,0.00025168597,0.00009987315],"domain_scores_gemma":[0.9980281,0.0008097676,0.0001866951,0.000159699,0.00074398395,0.000071750736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017293023,0.00068784325,0.0009916297,0.0019762218,0.00042144067,0.0011109525,0.00074184977,0.0007070603,0.0010867888],"category_scores_gemma":[0.0034916238,0.00017365174,0.0008104063,0.00088593824,0.00020470432,0.0016233488,0.00056948274,0.0008533566,0.00065929914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005626739,0.00044036438,0.013575929,0.00012809067,0.00030768348,0.0002431012,0.00047696324,0.040890567,0.026592815,0.0019435766,0.0045170137,0.9103213],"study_design_scores_gemma":[0.000011437044,0.00010063337,0.004456789,0.0000118487305,0.00008927577,0.00007205721,0.00012907996,0.9842098,0.0073514935,0.0025484865,0.0009958126,0.00002329657],"about_ca_topic_score_codex":0.001475232,"about_ca_topic_score_gemma":0.0020307088,"teacher_disagreement_score":0.0019762218,"about_ca_system_score_codex":0.00039230645,"about_ca_system_score_gemma":0.00026926136,"threshold_uncertainty_score":0.009145558},"labels":[],"label_agreement":null},{"id":"W2084046180","doi":"10.1162/coli_a_00049","title":"Lexicon-Based Methods for Sentiment Analysis","year":2011,"lang":"en","type":"article","venue":"Computational Linguistics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3255,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of Toronto; Simon Fraser University","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Sentiment analysis; Lexicon; Polarity (international relations); Natural language processing; Artificial intelligence; Negation; Consistency (knowledge bases); Orientation (vector space); Word (group theory); Process (computing); Reliability (semiconductor); Linguistics; Mathematics","score_opus":0.08804081832030412,"score_gpt":0.3846883079456333,"score_spread":0.29664748962532916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084046180","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012882927,0.0014313121,0.98315823,0.00038878573,0.00031007818,0.000406241,0.0021492587,0.0030226517,0.007845165],"genre_scores_gemma":[0.045336884,0.0022307371,0.93637496,0.00039025216,0.0005900786,0.0012928011,0.007213166,0.000841814,0.005729201],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9958007,0.0015425266,0.0004785838,0.00063625077,0.0014078915,0.00013401979],"domain_scores_gemma":[0.99447435,0.0029821282,0.00048461577,0.0007069867,0.001249119,0.00010278383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030452975,0.0020717187,0.0015420294,0.00900719,0.0014191739,0.0053936513,0.0018230806,0.0013767272,0.012928959],"category_scores_gemma":[0.01324147,0.00086831144,0.0019707284,0.007577281,0.0010170842,0.0037888756,0.0022795778,0.0023612832,0.014274262],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001124247,0.00016799835,0.0018883673,0.0014215712,0.00043687847,0.00025717905,0.0006165473,0.008685959,0.010404882,0.103362426,0.06512037,0.8075254],"study_design_scores_gemma":[0.00011854346,0.00009532741,0.0032335354,0.00053438,0.00024625778,0.0006830376,0.00077222905,0.36462542,0.009748445,0.38001645,0.23973009,0.00019620186],"about_ca_topic_score_codex":0.0018193896,"about_ca_topic_score_gemma":0.002607475,"teacher_disagreement_score":0.012928959,"about_ca_system_score_codex":0.0011462938,"about_ca_system_score_gemma":0.0016088362,"threshold_uncertainty_score":0.043251693},"labels":[],"label_agreement":null},{"id":"W2087827621","doi":"10.1109/ccece.2014.6900951","title":"A sentiment analysis prototype system for social network data","year":2014,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Sentiment analysis; Computer science; Variety (cybernetics); Data science; Feeling; Social network (sociolinguistics); Mechanism (biology); Public opinion; Social media; World Wide Web; Human–computer interaction; Artificial intelligence; Psychology; Political science","score_opus":0.05256596163253666,"score_gpt":0.30804850497060254,"score_spread":0.25548254333806586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087827621","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04484297,0.0004848876,0.57675487,0.0017992381,0.0011704896,0.0064440514,0.06604917,0.2720508,0.030403517],"genre_scores_gemma":[0.117966615,0.00048955804,0.7491168,0.00096352136,0.00038380583,0.007627539,0.0808664,0.007904762,0.03468103],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989429,0.00021259663,0.00015133481,0.0002143636,0.00041407155,0.00006472408],"domain_scores_gemma":[0.9979044,0.0006103991,0.00016403683,0.00031465845,0.0008946875,0.00011179344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023872922,0.0010433646,0.00067723973,0.0021553289,0.0009900394,0.0013636163,0.00091635826,0.00062276714,0.017659107],"category_scores_gemma":[0.006259898,0.0005766384,0.0006919245,0.0013634689,0.00023451631,0.0020956486,0.0011513914,0.0010847357,0.011201969],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011775678,0.0006033564,0.013087735,0.0013948008,0.00037741257,0.0009549398,0.0016090386,0.0019460291,0.12577361,0.007133794,0.48129335,0.3646484],"study_design_scores_gemma":[0.0007703126,0.00059685216,0.033266567,0.00024121691,0.00031735527,0.0013344418,0.0009938774,0.20348924,0.09861704,0.014147668,0.6459682,0.0002572368],"about_ca_topic_score_codex":0.0022472623,"about_ca_topic_score_gemma":0.0034006273,"teacher_disagreement_score":0.017659107,"about_ca_system_score_codex":0.0005362792,"about_ca_system_score_gemma":0.0008219531,"threshold_uncertainty_score":0.059075594},"labels":[],"label_agreement":null},{"id":"W2089752704","doi":"10.1017/s1351324910000264","title":"Subjectivity detection in spoken and written conversations","year":2010,"lang":"en","type":"article","venue":"Natural Language Engineering","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Conversation; Set (abstract data type); Subjectivity; Natural language processing; Artificial intelligence; Domain (mathematical analysis); Speech recognition; Linguistics","score_opus":0.0024433274761154348,"score_gpt":0.20363026364576112,"score_spread":0.20118693616964567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089752704","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9308406,0.00045556587,0.0595058,0.00022151662,0.000110615336,0.00018729971,0.00085004803,0.0009259195,0.006902582],"genre_scores_gemma":[0.979815,0.00014042175,0.017580839,0.000054314834,0.00008910652,0.00007944891,0.0007097833,0.00006454933,0.0014664215],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977198,0.001128231,0.00014447402,0.00041467632,0.00040153018,0.00019117788],"domain_scores_gemma":[0.98717016,0.008338367,0.0015094599,0.0006451843,0.0018379168,0.0004989072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018830758,0.0005442384,0.00061248423,0.0019630857,0.00048005368,0.0016746187,0.0004079836,0.00062627206,0.0017523441],"category_scores_gemma":[0.012242606,0.00023860768,0.00042855943,0.0006342608,0.00038367874,0.0017208076,0.0013602284,0.000666285,0.0010358167],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003731202,0.00046732792,0.1683859,0.0010067945,0.00035210894,0.0009166583,0.006668733,0.0068836175,0.21739371,0.0015813542,0.0043892185,0.58822334],"study_design_scores_gemma":[0.00013394309,0.001170542,0.42909873,0.00026863898,0.00030153323,0.0016408195,0.008980107,0.37087902,0.16555476,0.00974896,0.011926938,0.00029603855],"about_ca_topic_score_codex":0.0010300669,"about_ca_topic_score_gemma":0.0012600559,"teacher_disagreement_score":0.0019630857,"about_ca_system_score_codex":0.0003176414,"about_ca_system_score_gemma":0.0003263499,"threshold_uncertainty_score":0.009958744},"labels":[],"label_agreement":null},{"id":"W2090250652","doi":"10.1111/j.1467-8640.2012.00458.x","title":"A BOOTSTRAPPING METHOD FOR EXTRACTING PARAPHRASES OF EMOTION EXPRESSIONS FROM TEXTS","year":2012,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Bootstrapping (finance); WordNet; Computer science; Natural language processing; Artificial intelligence; Mathematics","score_opus":0.09356020941455963,"score_gpt":0.3895464476941189,"score_spread":0.2959862382795593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090250652","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020704146,0.00040414455,0.97209364,0.00018873191,0.000066802655,0.00045524098,0.0010145243,0.0027141846,0.0023586103],"genre_scores_gemma":[0.08962989,0.0002949372,0.9037564,0.00009333163,0.00007578602,0.00061943324,0.0030401568,0.0002676156,0.0022224747],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867636,0.00043883125,0.00015063131,0.00030619613,0.00037427203,0.000053745523],"domain_scores_gemma":[0.99538535,0.002249888,0.00036635553,0.00080964883,0.0011131578,0.00007562091],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001357062,0.0009832882,0.0006244467,0.0028012826,0.00079869526,0.00086464814,0.0009333983,0.0007870289,0.0039950036],"category_scores_gemma":[0.009972679,0.0004374464,0.000730691,0.0025636007,0.00060744106,0.001934924,0.0009311705,0.0011730157,0.0028597359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021345647,0.00019505735,0.0014244328,0.0006057735,0.000118982854,0.0007449994,0.0014678927,0.0029736937,0.10134032,0.005917755,0.011524787,0.87347275],"study_design_scores_gemma":[0.00025659997,0.001052112,0.02069502,0.0004135845,0.00048317155,0.0077295504,0.002458423,0.55868274,0.2284453,0.06421298,0.11525365,0.00031687526],"about_ca_topic_score_codex":0.000703436,"about_ca_topic_score_gemma":0.0012954848,"teacher_disagreement_score":0.0039950036,"about_ca_system_score_codex":0.0002960339,"about_ca_system_score_gemma":0.00057199376,"threshold_uncertainty_score":0.013364613},"labels":[],"label_agreement":null},{"id":"W2093927111","doi":"10.1109/socialinformatics.2012.49","title":"Sentiment Analysis of Social Issues","year":2012,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Sentiment analysis; Computer science; Natural language processing; Artificial intelligence; Statistical analysis; Data science; Social media; Verb; Feature (linguistics); Computational linguistics; Information retrieval; Linguistics; World Wide Web","score_opus":0.026192868622842324,"score_gpt":0.31590914224877414,"score_spread":0.28971627362593183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093927111","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6846107,0.005408103,0.18842123,0.003969109,0.001389626,0.0021719271,0.0106123695,0.0007538945,0.10266304],"genre_scores_gemma":[0.9301792,0.0017798065,0.05582151,0.00040140928,0.00057772436,0.00061205856,0.0039198375,0.00008250547,0.0066258726],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99670666,0.0013037053,0.00032244634,0.00031707843,0.0011895497,0.00016053743],"domain_scores_gemma":[0.99378467,0.0023963915,0.0010227325,0.00022789578,0.0024273766,0.00014084627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027977098,0.0005165952,0.00056986493,0.0038319754,0.000731557,0.0020429427,0.0002712053,0.0003583596,0.0036634959],"category_scores_gemma":[0.010410447,0.000119678545,0.00063410006,0.0031297556,0.00043624622,0.0012673386,0.0005944621,0.00050887896,0.0010462811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007887039,0.00037518016,0.09215333,0.0027041247,0.0007400129,0.00090290414,0.0070778136,0.004994108,0.06999712,0.035593983,0.03937083,0.74530184],"study_design_scores_gemma":[0.00016579432,0.0013347834,0.4103424,0.0011227714,0.0010389822,0.002010642,0.023201635,0.14397229,0.05484886,0.09079981,0.27084684,0.00031518494],"about_ca_topic_score_codex":0.0008667083,"about_ca_topic_score_gemma":0.0009366624,"teacher_disagreement_score":0.0038319754,"about_ca_system_score_codex":0.0007891825,"about_ca_system_score_gemma":0.0005924287,"threshold_uncertainty_score":0.014795899},"labels":[],"label_agreement":null},{"id":"W2103251029","doi":"","title":"Prior versus Contextual Emotion of a Word in a Sentence","year":2012,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Sentence; Computer science; Natural language processing; Word (group theory); Task (project management); Context (archaeology); Focus (optics); Artificial intelligence; Representation (politics); Set (abstract data type); Affect (linguistics); Function (biology); Interpretation (philosophy); Linguistics; Psychology; Communication","score_opus":0.045738315487440624,"score_gpt":0.29333977947918854,"score_spread":0.24760146399174793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103251029","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83589053,0.0019075646,0.12236109,0.0013762446,0.0006754534,0.000213572,0.0025226723,0.0007536219,0.03429923],"genre_scores_gemma":[0.9620793,0.00037240423,0.034048196,0.00019084843,0.00028613728,0.000084089384,0.0010535014,0.000090288595,0.0017952658],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99926704,0.00026517495,0.00009367479,0.00016816481,0.00014235612,0.00006366512],"domain_scores_gemma":[0.9977028,0.001307122,0.00030320007,0.00018751183,0.00038950486,0.00010989433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008717307,0.00035728098,0.00037996983,0.001042383,0.00038506236,0.0015965186,0.00025443424,0.0006832897,0.0031826713],"category_scores_gemma":[0.0041934713,0.00011325878,0.00032114403,0.0006668713,0.0008999514,0.0018003774,0.0006229162,0.0006145876,0.0009925389],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004128951,0.0003426824,0.07862598,0.0013760001,0.0003841966,0.0012191574,0.005516736,0.002761034,0.4230006,0.03903277,0.010256916,0.43335506],"study_design_scores_gemma":[0.00014603199,0.0028954416,0.6085744,0.000961092,0.001323274,0.0062496047,0.007213148,0.12692882,0.08855044,0.08290474,0.073760055,0.00049300276],"about_ca_topic_score_codex":0.00033419477,"about_ca_topic_score_gemma":0.00070002564,"teacher_disagreement_score":0.0031826713,"about_ca_system_score_codex":0.00032621986,"about_ca_system_score_gemma":0.000134317,"threshold_uncertainty_score":0.010647118},"labels":[],"label_agreement":null},{"id":"W2105147223","doi":"10.5539/cis.v6n3p80","title":"A Novel Approach for Dynamic Polarity Mining from Customer Reviews","year":2013,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Ministry of Education, India","keywords":"Polarity (international relations); Computer science; Sentiment analysis; Feature (linguistics); Artificial intelligence; Voting; Word (group theory); Natural language processing; Data mining; Linguistics","score_opus":0.02934001019220665,"score_gpt":0.27225433017676404,"score_spread":0.2429143199845574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105147223","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047094215,0.0014362187,0.9401374,0.00048718514,0.00034582696,0.0009780109,0.0017483132,0.0021001436,0.00567269],"genre_scores_gemma":[0.28976163,0.000929894,0.69839436,0.0002515171,0.0004809806,0.0009464944,0.003797944,0.0001770472,0.005260228],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99828917,0.00031329042,0.00018390153,0.00041837498,0.000684468,0.00011081246],"domain_scores_gemma":[0.99733967,0.0007168145,0.00026993267,0.00017900499,0.0014227038,0.0000720081],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010554066,0.0009916187,0.00105169,0.00496706,0.0007009485,0.0015202812,0.0011074044,0.0006630029,0.001389608],"category_scores_gemma":[0.004640163,0.00038186173,0.0009767732,0.0037813901,0.00029786787,0.0014904204,0.00095185474,0.0007654393,0.0015614191],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019700946,0.0001840273,0.0061729606,0.00053362927,0.00017952616,0.0004131026,0.00045862808,0.0027672772,0.046968322,0.003443436,0.010741658,0.92794037],"study_design_scores_gemma":[0.000128038,0.0004521437,0.024745828,0.00014185063,0.0004775309,0.0028788433,0.0013626202,0.8310009,0.06041688,0.017805407,0.060398508,0.00019140223],"about_ca_topic_score_codex":0.0019431461,"about_ca_topic_score_gemma":0.0036870628,"teacher_disagreement_score":0.00496706,"about_ca_system_score_codex":0.00040371934,"about_ca_system_score_gemma":0.0009070353,"threshold_uncertainty_score":0.0055815578},"labels":[],"label_agreement":null},{"id":"W2108850603","doi":"10.5555/2457524.2457702","title":"Verb Oriented Sentiment Classification","year":2012,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Sentiment analysis; Computer science; Natural language processing; Verb; Sentence; Artificial intelligence; Noun; Feature (linguistics); Computational linguistics; Linguistics","score_opus":0.03731191019271947,"score_gpt":0.28426843620107006,"score_spread":0.2469565260083506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108850603","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27093792,0.0037705882,0.5985625,0.001893738,0.0028047953,0.0055731977,0.018890822,0.0064746323,0.09109188],"genre_scores_gemma":[0.5608195,0.0021854748,0.37373924,0.0008691414,0.001166954,0.0022976354,0.030992834,0.0003536735,0.027575536],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989322,0.00021090553,0.0001270532,0.00017066332,0.0004464417,0.000112754664],"domain_scores_gemma":[0.9987244,0.00028578297,0.00014618335,0.00008030226,0.00071447785,0.00004888019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011896748,0.00089007954,0.00066807773,0.0031990313,0.0006905962,0.0017619821,0.00058589614,0.00067063904,0.007374603],"category_scores_gemma":[0.0029477444,0.00015663203,0.0009717275,0.0022428678,0.00023354679,0.0011285665,0.0005564043,0.0006371244,0.0047801356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039554862,0.0005565436,0.013079043,0.0006439879,0.000172722,0.00029206593,0.00033750827,0.0020753827,0.043985095,0.0070552006,0.041561864,0.889845],"study_design_scores_gemma":[0.00036380457,0.0014404911,0.10032167,0.0007008589,0.0007657116,0.0019380173,0.002348452,0.47734213,0.09052426,0.05384275,0.27017555,0.00023638384],"about_ca_topic_score_codex":0.000978765,"about_ca_topic_score_gemma":0.001111781,"teacher_disagreement_score":0.007374603,"about_ca_system_score_codex":0.0005594675,"about_ca_system_score_gemma":0.0005555069,"threshold_uncertainty_score":0.024670541},"labels":[],"label_agreement":null},{"id":"W2110003440","doi":"10.3115/v1/s14-2105","title":"Swiss-Chocolate: Sentiment Detection using Sparse SVMs and Part-Of-Speech n-Grams","year":2014,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Support vector machine; Classifier (UML); Sentiment analysis; Artificial intelligence; Social media; Regularization (linguistics); SemEval; Natural language processing; Machine learning; Pattern recognition (psychology); Speech recognition; Task (project management); World Wide Web","score_opus":0.028610564678444472,"score_gpt":0.26310640585329487,"score_spread":0.2344958411748504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110003440","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26704928,0.0013949752,0.6846129,0.0014983286,0.0007896081,0.0004130795,0.00910536,0.024264652,0.010871763],"genre_scores_gemma":[0.6137066,0.00042349138,0.3428528,0.00045953903,0.0004272973,0.00035538105,0.024242854,0.0006534937,0.016878508],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996395,0.00007620958,0.00002330089,0.000098085446,0.000102111844,0.000060643506],"domain_scores_gemma":[0.9995023,0.00013373414,0.000048459133,0.00008729076,0.00019118513,0.00003695535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000593179,0.0009381804,0.0006431708,0.0008611784,0.00062517,0.00084186543,0.00066312385,0.0011056626,0.0047152396],"category_scores_gemma":[0.0018966772,0.00027536313,0.0006282096,0.0006908755,0.00020382888,0.0010085606,0.0007771209,0.00083518017,0.005520297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001198531,0.00057658175,0.012638119,0.00031424893,0.00027819604,0.0004397406,0.00019924821,0.027016617,0.09249673,0.0025687579,0.07144519,0.7908281],"study_design_scores_gemma":[0.00006346222,0.00021777503,0.0051784473,0.000019564513,0.00004162156,0.00015438748,0.0000769281,0.95463806,0.023642302,0.0028453511,0.013089258,0.000032731976],"about_ca_topic_score_codex":0.0031521628,"about_ca_topic_score_gemma":0.00676331,"teacher_disagreement_score":0.0047152396,"about_ca_system_score_codex":0.00028004273,"about_ca_system_score_gemma":0.0004791138,"threshold_uncertainty_score":0.015774012},"labels":[],"label_agreement":null},{"id":"W2115639608","doi":"10.1109/icmlc.2009.5212278","title":"Improving sentiment analysis with Part-of-Speech weighting","year":2009,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Weighting; Sentiment analysis; Computer science; Term (time); Artificial intelligence; Natural language processing; Scheme (mathematics); Product (mathematics); Speech recognition; Machine learning; Mathematics","score_opus":0.011792376006747126,"score_gpt":0.2389350352160614,"score_spread":0.22714265920931428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115639608","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06474723,0.0008065192,0.93072826,0.0001922879,0.0002926766,0.00016949663,0.00013339109,0.0012708412,0.0016593011],"genre_scores_gemma":[0.37505502,0.0006929151,0.61825746,0.0002508005,0.00033723027,0.00025436285,0.000786788,0.0004159551,0.003949527],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977639,0.00079492194,0.00018127087,0.0002560228,0.00089433795,0.00010956618],"domain_scores_gemma":[0.99119204,0.0038427515,0.00057772634,0.00088622095,0.0033548255,0.0001464846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032989853,0.0013323702,0.0013721786,0.002399776,0.00056960405,0.0012960173,0.0008249493,0.0011095934,0.0015558144],"category_scores_gemma":[0.014921144,0.00030395304,0.0010379041,0.0019572787,0.00037647958,0.0023867858,0.0010230347,0.0012413599,0.001602542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000505009,0.00033468212,0.0035307498,0.00031607508,0.00024604736,0.00012554364,0.00026689318,0.025606407,0.1808175,0.0030582668,0.004484874,0.7807079],"study_design_scores_gemma":[0.00006733384,0.00034146896,0.0037657516,0.000037961785,0.00030825665,0.00021163312,0.00013319164,0.88956165,0.09024788,0.009284949,0.005945618,0.00009433297],"about_ca_topic_score_codex":0.0010544348,"about_ca_topic_score_gemma":0.0015883498,"teacher_disagreement_score":0.0032989853,"about_ca_system_score_codex":0.0003634054,"about_ca_system_score_gemma":0.00044146413,"threshold_uncertainty_score":0.017446876},"labels":[],"label_agreement":null},{"id":"W2116401198","doi":"10.1109/icdm.2008.94","title":"Modeling and Predicting the Helpfulness of Online Reviews","year":2008,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":324,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Helpfulness; Computer science; Quality (philosophy); Data science; Resource (disambiguation); Psychology","score_opus":0.07223397040685091,"score_gpt":0.28718244489899114,"score_spread":0.21494847449214022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116401198","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8237115,0.0014488095,0.17047486,0.0010267441,0.00006585193,0.00015417907,0.00079066976,0.0005086103,0.0018187461],"genre_scores_gemma":[0.9636944,0.00046384986,0.033817045,0.000047784273,0.00014350309,0.00007304516,0.0006436737,0.000025010333,0.0010916853],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989361,0.0004690886,0.000095859534,0.0002002361,0.00022582132,0.00007290015],"domain_scores_gemma":[0.9834932,0.011927428,0.002320018,0.00034061345,0.0017059802,0.00021281673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032134526,0.0007500395,0.00066649204,0.001939242,0.00023726647,0.00097396824,0.00061179843,0.00091729866,0.00045467462],"category_scores_gemma":[0.016921988,0.00033737405,0.00042964425,0.0011036547,0.00026547417,0.0010973315,0.00030224782,0.00086394034,0.00033515613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005476446,0.00059835735,0.1701107,0.00044096104,0.00032078734,0.00058933714,0.00058855925,0.5670464,0.010058207,0.004783338,0.005795208,0.23912044],"study_design_scores_gemma":[0.000005137541,0.00003329973,0.0058185253,0.000005511099,0.000014565108,0.000040872466,0.000018752833,0.99250484,0.0005070496,0.00082086213,0.00022395742,0.0000066559714],"about_ca_topic_score_codex":0.004380575,"about_ca_topic_score_gemma":0.005634597,"teacher_disagreement_score":0.004380575,"about_ca_system_score_codex":0.0005552065,"about_ca_system_score_gemma":0.00050608284,"threshold_uncertainty_score":0.016994536},"labels":[],"label_agreement":null},{"id":"W2116959421","doi":"10.1145/2684822.2685291","title":"FLAME","year":2015,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":136,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Sentiment analysis; Collaborative filtering; Probabilistic logic; Set (abstract data type); Task (project management); Topic model; Data science; Artificial intelligence; Information retrieval; Recommender system","score_opus":0.06463963709569835,"score_gpt":0.27403224508660357,"score_spread":0.2093926079909052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116959421","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019618977,0.003320975,0.44634667,0.003048831,0.0017702942,0.0012181856,0.065913476,0.2016199,0.2571427],"genre_scores_gemma":[0.17055777,0.0036087828,0.40469122,0.0017816339,0.00086087006,0.0012441154,0.17747039,0.011894039,0.22789124],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99895394,0.00014510786,0.000064224405,0.00031154449,0.00044610302,0.00007906958],"domain_scores_gemma":[0.9983432,0.00030105517,0.0001334362,0.00053301884,0.00057441194,0.00011478766],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001234585,0.0010054962,0.0005909306,0.0018713634,0.0005543214,0.0025029713,0.0016268898,0.001037866,0.05348809],"category_scores_gemma":[0.0059303213,0.0005242934,0.00089454657,0.0014066114,0.000254177,0.002759741,0.0013969211,0.0009223709,0.058487393],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041613998,0.00013050355,0.003959016,0.0006618602,0.00009749111,0.00018519186,0.00025992322,0.0046814503,0.0063824416,0.029460441,0.45208684,0.50167865],"study_design_scores_gemma":[0.000105843625,0.000112657915,0.0031486717,0.000107177206,0.000054329685,0.00040064153,0.00009169573,0.074211136,0.0077850404,0.027324507,0.886587,0.00007123096],"about_ca_topic_score_codex":0.0037136038,"about_ca_topic_score_gemma":0.004582846,"teacher_disagreement_score":0.9465119,"about_ca_system_score_codex":0.00070985913,"about_ca_system_score_gemma":0.0010849362,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2117808614","doi":"10.4137/bii.s8933","title":"Binary Classifiers and Latent Sequence Models for Emotion Detection in Suicide Notes","year":2012,"lang":"en","type":"article","venue":"Biomedical Informatics Insights","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Sentence; Computer science; Classifier (UML); Artificial intelligence; Task (project management); Margin (machine learning); Binary classification; Sequence (biology); Natural language processing; Sentiment analysis; Machine learning; Support vector machine","score_opus":0.09195606268752786,"score_gpt":0.304605320211808,"score_spread":0.2126492575242801,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117808614","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05315231,0.001096096,0.94052696,0.0012397857,0.00015342765,0.000109057655,0.00069473125,0.0011262908,0.0019013466],"genre_scores_gemma":[0.69892144,0.0008935559,0.2892873,0.0003932342,0.000402165,0.00045654867,0.0035352472,0.00018412701,0.0059263925],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866664,0.00063585164,0.00008763015,0.00028694674,0.00020559522,0.00011732754],"domain_scores_gemma":[0.9950814,0.003577006,0.0004587801,0.00024961194,0.0005214544,0.00011164848],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026179103,0.0007213885,0.00078565616,0.0014196485,0.00059340434,0.0012771958,0.0012254041,0.0011114411,0.0023916126],"category_scores_gemma":[0.009657689,0.00037033224,0.0009413007,0.0009919736,0.0005062027,0.002212982,0.0007293719,0.0024138775,0.0017744479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010747829,0.0007160247,0.014597941,0.0003698688,0.0001798314,0.0002811807,0.0006934914,0.29131517,0.011789199,0.049359582,0.014718974,0.61490387],"study_design_scores_gemma":[0.000012459791,0.000027110395,0.00075125037,0.0000143488605,0.000008520281,0.000016808346,0.000026005211,0.980565,0.00066531263,0.01720821,0.0006954662,0.000009527732],"about_ca_topic_score_codex":0.004543944,"about_ca_topic_score_gemma":0.0052832356,"teacher_disagreement_score":0.004543944,"about_ca_system_score_codex":0.0012447686,"about_ca_system_score_gemma":0.0007526517,"threshold_uncertainty_score":0.0138450265},"labels":[],"label_agreement":null},{"id":"W2120120501","doi":"10.5539/ass.v10n18p144","title":"Who Is Tweeting on #PRU13?","year":2014,"lang":"en","type":"article","venue":"Asian Social Science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Social media; Timeline; Microblogging; Opposition (politics); General election; Politics; Political science; Thematic analysis; Public relations; Media studies; Political communication; Advertising; Sociology; Qualitative research; Social science; Business; Law; History","score_opus":0.016652407581364024,"score_gpt":0.285070835397521,"score_spread":0.26841842781615693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120120501","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88724214,0.0027028043,0.0038320373,0.01618411,0.0018084924,0.0001332791,0.0121582765,0.00031580514,0.075623006],"genre_scores_gemma":[0.9703523,0.001691431,0.001315406,0.0016393895,0.0008357467,0.0000783955,0.0032425742,0.00010482986,0.020739896],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993136,0.00021193852,0.000062755294,0.00011259996,0.00016172943,0.00013736996],"domain_scores_gemma":[0.9972146,0.0010687765,0.00078931876,0.00011289019,0.0005321945,0.0002821441],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074277463,0.00019648742,0.00027677944,0.001105603,0.0009898589,0.0020018818,0.0001996446,0.0008009322,0.007914657],"category_scores_gemma":[0.005570696,0.0001621452,0.0002494063,0.0016436881,0.00030749524,0.0020769069,0.00046631414,0.0005824657,0.0058197086],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007667895,0.00009646418,0.61224,0.00091495365,0.00020907525,0.0012408019,0.01937549,0.00030806573,0.007433567,0.0056633763,0.12737504,0.22437637],"study_design_scores_gemma":[0.000018067974,0.00021203903,0.67402554,0.0004656722,0.00019549904,0.0023671803,0.041503135,0.003575805,0.0061836243,0.0021355937,0.26919836,0.00011948246],"about_ca_topic_score_codex":0.0042221826,"about_ca_topic_score_gemma":0.008355063,"teacher_disagreement_score":0.007914657,"about_ca_system_score_codex":0.0004170205,"about_ca_system_score_gemma":0.00024472232,"threshold_uncertainty_score":0.026477158},"labels":[],"label_agreement":null},{"id":"W2120158336","doi":"","title":"Cross Lingual Adaptation: An Experiment on Sentiment Classifications","year":2010,"lang":"en","type":"article","venue":"Meeting of the Association for Computational Linguistics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":87,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Machine translation; Natural language processing; Task (project management); Artificial intelligence; Representation (politics); Adaptation (eye); Key (lock); Sentiment analysis; Natural language; Translation (biology); Parallel corpora; Noise (video); Machine learning","score_opus":0.03508279994780869,"score_gpt":0.3290336885755691,"score_spread":0.2939508886277604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120158336","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9708086,0.00065400713,0.015990905,0.00038608967,0.00044940313,0.00052409933,0.0011032313,0.0026482174,0.007435437],"genre_scores_gemma":[0.94997513,0.0002993061,0.034721702,0.00073338783,0.00016363703,0.00055788044,0.005980571,0.0006424595,0.006926008],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.994584,0.0029752045,0.0004172167,0.0011147482,0.0007202533,0.00018854167],"domain_scores_gemma":[0.98784864,0.0062381006,0.00039860685,0.0031899866,0.001925685,0.00039897594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00595533,0.00091273896,0.0008915361,0.00078980613,0.00079651695,0.0007827425,0.0010108338,0.0009300165,0.002240641],"category_scores_gemma":[0.018930033,0.00033934208,0.00063846534,0.0012447278,0.00058158557,0.0016813084,0.001779207,0.0014283995,0.0016706613],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0074957274,0.007850026,0.05790671,0.0012181761,0.0014148876,0.0019145674,0.003424327,0.021656772,0.13497415,0.000930304,0.032332093,0.72888225],"study_design_scores_gemma":[0.0018986722,0.011829093,0.24701603,0.00024571523,0.0019571441,0.0040140417,0.0056116106,0.4795981,0.17899941,0.006074841,0.06212859,0.0006268296],"about_ca_topic_score_codex":0.0038076907,"about_ca_topic_score_gemma":0.003452775,"teacher_disagreement_score":0.00595533,"about_ca_system_score_codex":0.00036022603,"about_ca_system_score_gemma":0.00044254973,"threshold_uncertainty_score":0.031495154},"labels":[],"label_agreement":null},{"id":"W2129913068","doi":"10.1017/s135132491100012x","title":"Learning opinions in user-generated web content","year":2011,"lang":"en","type":"article","venue":"Natural Language Engineering","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Children's Hospital of Eastern Ontario; University of Ottawa","funders":"","keywords":"Computer science; Construct (python library); Information retrieval; Product (mathematics); Hierarchy; User-generated content; Natural language processing; Sentiment analysis; World Wide Web; Artificial intelligence; Web content; Web page; Social media","score_opus":0.022416421518357734,"score_gpt":0.22655476296845853,"score_spread":0.2041383414501008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129913068","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9655349,0.00014482455,0.0319426,0.00018924185,0.000034745364,0.000086699336,0.00047319283,0.00024300431,0.0013507277],"genre_scores_gemma":[0.99008596,0.000039810282,0.00870347,0.000026529886,0.00003648583,0.000026394267,0.0007265876,0.000012356099,0.00034246076],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985373,0.0007574063,0.00008613331,0.0002296275,0.00029721533,0.000092300186],"domain_scores_gemma":[0.98777664,0.00842526,0.0011260076,0.0003295978,0.0021154424,0.00022701758],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018496959,0.00043109283,0.00035654465,0.0017932327,0.00018758161,0.0011051855,0.00038543917,0.00060212973,0.00077094085],"category_scores_gemma":[0.015158251,0.00015734682,0.0003599691,0.00080290943,0.00024186035,0.0012702123,0.00031139774,0.0005389194,0.00047305683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023651067,0.001708509,0.3310988,0.0004893154,0.0006008016,0.0009332584,0.0020124726,0.0947165,0.034988213,0.0024367692,0.007866274,0.520784],"study_design_scores_gemma":[0.000020812404,0.00023778203,0.047842212,0.000018829842,0.000048299094,0.000072255025,0.00030040587,0.9432367,0.0059626694,0.0015590069,0.0006785405,0.000022422726],"about_ca_topic_score_codex":0.0016891935,"about_ca_topic_score_gemma":0.001800506,"teacher_disagreement_score":0.0018496959,"about_ca_system_score_codex":0.0006769084,"about_ca_system_score_gemma":0.00018902891,"threshold_uncertainty_score":0.009782255},"labels":[],"label_agreement":null},{"id":"W2131078515","doi":"10.3115/v1/s14-2065","title":"Kea: Sentiment Analysis of Phrases Within Short Texts","year":2014,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Sarcasm; Sentiment analysis; Computer science; Phrase; Natural language processing; Artificial intelligence; SemEval; Task (project management); Irony; Linguistics","score_opus":0.01795501358753309,"score_gpt":0.2695274342227786,"score_spread":0.2515724206352455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131078515","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15967482,0.00440711,0.7433076,0.0016904778,0.001637149,0.0016415593,0.03208631,0.03591393,0.01964117],"genre_scores_gemma":[0.51697886,0.0020457145,0.42532414,0.0005657936,0.0008165078,0.0010544541,0.03490483,0.0016575757,0.01665205],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99824786,0.000493874,0.00019708234,0.00032882742,0.0006220173,0.0001104341],"domain_scores_gemma":[0.9964533,0.001321268,0.00053349696,0.00042752392,0.0011077019,0.0001567859],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023932583,0.001135517,0.00087531045,0.0036467128,0.0007137614,0.0021496988,0.0007221479,0.00080529135,0.005990742],"category_scores_gemma":[0.0070946985,0.0002682899,0.0009050196,0.0022297513,0.00033087493,0.0032132568,0.0011527017,0.0011539888,0.0075441054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006290697,0.00036851346,0.018641185,0.0013001963,0.00048403908,0.0003791676,0.0009508901,0.0031313086,0.10637867,0.004663981,0.055748396,0.8073246],"study_design_scores_gemma":[0.00015908692,0.0016101147,0.11278739,0.0005493303,0.0005147554,0.00264386,0.0030661605,0.47537798,0.16680977,0.035554312,0.20053889,0.00038844545],"about_ca_topic_score_codex":0.0006164288,"about_ca_topic_score_gemma":0.0012320792,"teacher_disagreement_score":0.005990742,"about_ca_system_score_codex":0.00044656664,"about_ca_system_score_gemma":0.0004817822,"threshold_uncertainty_score":0.020041049},"labels":[],"label_agreement":null},{"id":"W2132059679","doi":"10.3115/v1/w14-2624","title":"The Use of Text Similarity and Sentiment Analysis to Examine Rationales in the Large-Scale Online Deliberations","year":2014,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Sentence; Sentiment analysis; Similarity (geometry); Natural language processing; Reading (process); Scale (ratio); Artificial intelligence; Key (lock); Process (computing); Information retrieval; Identification (biology); Data science; Linguistics; Image (mathematics)","score_opus":0.04589511766461575,"score_gpt":0.2835755704175706,"score_spread":0.23768045275295485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132059679","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37493512,0.00047424922,0.60625833,0.0009478442,0.00021145241,0.0008953258,0.0012484589,0.0012118459,0.013817329],"genre_scores_gemma":[0.74267554,0.0001474768,0.25399706,0.00011800856,0.0001330846,0.00048596738,0.00089185964,0.00008713414,0.0014638671],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9971354,0.0013213984,0.00028577898,0.0004013815,0.0007699093,0.00008610184],"domain_scores_gemma":[0.9788663,0.01335549,0.0029551135,0.0010170625,0.003292258,0.0005138189],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003462948,0.0004931208,0.00048914255,0.007590161,0.0009191166,0.002137488,0.0005356748,0.0006798257,0.002452749],"category_scores_gemma":[0.023805628,0.00018449372,0.00071979355,0.004045366,0.0008530367,0.0032086556,0.0010592837,0.0008355683,0.00078810134],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00084097165,0.0010437553,0.09189534,0.0009928648,0.00044825373,0.0009819792,0.008563357,0.007641276,0.08025957,0.029769925,0.0077983607,0.7697643],"study_design_scores_gemma":[0.00016838443,0.0011816955,0.21007448,0.000299837,0.00036287162,0.001664147,0.010218704,0.6068198,0.046089374,0.10024957,0.022520827,0.0003502858],"about_ca_topic_score_codex":0.0011019082,"about_ca_topic_score_gemma":0.0018392332,"teacher_disagreement_score":0.007590161,"about_ca_system_score_codex":0.00058531895,"about_ca_system_score_gemma":0.0009186184,"threshold_uncertainty_score":0.018314064},"labels":[],"label_agreement":null},{"id":"W21337853","doi":"10.1109/asonam.2012.26","title":"Tools for data analysis.","year":2011,"lang":"en","type":"article","venue":"PubMed","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Sentiment analysis; Naive Bayes classifier; Classifier (UML); Artificial intelligence; Machine learning; Data mining; Pattern recognition (psychology); Support vector machine","score_opus":0.34314752858540276,"score_gpt":0.2906663085654617,"score_spread":0.052481220019941044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W21337853","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012965419,0.004362786,0.79304373,0.0031708586,0.0014819694,0.0029497165,0.056939796,0.10296091,0.03379365],"genre_scores_gemma":[0.021610044,0.0037019749,0.8981991,0.001668128,0.0006878148,0.009846959,0.03939695,0.006271324,0.018617826],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9890414,0.0036294323,0.0025453628,0.0016646206,0.002858739,0.00026039183],"domain_scores_gemma":[0.95934963,0.02239884,0.0028199363,0.009090843,0.0053416872,0.0009989598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018400725,0.0023982637,0.002561837,0.0074536973,0.0009419364,0.005577108,0.0024942067,0.0013002068,0.0917002],"category_scores_gemma":[0.050000526,0.0013414078,0.0023782898,0.007365557,0.0011442379,0.0046440605,0.004506679,0.0036140345,0.07142217],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044265538,0.00019174894,0.0022737498,0.004879541,0.0006303368,0.0006321994,0.0007205781,0.002586827,0.0048385332,0.05747068,0.32058498,0.60474813],"study_design_scores_gemma":[0.00024174,0.00018117389,0.0040800767,0.0020826873,0.0002301568,0.00089733605,0.00045797188,0.020679997,0.0043971925,0.11676313,0.84984994,0.00013854886],"about_ca_topic_score_codex":0.0014334824,"about_ca_topic_score_gemma":0.001328932,"teacher_disagreement_score":0.0917002,"about_ca_system_score_codex":0.0012737644,"about_ca_system_score_gemma":0.004048908,"threshold_uncertainty_score":0.3067677},"labels":[],"label_agreement":null},{"id":"W2136376686","doi":"10.1177/0010880405276309","title":"Let Me Count the Words","year":2005,"lang":"en","type":"article","venue":"Cornell Hotel and Restaurant Administration Quarterly","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Quest University Canada","funders":"","keywords":"Computer science; Context (archaeology); Customer intelligence; Categorization; Phrase; Point (geometry); Linguistics; Semantics (computer science); Syntax; Customer relationship management; Voice of the customer; Process (computing); Meaning (existential); Natural language processing; Data science; Customer retention; Artificial intelligence; Psychology; Marketing; Business; Database; Service (business)","score_opus":0.017325011516426205,"score_gpt":0.2433321013699617,"score_spread":0.2260070898535355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136376686","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059049107,0.042138338,0.03425944,0.10360041,0.059469726,0.001105891,0.06672028,0.0079604,0.6256964],"genre_scores_gemma":[0.20181358,0.02970289,0.04638513,0.041012354,0.013213062,0.0011655504,0.02952177,0.0047920826,0.63239366],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977582,0.00041239578,0.00030619802,0.00033132854,0.0009925416,0.00019941795],"domain_scores_gemma":[0.9950617,0.0011882741,0.0005259133,0.00027529133,0.0026044755,0.00034433417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011048102,0.0012214972,0.00073169096,0.004720636,0.0018576282,0.0044261143,0.00081733445,0.0011560698,0.1403799],"category_scores_gemma":[0.011293877,0.000328151,0.0005545478,0.005083258,0.0010101709,0.0061243917,0.0017304479,0.0017164128,0.15682578],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021740225,0.00003996304,0.0050250115,0.0009807777,0.000039608356,0.00031604368,0.0030148386,0.000076269156,0.0025208448,0.009558864,0.7209082,0.25730205],"study_design_scores_gemma":[0.000009818638,0.0000567023,0.005261858,0.0004356855,0.000018677103,0.0006744964,0.0038843802,0.00011842461,0.0008787817,0.003490181,0.9851295,0.000041519634],"about_ca_topic_score_codex":0.003043788,"about_ca_topic_score_gemma":0.0033605576,"teacher_disagreement_score":0.1403799,"about_ca_system_score_codex":0.0009486864,"about_ca_system_score_gemma":0.00069912424,"threshold_uncertainty_score":0.46961755},"labels":[],"label_agreement":null},{"id":"W2140840069","doi":"10.1109/hicss.2011.259","title":"Is Happiness Contagious Online? A Case of Twitter and the 2010 Winter Olympics","year":2011,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":91,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Happiness; Argumentative; Context (archaeology); Computer science; Sentiment analysis; Advertising; World Wide Web; Internet privacy; Social media; Psychology; Online community; Social psychology; Artificial intelligence; Political science; Business; Geography","score_opus":0.062250414095902165,"score_gpt":0.27682346773740274,"score_spread":0.2145730536415006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140840069","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9793761,0.00047966317,0.000423528,0.009257254,0.000048677346,0.000015411559,0.00011094999,0.0000067744822,0.010281607],"genre_scores_gemma":[0.9981724,0.00037975676,0.00018424918,0.00041038202,0.000106286294,0.000013440555,0.000052857995,0.000007300385,0.0006732437],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99870765,0.0007700361,0.000042880198,0.00010098056,0.00013646626,0.00024197517],"domain_scores_gemma":[0.9922523,0.004977433,0.0015474677,0.00024745922,0.00035566936,0.0006197603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022476697,0.00024180251,0.000358397,0.001665552,0.004634982,0.0028695464,0.00041777332,0.0023527707,0.002618595],"category_scores_gemma":[0.007915233,0.00022683326,0.000367785,0.001950059,0.002593429,0.0037865404,0.0017724144,0.001565446,0.00028394102],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015876252,0.0004779994,0.5334624,0.00040298642,0.0002424098,0.040304378,0.30641967,0.0010453988,0.0064216894,0.03689994,0.0149388155,0.057796806],"study_design_scores_gemma":[0.000048603637,0.00022976442,0.43984684,0.00026879492,0.00014726001,0.00627331,0.50161415,0.0055725197,0.0014549713,0.011070875,0.033358123,0.00011476651],"about_ca_topic_score_codex":0.012615714,"about_ca_topic_score_gemma":0.020386292,"teacher_disagreement_score":0.012615714,"about_ca_system_score_codex":0.0012851916,"about_ca_system_score_gemma":0.00046210643,"threshold_uncertainty_score":0.025084555},"labels":[],"label_agreement":null},{"id":"W2142341229","doi":"10.1109/wiiat.2008.299","title":"HelpMeter: A Nonlinear Model for Predicting the Helpfulness of Online Reviews","year":2008,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Helpfulness; Automatic summarization; Computer science; Quality (philosophy); Sentiment analysis; Set (abstract data type); The Internet; Data science; Data mining; Artificial intelligence; Machine learning; World Wide Web; Psychology","score_opus":0.10095571229430465,"score_gpt":0.31166483247510174,"score_spread":0.21070912018079707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142341229","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24364048,0.0017388563,0.74420035,0.0018100605,0.00021095535,0.0004423293,0.0017961361,0.0026484004,0.003512367],"genre_scores_gemma":[0.8289235,0.00071203447,0.16236217,0.00032471633,0.00027325936,0.0004033223,0.0015109557,0.000105114625,0.005384905],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925834,0.00031785807,0.000043214946,0.00015113363,0.00017973392,0.000049651095],"domain_scores_gemma":[0.9958467,0.00245552,0.00052638364,0.00014410188,0.0009080149,0.00011925772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023887896,0.0010552965,0.00087144994,0.0017010065,0.00026930936,0.0007632861,0.0012498951,0.000970637,0.001301139],"category_scores_gemma":[0.010084506,0.00032905606,0.0006007651,0.0008566346,0.0003436417,0.0013850116,0.00058231916,0.0011242465,0.0010012115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015986343,0.00085221475,0.093786426,0.0006426728,0.00057169504,0.00048258208,0.0006732977,0.30016097,0.016288593,0.006698299,0.021330101,0.5569145],"study_design_scores_gemma":[0.000015975684,0.00010594794,0.0032935385,0.000009858039,0.00002873937,0.000046885383,0.000021618604,0.9934455,0.0010948877,0.0010439424,0.0008750697,0.000017914183],"about_ca_topic_score_codex":0.006575235,"about_ca_topic_score_gemma":0.009191314,"teacher_disagreement_score":0.006575235,"about_ca_system_score_codex":0.00074893166,"about_ca_system_score_gemma":0.0007973771,"threshold_uncertainty_score":0.013073921},"labels":[],"label_agreement":null},{"id":"W2145747781","doi":"10.3115/v1/d14-1124","title":"Detecting Disagreement in Conversations using Pseudo-Monologic Rhetorical Structure","year":2014,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Bigram; Rhetorical question; Conversation; Popularity; Casual; Computer science; Social media; Artificial intelligence; Natural language processing; Domain (mathematical analysis); Set (abstract data type); Baseline (sea); Psychology; Linguistics; World Wide Web; Social psychology; Communication; Mathematics","score_opus":0.03814397500976811,"score_gpt":0.28260476569325454,"score_spread":0.24446079068348645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145747781","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8377483,0.0015929628,0.12866126,0.00071712316,0.00040445168,0.00041222488,0.006538978,0.0019786276,0.021946073],"genre_scores_gemma":[0.93338984,0.00027353148,0.0552201,0.000093299095,0.00019229877,0.00035659227,0.0072145513,0.0001836815,0.0030760998],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.994825,0.0023894655,0.00041066043,0.0007938234,0.0013210615,0.00025993725],"domain_scores_gemma":[0.9710476,0.020935806,0.0020892282,0.0013079057,0.004005084,0.00061437086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036864954,0.00065623614,0.0005285476,0.0054651625,0.0012861075,0.0020300315,0.0007843964,0.0013267897,0.0027703466],"category_scores_gemma":[0.027459878,0.00029406394,0.00036504027,0.002644612,0.0006393395,0.002896917,0.0020384716,0.0010374227,0.0020813707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025033865,0.0007957213,0.1378126,0.0034223925,0.00025577142,0.0022680522,0.034753975,0.005856093,0.17583336,0.0132251335,0.02283896,0.6004345],"study_design_scores_gemma":[0.00023661705,0.0013443665,0.3137601,0.0008352699,0.00028885258,0.0031935996,0.024663426,0.3659253,0.13107443,0.03242679,0.12584084,0.0004104513],"about_ca_topic_score_codex":0.00089697517,"about_ca_topic_score_gemma":0.0017781588,"teacher_disagreement_score":0.0054651625,"about_ca_system_score_codex":0.00060680514,"about_ca_system_score_gemma":0.0005509789,"threshold_uncertainty_score":0.019496322},"labels":[],"label_agreement":null},{"id":"W2146281278","doi":"","title":"uOttawa: System description for SemEval 2013 Task 2 Sentiment Analysis in Twitter","year":2013,"lang":"en","type":"article","venue":"Joint Conference on Lexical and Computational Semantics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"SemEval; Computer science; Task (project management); Polarity (international relations); Word (group theory); Artificial intelligence; Natural language processing; Feature (linguistics); Sentiment analysis; Representation (politics); Orientation (vector space); Simple (philosophy)","score_opus":0.05108846982769074,"score_gpt":0.270176888819298,"score_spread":0.21908841899160728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146281278","genre_codex":"software","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023406412,0.0017924322,0.14746779,0.0010280441,0.0006687876,0.0070381314,0.17952314,0.6166534,0.022421872],"genre_scores_gemma":[0.14149252,0.00081040204,0.3801493,0.0015444095,0.0002425523,0.01968082,0.40341815,0.022853,0.029808847],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979023,0.00044161422,0.00026381333,0.0006412043,0.00050521543,0.0002458012],"domain_scores_gemma":[0.99765044,0.00056437624,0.0001031182,0.0005688548,0.000927386,0.00018582279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001972394,0.0026152013,0.0021049979,0.0031311589,0.0017660838,0.0030499771,0.0037261066,0.0019224598,0.03878409],"category_scores_gemma":[0.0068828617,0.0012939015,0.0014579617,0.0019259087,0.0005280277,0.0033970948,0.0021471228,0.0015218446,0.034880087],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027854703,0.000433965,0.006801257,0.002661793,0.0005643322,0.0007179083,0.00086527015,0.007030911,0.032976482,0.0027735857,0.74931014,0.19307882],"study_design_scores_gemma":[0.0012696886,0.0007052884,0.012166103,0.0003428187,0.0005657847,0.0010389842,0.00070323853,0.20535514,0.10049719,0.0077319546,0.66886365,0.00076009217],"about_ca_topic_score_codex":0.044230297,"about_ca_topic_score_gemma":0.05418789,"teacher_disagreement_score":0.044230297,"about_ca_system_score_codex":0.002163457,"about_ca_system_score_gemma":0.0032717537,"threshold_uncertainty_score":0.12974572},"labels":[],"label_agreement":null},{"id":"W2146492580","doi":"10.1007/s11280-011-0127-3","title":"Opinion helpfulness prediction in the presence of “words of few mouths”","year":2011,"lang":"en","type":"article","venue":"World Wide Web","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Helpfulness; Probabilistic logic; Computer science; Phenomenon; Support vector machine; Logistic regression; Artificial intelligence; Machine learning; Metric (unit); Context (archaeology); Recommender system; Psychology; Social psychology","score_opus":0.03919549582236045,"score_gpt":0.25529995848637765,"score_spread":0.2161044626640172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146492580","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98877674,0.00020925893,0.00793652,0.00023834298,0.00006549889,0.000025158086,0.00047482958,0.00009278426,0.0021808222],"genre_scores_gemma":[0.99706763,0.000055333447,0.0019482613,0.00002912927,0.00008406573,0.0000066995594,0.000402713,0.000007002375,0.00039923226],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955374,0.00013200966,0.00003808087,0.00007863203,0.00013075919,0.00006671581],"domain_scores_gemma":[0.99163955,0.00528074,0.0010107383,0.0002038816,0.0014510475,0.0004140631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011380418,0.00040448783,0.00044452972,0.0011293552,0.00033021538,0.000707162,0.00020209301,0.0006319655,0.0011305818],"category_scores_gemma":[0.0072436943,0.00010255947,0.00035083306,0.0005915527,0.00016320025,0.0009798346,0.00026302383,0.00055327703,0.00057668623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0042286627,0.00082660373,0.6678009,0.00046755475,0.00042470696,0.0017174187,0.00068484864,0.010572404,0.08897108,0.0012852197,0.012287018,0.21073352],"study_design_scores_gemma":[0.00006998963,0.0007174768,0.35780823,0.00004733866,0.00045987844,0.00091236475,0.00084791356,0.6075792,0.026368335,0.0024261768,0.0027038923,0.00005919071],"about_ca_topic_score_codex":0.0014226693,"about_ca_topic_score_gemma":0.0023342795,"teacher_disagreement_score":0.0014226693,"about_ca_system_score_codex":0.00020920578,"about_ca_system_score_gemma":0.00019096315,"threshold_uncertainty_score":0.0060186386},"labels":[],"label_agreement":null},{"id":"W2147083002","doi":"10.1109/wiiat.2008.149","title":"An Entropy-Based Model for Discovering the Usefulness of Online Product Reviews","year":2008,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Helpfulness; Computer science; Entropy (arrow of time); Ranking (information retrieval); Product (mathematics); Quality (philosophy); Data science; Machine learning; Information retrieval; World Wide Web; Mathematics; Psychology","score_opus":0.11487637012675579,"score_gpt":0.3135734761200201,"score_spread":0.19869710599326434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147083002","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26952738,0.0014422085,0.7204432,0.0016886876,0.00013609724,0.00023034113,0.0013012697,0.0006968896,0.0045338566],"genre_scores_gemma":[0.94949394,0.00055799953,0.04581163,0.00015398128,0.00023580654,0.0002044195,0.0008583452,0.000034438923,0.002649474],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988901,0.00039137222,0.00010146317,0.00021279717,0.00032746748,0.000076795106],"domain_scores_gemma":[0.990424,0.00792231,0.0006359277,0.00017385927,0.00073426665,0.00010969392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002574911,0.0008936333,0.0010272043,0.0029142834,0.00037908703,0.0012801387,0.0010391972,0.0011794228,0.0012587754],"category_scores_gemma":[0.012609908,0.00047128825,0.0007780289,0.0016233324,0.00055686885,0.0024067103,0.00047271675,0.000951454,0.0004203111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006422094,0.0005189672,0.0409619,0.0002994878,0.00047637965,0.0005874318,0.0005460722,0.7411844,0.0052622487,0.02088912,0.0048868107,0.18374495],"study_design_scores_gemma":[0.000007449671,0.00003248553,0.0018910136,0.000007797678,0.000024004961,0.000054562322,0.000006099398,0.99348867,0.00021479724,0.0041051344,0.00015682532,0.000011133989],"about_ca_topic_score_codex":0.004596725,"about_ca_topic_score_gemma":0.0043896325,"teacher_disagreement_score":0.004596725,"about_ca_system_score_codex":0.000992512,"about_ca_system_score_gemma":0.0005843053,"threshold_uncertainty_score":0.013617575},"labels":[],"label_agreement":null},{"id":"W2153062644","doi":"10.1145/1772690.1772882","title":"A quality-aware model for sales prediction using reviews","year":2010,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Quality (philosophy)","score_opus":0.1606706584683008,"score_gpt":0.3864617005880452,"score_spread":0.22579104211974438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153062644","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18962924,0.0038097915,0.7932924,0.0031207057,0.00042131817,0.00024899814,0.0016909863,0.0019248676,0.00586172],"genre_scores_gemma":[0.8964982,0.0013617026,0.09129715,0.00036167345,0.0004914559,0.00029848234,0.0017512642,0.000114764385,0.007825158],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992711,0.00020956395,0.000069313384,0.00023521273,0.00014407556,0.00007078948],"domain_scores_gemma":[0.99651307,0.00197943,0.00045557966,0.000108682085,0.0008544885,0.00008879006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002279157,0.0010375337,0.0017843074,0.0013361183,0.00036941614,0.0015519644,0.0023291106,0.0014883172,0.0018659277],"category_scores_gemma":[0.0063087335,0.00087498146,0.00090788875,0.0014375706,0.0003847877,0.0016599233,0.00047319036,0.0014731832,0.0012682399],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034873132,0.0002758792,0.010508713,0.00020818794,0.00026607083,0.00029669947,0.00013660773,0.8610734,0.0023297684,0.0048526456,0.0061064824,0.11359676],"study_design_scores_gemma":[0.000006085145,0.00001131561,0.00031750475,0.0000033030954,0.000013698903,0.000011199586,0.0000021705737,0.998777,0.000091041446,0.0006235417,0.00013939315,0.0000038299772],"about_ca_topic_score_codex":0.0123735005,"about_ca_topic_score_gemma":0.010803085,"teacher_disagreement_score":0.0123735005,"about_ca_system_score_codex":0.0009776978,"about_ca_system_score_gemma":0.00092898874,"threshold_uncertainty_score":0.02460295},"labels":[],"label_agreement":null},{"id":"W2154444445","doi":"10.3115/v1/s14-2077","title":"NRC-Canada-2014: Recent Improvements in the Sentiment Analysis of Tweets","year":2014,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":126,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sentiment analysis; Computer science; Natural language processing; Data science; Artificial intelligence","score_opus":0.01288012008695685,"score_gpt":0.2461352019669492,"score_spread":0.23325508187999236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154444445","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13304965,0.02031776,0.33498213,0.012961708,0.008001737,0.0052304002,0.13296002,0.28196317,0.07053336],"genre_scores_gemma":[0.13212374,0.0042961496,0.5555386,0.0024658975,0.0015715793,0.0018344175,0.24594057,0.013676552,0.04255253],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.98448807,0.0029240393,0.000978511,0.0023449115,0.0082507115,0.0010137004],"domain_scores_gemma":[0.9530146,0.0050534825,0.0009756196,0.004612267,0.03463992,0.0017040231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019791892,0.0034682571,0.0024457728,0.00959583,0.002705178,0.0040064952,0.0052928245,0.0017472951,0.009989619],"category_scores_gemma":[0.032793734,0.0014851608,0.0018312834,0.0064577414,0.0013554844,0.00452453,0.0044814763,0.0037395481,0.01405197],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013497606,0.0005926539,0.014890407,0.0014407692,0.0005062362,0.00014334689,0.0007816931,0.0033687295,0.030028174,0.0024669857,0.3936301,0.5508011],"study_design_scores_gemma":[0.0007249321,0.0008696838,0.058826517,0.0005877679,0.00064454484,0.0005030342,0.0012539976,0.20601785,0.059869256,0.0044198125,0.665603,0.000679541],"about_ca_topic_score_codex":0.3303829,"about_ca_topic_score_gemma":0.45914778,"teacher_disagreement_score":0.3303829,"about_ca_system_score_codex":0.0064296927,"about_ca_system_score_gemma":0.0152211515,"threshold_uncertainty_score":0.6569197},"labels":[],"label_agreement":null},{"id":"W2155224116","doi":"10.1007/s10115-012-0495-8","title":"Estimating feature ratings through an effective review selection approach","year":2013,"lang":"en","type":"article","venue":"Knowledge and Information Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Feature (linguistics); Purchasing; Feature selection; Product (mathematics); Recommender system; Machine learning; Selection (genetic algorithm); Sentiment analysis; Artificial intelligence; Data mining; Information retrieval; Marketing","score_opus":0.01256800742941423,"score_gpt":0.2659206055018179,"score_spread":0.2533525980724037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155224116","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06664125,0.0030471303,0.92366225,0.00058792584,0.00029574693,0.00046100022,0.0007528163,0.001518447,0.0030335449],"genre_scores_gemma":[0.6093815,0.0012076913,0.38000894,0.00022173973,0.000949306,0.00042217842,0.0019303565,0.00012750611,0.0057508466],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995937,0.0013471182,0.00034797995,0.0006312073,0.0015454947,0.000191267],"domain_scores_gemma":[0.9882881,0.005195532,0.0008750958,0.0006042421,0.0048491647,0.0001878998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038322138,0.0011500322,0.0017227246,0.0046589402,0.0005883146,0.0019519727,0.0012173448,0.001188,0.0016771196],"category_scores_gemma":[0.015526925,0.00038767103,0.0010967342,0.0029566218,0.0002501567,0.0018111608,0.00061446754,0.00074768375,0.0016395648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064555724,0.00044794698,0.015071817,0.00057583867,0.00079214375,0.00033634194,0.00015374548,0.016472483,0.031988125,0.002715764,0.021295322,0.90950495],"study_design_scores_gemma":[0.00012350245,0.0005592709,0.021664556,0.000056035493,0.0008291542,0.000587978,0.00013602097,0.94541395,0.016865246,0.0052207573,0.008449137,0.000094470626],"about_ca_topic_score_codex":0.002551422,"about_ca_topic_score_gemma":0.005132187,"teacher_disagreement_score":0.0046589402,"about_ca_system_score_codex":0.00047520836,"about_ca_system_score_gemma":0.001009763,"threshold_uncertainty_score":0.02026695},"labels":[],"label_agreement":null},{"id":"W2156413587","doi":"10.1613/jair.4272","title":"Sentiment Analysis of Short Informal Texts","year":2014,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Research","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":890,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Computer science; SemEval; Task (project management); Lexicon; Sentiment analysis; Phrase; Natural language processing; Artificial intelligence; Variety (cybernetics); Word (group theory); Term (time); Set (abstract data type); Linguistics","score_opus":0.14109590791519244,"score_gpt":0.42683299948391384,"score_spread":0.2857370915687214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156413587","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43652833,0.0031576273,0.48434526,0.0012373655,0.0010752162,0.0015941818,0.022678223,0.013285969,0.036097877],"genre_scores_gemma":[0.7146364,0.0016473386,0.22799759,0.00040470882,0.0010231466,0.0010297158,0.034921143,0.0007610975,0.017578801],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988631,0.0002494388,0.00013107111,0.0001997902,0.00047172257,0.00008483694],"domain_scores_gemma":[0.9974105,0.00083759404,0.0003894728,0.00019824166,0.001066459,0.00009781727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084725366,0.0008502281,0.00055427756,0.0018553106,0.00039918892,0.0010547624,0.0003945854,0.00034961503,0.0049460963],"category_scores_gemma":[0.00449419,0.00016530958,0.0005781113,0.001020459,0.00022263723,0.0010147347,0.0006578276,0.00041295672,0.0043669306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007428139,0.00024569922,0.017206151,0.0015356012,0.00021593446,0.0006995876,0.001047214,0.0030341588,0.21458212,0.003283647,0.04082642,0.71658075],"study_design_scores_gemma":[0.00020053957,0.0012854254,0.16202071,0.00052899675,0.00039043662,0.002338798,0.0025714564,0.32343575,0.23591574,0.022921655,0.24813122,0.0002593223],"about_ca_topic_score_codex":0.0006610026,"about_ca_topic_score_gemma":0.0012771362,"teacher_disagreement_score":0.0049460963,"about_ca_system_score_codex":0.00029384758,"about_ca_system_score_gemma":0.0003167928,"threshold_uncertainty_score":0.016546309},"labels":[],"label_agreement":null},{"id":"W2157104236","doi":"10.1002/asi.23052","title":"A method for automatic extraction of multiword units representing business aspects from user reviews","year":2014,"lang":"en","type":"article","venue":"Journal of the Association for Information Science and Technology","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Ranking (information retrieval); Similarity (geometry); Natural language processing; Set (abstract data type); Information retrieval; Task (project management); Artificial intelligence; Word (group theory); Similarity measure; Measure (data warehouse); Function (biology); Data mining; Mathematics","score_opus":0.024752128288583596,"score_gpt":0.3203154096843643,"score_spread":0.2955632813957807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157104236","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023480864,0.0016023044,0.9508124,0.00040460785,0.00038110296,0.0015817888,0.0047053583,0.012970105,0.0040615215],"genre_scores_gemma":[0.05641483,0.00047780754,0.93147224,0.00009429896,0.00014390644,0.0013235409,0.00507616,0.0004045339,0.0045926035],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99768996,0.00048957864,0.00030995437,0.00047166398,0.0009627269,0.00007610078],"domain_scores_gemma":[0.99636674,0.0011682977,0.00046304095,0.00031723312,0.0015885067,0.00009618595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012440889,0.0014607767,0.00078615424,0.0053194007,0.00070590863,0.0013511382,0.0008053358,0.0010200425,0.0028944432],"category_scores_gemma":[0.006052801,0.0006029739,0.00093128585,0.0037771892,0.00036153145,0.0015033624,0.0007203261,0.00095783535,0.00462441],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020375208,0.00017467339,0.0037821229,0.0011189365,0.00020883132,0.00036031875,0.0005469573,0.0008550223,0.08745321,0.0018062757,0.02177185,0.88171804],"study_design_scores_gemma":[0.0003372038,0.0011397507,0.070819244,0.00051646645,0.00087187596,0.0066951006,0.0011797899,0.35241547,0.25535622,0.010832,0.2992718,0.0005651157],"about_ca_topic_score_codex":0.0020153967,"about_ca_topic_score_gemma":0.0042487746,"teacher_disagreement_score":0.0053194007,"about_ca_system_score_codex":0.0005079568,"about_ca_system_score_gemma":0.001473374,"threshold_uncertainty_score":0.009682834},"labels":[],"label_agreement":null},{"id":"W2159087629","doi":"10.1017/s1351324911000118","title":"A hierarchical approach to mood classification in blogs","year":2011,"lang":"en","type":"article","venue":"Natural Language Engineering","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Task (project management); Mood; Hierarchy; Set (abstract data type); Artificial intelligence; Machine learning; Sentiment analysis; Training set; Orientation (vector space); Natural language processing; Data set; Psychology","score_opus":0.01986886967877055,"score_gpt":0.23441606871738657,"score_spread":0.21454719903861602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159087629","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17277862,0.0016608939,0.80581707,0.0010359172,0.0003098799,0.00063697237,0.0032447756,0.0045899246,0.009926039],"genre_scores_gemma":[0.65030545,0.00032882264,0.34102142,0.0001837088,0.00029991227,0.0003539719,0.0035221737,0.00018991182,0.003794614],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985702,0.00051949866,0.00012596771,0.0003350371,0.0002834774,0.00016587762],"domain_scores_gemma":[0.99698323,0.0012684517,0.0002910272,0.00034951683,0.0009519132,0.0001558324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018033439,0.0006212797,0.00070317695,0.004992743,0.0013046992,0.0014658804,0.00097228686,0.00065964606,0.0033661597],"category_scores_gemma":[0.0057584825,0.0003640403,0.000856318,0.002895956,0.00047230555,0.0014008598,0.001161119,0.001118101,0.0016420824],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005427099,0.0007141877,0.028541606,0.00039317805,0.00018390208,0.00022826994,0.0007308711,0.021800796,0.026189126,0.0070070117,0.024741286,0.8889271],"study_design_scores_gemma":[0.00007053383,0.00015315299,0.023979135,0.00007315566,0.00009569492,0.00012141468,0.0003889947,0.93334395,0.007116538,0.02814004,0.006457412,0.00005996676],"about_ca_topic_score_codex":0.0075345226,"about_ca_topic_score_gemma":0.01445476,"teacher_disagreement_score":0.0075345226,"about_ca_system_score_codex":0.00086483563,"about_ca_system_score_gemma":0.0008609396,"threshold_uncertainty_score":0.014981329},"labels":[],"label_agreement":null},{"id":"W2161926933","doi":"","title":"When Specialists and Generalists Work Together: Overcoming Domain Dependence in Sentiment Tagging","year":2008,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":165,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Software portability; Computer science; Lexicon; WordNet; Classifier (UML); Artificial intelligence; Annotation; Natural language processing; Domain (mathematical analysis); Weighting; Sentiment analysis; Precision and recall; Training set; Information retrieval; Machine learning","score_opus":0.026361699187722576,"score_gpt":0.2488749297044931,"score_spread":0.22251323051677052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161926933","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64051574,0.0010136681,0.3263978,0.0041180276,0.0005961139,0.00064990553,0.000556861,0.0022844388,0.023867508],"genre_scores_gemma":[0.9310318,0.00021580947,0.0626994,0.0014106001,0.00029096473,0.0001936798,0.0007507794,0.00044643832,0.0029604805],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9816595,0.0098720165,0.0009905916,0.004459599,0.00218264,0.0008356997],"domain_scores_gemma":[0.938006,0.041122288,0.004731069,0.007109711,0.0073293834,0.0017016147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027693918,0.0010725106,0.0014522785,0.002653231,0.0031351768,0.003791571,0.0018289656,0.0022680298,0.0015442123],"category_scores_gemma":[0.065442756,0.0013493404,0.000558907,0.0023777562,0.0019363547,0.011606466,0.0059581962,0.0029435684,0.0015795729],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025500844,0.0004662247,0.19210337,0.0006986744,0.0003789397,0.0018039901,0.031737342,0.005962857,0.053612888,0.0054499027,0.016730012,0.6885058],"study_design_scores_gemma":[0.00045179107,0.0015075192,0.24276954,0.0006436647,0.0012062533,0.0053668683,0.0443457,0.45648393,0.07398529,0.093777366,0.07884892,0.0006130913],"about_ca_topic_score_codex":0.0048983023,"about_ca_topic_score_gemma":0.008711512,"teacher_disagreement_score":0.027693918,"about_ca_system_score_codex":0.0013792568,"about_ca_system_score_gemma":0.0017704488,"threshold_uncertainty_score":0.14646113},"labels":[],"label_agreement":null},{"id":"W2162010436","doi":"","title":"Emotions Evoked by Common Words and Phrases: Using Mechanical Turk to Create an Emotion Lexicon","year":2010,"lang":"en","type":"article","venue":"NPARC","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":873,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"National Research Council Canada","keywords":"Lexicon; Computer science; Natural language processing; Word (group theory); Sentiment analysis; Emotion classification; Orientation (vector space); Artificial intelligence; Term (time); Polarity (international relations); Quality (philosophy); Emotion recognition; Semantics (computer science); Speech recognition; Linguistics; Mathematics","score_opus":0.024727977181637467,"score_gpt":0.2964459804661465,"score_spread":0.271718003284509,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162010436","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81391233,0.00035119566,0.14379528,0.0008871724,0.0002460492,0.0041610315,0.01502479,0.0021274285,0.019494694],"genre_scores_gemma":[0.79325104,0.0001736316,0.16515769,0.00058945996,0.00012632234,0.007983799,0.026761616,0.0004660407,0.0054905475],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9961195,0.002109001,0.00036037387,0.00048744772,0.0007508595,0.00017289557],"domain_scores_gemma":[0.9885427,0.007054177,0.0010328116,0.001043756,0.002017295,0.00030937223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032371539,0.00073272013,0.00042130976,0.0028850287,0.0010446671,0.0014911771,0.0008060238,0.00080303807,0.0030354992],"category_scores_gemma":[0.01413025,0.0002828419,0.00058115367,0.0019759724,0.0009835534,0.0013380923,0.0019708204,0.00086436194,0.0018766284],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0030954776,0.0023078148,0.10780106,0.0034172279,0.00040130952,0.00441221,0.032589495,0.017171346,0.25088805,0.015786223,0.09766373,0.4644661],"study_design_scores_gemma":[0.0008138227,0.0018530393,0.3978388,0.00053306384,0.0003343799,0.0029813414,0.025001755,0.2263395,0.09878345,0.036651645,0.20810638,0.00076277915],"about_ca_topic_score_codex":0.0026037104,"about_ca_topic_score_gemma":0.0041290573,"teacher_disagreement_score":0.0032371539,"about_ca_system_score_codex":0.0008646214,"about_ca_system_score_gemma":0.0006577972,"threshold_uncertainty_score":0.017119944},"labels":[],"label_agreement":null},{"id":"W2166048187","doi":"10.1016/j.dss.2012.05.030","title":"From once upon a time to happily ever after: Tracking emotions in mail and books","year":2012,"lang":"en","type":"article","venue":"Decision Support Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":161,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Sentiment analysis; Affect (linguistics); Tracking (education); Computer science; Psychology; Negative emotion; Natural language processing; Social psychology; Communication","score_opus":0.026288756472289582,"score_gpt":0.277846949778926,"score_spread":0.2515581933066364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166048187","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98906976,0.00053511903,0.00275532,0.00050657144,0.000108999135,0.00005436768,0.0027095904,0.0004461895,0.0038141524],"genre_scores_gemma":[0.9843386,0.00026118846,0.0063376813,0.00014790457,0.00014474314,0.000055887875,0.0033648098,0.000067772446,0.005281466],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947935,0.00015566188,0.000044626933,0.000096456155,0.00016620512,0.00005772247],"domain_scores_gemma":[0.9952768,0.0029069025,0.0005454948,0.00013351362,0.00088454946,0.00025274532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077832886,0.00029924093,0.00024713084,0.0015155121,0.0005544046,0.0021970482,0.00033068005,0.0006303618,0.0022470197],"category_scores_gemma":[0.0073602214,0.00014565159,0.0001730593,0.0014327692,0.00020134811,0.001669452,0.0004930144,0.0006005175,0.0021319543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019389248,0.0007272452,0.55793214,0.0004869227,0.00024011328,0.0008567465,0.008416316,0.0022850214,0.013721045,0.00090075,0.060298856,0.35219583],"study_design_scores_gemma":[0.000039663417,0.00033399722,0.89202285,0.0001235841,0.00023218295,0.00043830916,0.01608089,0.05621628,0.0101001505,0.0016255216,0.022693347,0.000093200455],"about_ca_topic_score_codex":0.0051844795,"about_ca_topic_score_gemma":0.012415498,"teacher_disagreement_score":0.0051844795,"about_ca_system_score_codex":0.00060953584,"about_ca_system_score_gemma":0.00022388718,"threshold_uncertainty_score":0.010308564},"labels":[],"label_agreement":null},{"id":"W2166131245","doi":"10.1109/nlpke.2009.5313734","title":"Using sentiment orientation features for mood classification in blogs","year":2009,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Mood; Orientation (vector space); Task (project management); Sentiment analysis; Artificial intelligence; Hierarchy; Machine learning; Natural language processing; Psychology; Engineering","score_opus":0.06408382916153677,"score_gpt":0.3488221512552061,"score_spread":0.28473832209366934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166131245","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7268637,0.0034242324,0.22385256,0.0012050695,0.0013548133,0.0007884286,0.009111983,0.0074955993,0.025903616],"genre_scores_gemma":[0.8541433,0.00087166973,0.12859386,0.00020418882,0.000768804,0.00028966667,0.009479015,0.0002548323,0.005394599],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949086,0.0001156031,0.00004837408,0.000110319976,0.00015607825,0.0000786961],"domain_scores_gemma":[0.99804974,0.0009242134,0.0002226235,0.000133305,0.00055989216,0.00011020316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096158846,0.0009299204,0.00078033854,0.0034116488,0.0008165637,0.0013547861,0.00042179198,0.0006801864,0.0034531988],"category_scores_gemma":[0.0033127675,0.00024117646,0.00061387575,0.0021000847,0.00022453279,0.0015375628,0.000488378,0.00073127786,0.0027820508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062920933,0.000905316,0.045326162,0.00053242967,0.00018411195,0.00028632506,0.0003123203,0.0029732704,0.0613913,0.00078920904,0.025704548,0.8609657],"study_design_scores_gemma":[0.00034627315,0.0014383137,0.2789944,0.00037161095,0.000692544,0.0014210542,0.0016620605,0.55033827,0.099000245,0.014796893,0.05058302,0.00035530693],"about_ca_topic_score_codex":0.0012814398,"about_ca_topic_score_gemma":0.002937438,"teacher_disagreement_score":0.0034531988,"about_ca_system_score_codex":0.0002755761,"about_ca_system_score_gemma":0.00029753832,"threshold_uncertainty_score":0.011552095},"labels":[],"label_agreement":null},{"id":"W2168625136","doi":"10.1145/944012.944013","title":"Measuring praise and criticism","year":2003,"lang":"en","type":"article","venue":"ACM Transactions on Information Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1509,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"National Aeronautics and Space Administration","keywords":"Computer science; Praise; Natural language processing; Artificial intelligence; Orientation (vector space); Pointwise mutual information; Noun; Set (abstract data type); Word (group theory); Latent semantic analysis; Criticism; Semantics (computer science); Linguistics; Psychology; Mutual information; Mathematics","score_opus":0.03905183252988257,"score_gpt":0.24862510286070227,"score_spread":0.2095732703308197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168625136","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9543831,0.0003378367,0.019371713,0.00029821522,0.00006510548,0.00022820862,0.0006457847,0.00017880127,0.024491359],"genre_scores_gemma":[0.99120015,0.00013675455,0.0067294333,0.000051256597,0.00003247756,0.0001159688,0.00040653147,0.000018767712,0.0013087764],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99505496,0.0016094358,0.000498367,0.0004476615,0.0021573857,0.00023222412],"domain_scores_gemma":[0.9774327,0.00944686,0.0067352764,0.0011778933,0.004491852,0.0007154675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003876772,0.00046441663,0.00040278325,0.0030162926,0.00047844497,0.0017622019,0.00046799026,0.00079849403,0.0021128512],"category_scores_gemma":[0.03181303,0.00017392167,0.00050303724,0.0017332184,0.0012645262,0.0019120878,0.0016714886,0.0008936687,0.0007852385],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00074446516,0.00033878683,0.71742016,0.0006400787,0.00036133072,0.00025594054,0.014338439,0.0019410516,0.01869633,0.0073097437,0.0024255672,0.23552808],"study_design_scores_gemma":[0.00002570586,0.0006415847,0.95469356,0.00011178039,0.00012837116,0.0006182718,0.009283147,0.010756728,0.00849066,0.0076629147,0.007451966,0.00013534215],"about_ca_topic_score_codex":0.00065801165,"about_ca_topic_score_gemma":0.0009657931,"teacher_disagreement_score":0.003876772,"about_ca_system_score_codex":0.0006829122,"about_ca_system_score_gemma":0.00042739022,"threshold_uncertainty_score":0.020502567},"labels":[],"label_agreement":null},{"id":"W2169494088","doi":"10.1007/978-3-642-13059-5_30","title":"Comparison of Feature Selection Methods for Sentiment Analysis","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Feature selection; Sentiment analysis; Selection (genetic algorithm); Computer science; Artificial intelligence; Feature (linguistics); Pattern recognition (psychology); Data mining; Machine learning; Linguistics; Philosophy","score_opus":0.03444674095231112,"score_gpt":0.3749346063561996,"score_spread":0.3404878654038884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169494088","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46639588,0.02205351,0.48876813,0.0009848807,0.001542557,0.00085000956,0.0034948322,0.005176771,0.0107335225],"genre_scores_gemma":[0.67872196,0.0059153773,0.2957228,0.0002482777,0.0006245655,0.00074861816,0.009555387,0.0005802255,0.007882816],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982838,0.0005692094,0.000193289,0.00017513004,0.0006370364,0.00014148098],"domain_scores_gemma":[0.99271625,0.005313403,0.00016162173,0.00027248904,0.0014165393,0.00011966724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043629496,0.0012684719,0.0016103642,0.0029922654,0.0005272804,0.0012867068,0.000914761,0.0007181516,0.0037479387],"category_scores_gemma":[0.008107631,0.00023782658,0.0013325581,0.0027642776,0.00020614869,0.0017703141,0.0006964755,0.0006941662,0.001069888],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003273119,0.0004160903,0.00497748,0.00068358035,0.0008253715,0.00007424926,0.000121873505,0.009475791,0.010951768,0.0007816261,0.010919032,0.9574999],"study_design_scores_gemma":[0.001284494,0.0037794588,0.06389916,0.00018814932,0.0019004338,0.0005889117,0.00075757754,0.87640136,0.031078735,0.005573822,0.014359101,0.00018882232],"about_ca_topic_score_codex":0.0018083921,"about_ca_topic_score_gemma":0.0021213172,"teacher_disagreement_score":0.0043629496,"about_ca_system_score_codex":0.0004116134,"about_ca_system_score_gemma":0.00054360746,"threshold_uncertainty_score":0.023073792},"labels":[],"label_agreement":null},{"id":"W2170657549","doi":"10.18653/v1/s15-1001","title":"Neural Networks for Integrating Compositional and Non-compositional Sentiment in Sentiment Composition","year":2015,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; National Research Council Canada","funders":"","keywords":"Treebank; Computer science; Composition (language); Sentiment analysis; Artificial intelligence; Process (computing); Artificial neural network; Principle of compositionality; Natural language processing; Parsing; Programming language","score_opus":0.0236320803186479,"score_gpt":0.277199808752499,"score_spread":0.2535677284338511,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170657549","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05258101,0.00042844008,0.9403489,0.00056182075,0.00007415439,0.000084522864,0.00007787606,0.00054721296,0.005296077],"genre_scores_gemma":[0.69948345,0.00047877152,0.29419833,0.00026279187,0.00011270936,0.00014492788,0.00025425327,0.00012376929,0.0049408837],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994356,0.00021255124,0.00003856188,0.0001253538,0.00013727554,0.000050597217],"domain_scores_gemma":[0.9993129,0.00031734415,0.00011267626,0.000062452484,0.0001661524,0.000028400342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018036239,0.0007597498,0.00041662157,0.00074790255,0.00048261276,0.0010823053,0.0007753068,0.00079760695,0.0017598489],"category_scores_gemma":[0.0041681044,0.00049375225,0.00069056085,0.0006467165,0.0006707464,0.0030026636,0.0009934225,0.0014641979,0.0005454489],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032798323,0.00021489133,0.0055490355,0.00022299703,0.0002971162,0.00014380191,0.0004888248,0.53832006,0.027577521,0.07161962,0.0037445454,0.3514936],"study_design_scores_gemma":[0.0000069107564,0.000023769131,0.00038644354,0.000012177236,0.000029885276,0.000013840184,0.000018926528,0.9745336,0.0025802148,0.021312317,0.0010725099,0.000009430051],"about_ca_topic_score_codex":0.0023270561,"about_ca_topic_score_gemma":0.0044292733,"teacher_disagreement_score":0.0023270561,"about_ca_system_score_codex":0.0009417632,"about_ca_system_score_gemma":0.000491274,"threshold_uncertainty_score":0.009538591},"labels":[],"label_agreement":null},{"id":"W2177825695","doi":"","title":"Feature Selection for Sentiment Analysis Based on Content and Syntax Models","year":2011,"lang":"en","type":"article","venue":"The Atrium (University of Guelph)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Sentiment analysis; Feature selection; Lexicon; Artificial intelligence; Salient; Entropy (arrow of time); Natural language processing; Classifier (UML); Feature (linguistics); Syntax; Principle of maximum entropy; Set (abstract data type); Selection (genetic algorithm); Machine learning","score_opus":0.051670308438059406,"score_gpt":0.22004033955643895,"score_spread":0.16837003111837956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2177825695","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08140466,0.0005011192,0.9116648,0.00043204366,0.00009323429,0.00038330094,0.0008370154,0.0025799894,0.002103825],"genre_scores_gemma":[0.6695674,0.00036424492,0.3236267,0.00014886184,0.0001947912,0.00069468055,0.0032635075,0.0001917756,0.0019481616],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990472,0.00035621456,0.000100889156,0.00014510508,0.0002780872,0.0000725128],"domain_scores_gemma":[0.99776244,0.0013116095,0.00018249697,0.00013125222,0.000568694,0.00004359399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002185048,0.00081427355,0.0008364612,0.0026844544,0.00042084372,0.000990056,0.0005525142,0.0005748536,0.0019757487],"category_scores_gemma":[0.0059333467,0.00019012323,0.0011005156,0.0015718918,0.00031292415,0.0013481504,0.00051768654,0.00080885744,0.0011415314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006110461,0.00030946973,0.009795151,0.00027214564,0.00021078537,0.00025687693,0.00021883809,0.02572737,0.05099406,0.005886761,0.01347166,0.8922458],"study_design_scores_gemma":[0.000103639,0.00023740271,0.008956366,0.000041919535,0.00013345933,0.00021449305,0.00012141091,0.94891703,0.021727875,0.014348469,0.0051477696,0.000050118506],"about_ca_topic_score_codex":0.00089904846,"about_ca_topic_score_gemma":0.0009567728,"teacher_disagreement_score":0.0026844544,"about_ca_system_score_codex":0.000548284,"about_ca_system_score_gemma":0.0005441067,"threshold_uncertainty_score":0.011555791},"labels":[],"label_agreement":null},{"id":"W2186073939","doi":"","title":"Sentiment Analysis of Social Media Texts","year":2014,"lang":"en","type":"article","venue":"Empirical Methods in Natural Language Processing","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"SemEval; Computer science; Sentiment analysis; Negation; Lexicon; Task (project management); Artificial intelligence; Natural language processing; Focus (optics); Social media; Scope (computer science); World Wide Web","score_opus":0.038254256486088135,"score_gpt":0.4293544643095399,"score_spread":0.39110020782345173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2186073939","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5346519,0.005686185,0.32335663,0.0042192875,0.002749317,0.0021352314,0.04133147,0.010760675,0.07510943],"genre_scores_gemma":[0.80016255,0.0036717243,0.14114128,0.00080885936,0.0014444458,0.00095120416,0.031687476,0.00094265316,0.019189838],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813807,0.00050850154,0.00020667542,0.00027232186,0.00076651387,0.00010788389],"domain_scores_gemma":[0.9966654,0.001207646,0.00041719884,0.00018892363,0.0014333704,0.000087451546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001756024,0.0008367223,0.00048677687,0.0036026118,0.00054984196,0.0018714106,0.00026348766,0.00039820172,0.0043041795],"category_scores_gemma":[0.0075598205,0.00021623334,0.00069717946,0.00192128,0.00032365735,0.0014873035,0.000667153,0.0005621551,0.0036708326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066176086,0.00019486595,0.030168315,0.0017421662,0.00044146428,0.00055951794,0.0017174825,0.004812416,0.105735615,0.0047141965,0.073814586,0.7754376],"study_design_scores_gemma":[0.00016614555,0.00091394095,0.18949047,0.0008176641,0.00053236977,0.0014809254,0.005723152,0.3212771,0.13272534,0.030515222,0.31607687,0.0002807767],"about_ca_topic_score_codex":0.00097339076,"about_ca_topic_score_gemma":0.0014701742,"teacher_disagreement_score":0.0043041795,"about_ca_system_score_codex":0.00047161747,"about_ca_system_score_gemma":0.00039728958,"threshold_uncertainty_score":0.014398873},"labels":[],"label_agreement":null},{"id":"W2191266841","doi":"10.1609/aaai.v28i1.8772","title":"Modeling Argumentation and Explanation in the Social Web","year":2014,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Argumentation theory; Computer science; Data science; Computational sociology; Social web; Social media; World Wide Web; Epistemology","score_opus":0.09124313674391449,"score_gpt":0.3195607372196051,"score_spread":0.2283176004756906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2191266841","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19230686,0.0016755066,0.7676877,0.007416172,0.00012698655,0.00033999182,0.00057898957,0.0004709357,0.029396845],"genre_scores_gemma":[0.87513596,0.00056684227,0.119125634,0.00020856601,0.000100997415,0.00034694548,0.00038334314,0.000051404742,0.004080352],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996461,0.0024157683,0.00018945496,0.00037344996,0.0003641676,0.00019615948],"domain_scores_gemma":[0.9843709,0.013263436,0.00093054515,0.00060835027,0.00046695708,0.00035979133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043539032,0.00062666944,0.0006215703,0.0026108515,0.001339704,0.004736288,0.0015112608,0.0031599249,0.0036889182],"category_scores_gemma":[0.01531716,0.0005822229,0.0016175468,0.0018673178,0.0029223203,0.0073439805,0.0027632562,0.0020080043,0.00035530308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000090304056,0.00015342643,0.0038875572,0.0001283018,0.00014920974,0.0005066019,0.0019969658,0.26405394,0.00042994664,0.70786446,0.0010129709,0.019726336],"study_design_scores_gemma":[0.000027662847,0.00001711185,0.0004766447,0.000028636903,0.000024482482,0.000055971952,0.0003581218,0.619552,0.00014544115,0.37575647,0.0035433527,0.000014073596],"about_ca_topic_score_codex":0.007430426,"about_ca_topic_score_gemma":0.006016674,"teacher_disagreement_score":0.007430426,"about_ca_system_score_codex":0.00216909,"about_ca_system_score_gemma":0.001285229,"threshold_uncertainty_score":0.02302587},"labels":[],"label_agreement":null},{"id":"W2250231408","doi":"","title":"Kea: Expression-level Sentiment Analysis from Twitter Data","year":2013,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Sentiment analysis; Computer science; SemEval; Task (project management); Artificial intelligence; Expression (computer science); Natural language processing; Machine learning","score_opus":0.09951221675890859,"score_gpt":0.3020970838637467,"score_spread":0.20258486710483808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2250231408","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26389462,0.0014589856,0.49248734,0.0014362928,0.0011495905,0.0028996153,0.089963324,0.12512825,0.021581903],"genre_scores_gemma":[0.4553126,0.0007411678,0.4618299,0.0004737845,0.00028670253,0.0019356906,0.0591135,0.00235858,0.017948147],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999012,0.00020388256,0.000115440875,0.00019977769,0.00036733178,0.000101660655],"domain_scores_gemma":[0.99852186,0.00045880902,0.00018698763,0.00024739612,0.00048666453,0.000098347235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014973711,0.0010426815,0.00078015553,0.0031453033,0.0007900335,0.0014434804,0.00063967955,0.000602738,0.0048796874],"category_scores_gemma":[0.004342899,0.00034041944,0.00067906105,0.0013860348,0.00024023962,0.0020771024,0.0010683999,0.0011142722,0.006767712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012165601,0.0007364778,0.034584507,0.0013936331,0.00053528574,0.00078542234,0.0013047355,0.004532655,0.25975347,0.0034417026,0.10052181,0.5911937],"study_design_scores_gemma":[0.0002162831,0.001162092,0.11520571,0.00022199968,0.00034477233,0.0020751941,0.0016123481,0.43725803,0.25538313,0.011263572,0.17475164,0.00050517806],"about_ca_topic_score_codex":0.0010459981,"about_ca_topic_score_gemma":0.0028038346,"teacher_disagreement_score":0.0048796874,"about_ca_system_score_codex":0.0004059606,"about_ca_system_score_gemma":0.0005715044,"threshold_uncertainty_score":0.016324162},"labels":[],"label_agreement":null},{"id":"W2250362335","doi":"","title":"Supervised Ranking of Co-occurrence Profiles for Acquisition of Continuous Lexical Attributes","year":2014,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Lexicon; Formality; Computer science; Ranking (information retrieval); Artificial intelligence; Natural language processing; Polarity (international relations); Feature (linguistics); Quality (philosophy); Feature vector; Machine learning; Linguistics","score_opus":0.03207408653465457,"score_gpt":0.2901249387621085,"score_spread":0.25805085222745394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2250362335","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14015028,0.00044267706,0.8472952,0.00013769229,0.00008209376,0.00037552175,0.002257428,0.0056604925,0.0035985287],"genre_scores_gemma":[0.52786404,0.00017992727,0.45994478,0.00006107506,0.00011375642,0.00053178816,0.008506988,0.0004394038,0.002358147],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99800724,0.0006827728,0.00020613168,0.00045961022,0.0005131882,0.00013097678],"domain_scores_gemma":[0.99401456,0.0028250036,0.0007234861,0.00076028577,0.0014349144,0.00024175347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014965514,0.00093758124,0.0012162472,0.005522106,0.0006850074,0.0015332651,0.0010631289,0.00083609624,0.0029513822],"category_scores_gemma":[0.007953367,0.0003146809,0.0007228098,0.0035131576,0.0005104654,0.0018159498,0.0010476629,0.0009034145,0.0039991727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00077303423,0.0006189059,0.01995908,0.00059208815,0.00030695487,0.00031427544,0.00041686342,0.013662353,0.1105298,0.0052329516,0.010973911,0.83661985],"study_design_scores_gemma":[0.00012785394,0.00037214183,0.02113956,0.000058748483,0.00012590837,0.0006663703,0.00037040474,0.9029678,0.0499086,0.01603368,0.008110832,0.000118150085],"about_ca_topic_score_codex":0.0017633152,"about_ca_topic_score_gemma":0.004920705,"teacher_disagreement_score":0.005522106,"about_ca_system_score_codex":0.0004478167,"about_ca_system_score_gemma":0.0013511414,"threshold_uncertainty_score":0.00987339},"labels":[],"label_agreement":null},{"id":"W2250466724","doi":"10.18653/v1/w15-2924","title":"Using Combined Lexical Resources to Identify Hashtag Types","year":2015,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Lexicon; Computer science; Sentiment analysis; Natural language processing; Artificial intelligence; Resource (disambiguation); Word (group theory); Information retrieval; Linguistics","score_opus":0.15231750124460752,"score_gpt":0.38110238308356004,"score_spread":0.22878488183895251,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2250466724","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50355095,0.0030549506,0.4519705,0.0007070671,0.00039293943,0.001034986,0.0058217165,0.0048997267,0.028567154],"genre_scores_gemma":[0.8104464,0.0008597074,0.178983,0.00020588771,0.00013675986,0.0003567773,0.0050983657,0.00021254388,0.003700588],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998381,0.00040845014,0.00019633063,0.0003017314,0.0005714738,0.00014093384],"domain_scores_gemma":[0.9952891,0.002509915,0.0004920545,0.0003606955,0.001199699,0.00014856797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015290283,0.0009630714,0.0009267958,0.0104026375,0.000730011,0.002265697,0.0006688381,0.00065678864,0.002847863],"category_scores_gemma":[0.009385267,0.00033770615,0.00076941535,0.003732481,0.000401926,0.005143289,0.0016603258,0.0006291766,0.0029443717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008563876,0.00036475147,0.08611175,0.0011153137,0.0005280411,0.00085954193,0.0008963497,0.0047101458,0.0878188,0.0049293763,0.0072114286,0.8045982],"study_design_scores_gemma":[0.000338234,0.0010804742,0.165351,0.0009975905,0.0016801349,0.0047361264,0.0047079944,0.56442785,0.15976512,0.044203267,0.05210232,0.0006099195],"about_ca_topic_score_codex":0.0031105177,"about_ca_topic_score_gemma":0.0067167636,"teacher_disagreement_score":0.0104026375,"about_ca_system_score_codex":0.00047045943,"about_ca_system_score_gemma":0.0007429541,"threshold_uncertainty_score":0.009527028},"labels":[],"label_agreement":null},{"id":"W2250473310","doi":"","title":"Can I Hear You? Sentiment Analysis on Medical Forums","year":2013,"lang":"en","type":"article","venue":"International Joint Conference on Natural Language Processing","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Lexicon; Sentiment analysis; Computer science; Annotation; Natural language processing; World Wide Web; Information retrieval; Artificial intelligence","score_opus":0.018991286940984495,"score_gpt":0.29480584887556693,"score_spread":0.2758145619345824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2250473310","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98340064,0.00036561184,0.0069116508,0.0005919447,0.0001844312,0.0001864117,0.0025135216,0.0001275206,0.005718272],"genre_scores_gemma":[0.9853695,0.00022727289,0.009963064,0.00014752318,0.00026944472,0.00017517024,0.0020435024,0.000046981262,0.0017575081],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979929,0.00093166577,0.0001889443,0.00019447946,0.0005116315,0.00018032492],"domain_scores_gemma":[0.98637277,0.007988187,0.0019851672,0.00027607576,0.0029592786,0.0004185534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033118152,0.00027383887,0.00033855267,0.0034952261,0.0008559614,0.0009251354,0.00019042676,0.00030300458,0.0013987907],"category_scores_gemma":[0.010728126,0.000085685744,0.00025089955,0.0017492883,0.00035185713,0.0008467372,0.00064946106,0.0003622691,0.00044455027],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022076322,0.00045573228,0.33429313,0.002001528,0.00022050219,0.0022079858,0.053809114,0.0015381919,0.18239026,0.003952674,0.02874043,0.38818276],"study_design_scores_gemma":[0.000076161086,0.00073470065,0.803687,0.00056392327,0.00020486195,0.0014171934,0.037609655,0.052415162,0.037168484,0.0054171444,0.06054847,0.00015722073],"about_ca_topic_score_codex":0.0009560575,"about_ca_topic_score_gemma":0.0015089895,"teacher_disagreement_score":0.0034952261,"about_ca_system_score_codex":0.00045093842,"about_ca_system_score_gemma":0.00027430896,"threshold_uncertainty_score":0.017514765},"labels":[],"label_agreement":null},{"id":"W2250562254","doi":"10.3115/v1/w14-5907","title":"Recognition of Sentiment Sequences in Online Discussions","year":2014,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Gratitude; Confusion; Lexicon; Computer science; Sentiment analysis; Agreement; Natural language processing; The Internet; Online discussion; Domain (mathematical analysis); Artificial intelligence; World Wide Web; Information retrieval; Linguistics; Psychology; Social psychology; Mathematics","score_opus":0.03490513771065714,"score_gpt":0.2862816338409228,"score_spread":0.2513764961302657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2250562254","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94204515,0.0005571155,0.03854325,0.00031696018,0.00021294643,0.000345692,0.004754825,0.00076119433,0.012462881],"genre_scores_gemma":[0.95309114,0.00031647139,0.037589904,0.00007828198,0.0002078453,0.00033223134,0.004673185,0.00008148706,0.0036294232],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9989004,0.0003680562,0.00011712322,0.0002046649,0.00030687705,0.000102754086],"domain_scores_gemma":[0.99537164,0.0020103576,0.001082218,0.00014296966,0.0011961972,0.00019662379],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012207585,0.00036286443,0.00024163265,0.002483883,0.00057671237,0.0009207569,0.00019212104,0.00040080666,0.0025917212],"category_scores_gemma":[0.0055528595,0.00013798107,0.00028316054,0.0013091685,0.00023087318,0.0010999019,0.0006650501,0.00029807692,0.0014553741],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017128055,0.0003307202,0.24379127,0.0013936403,0.00011466757,0.0020297961,0.01787426,0.0023145964,0.20011552,0.004908635,0.018241052,0.5071731],"study_design_scores_gemma":[0.0000675829,0.00073445006,0.7173173,0.00046556015,0.00015269905,0.0023935179,0.014028525,0.12418055,0.06066464,0.011003585,0.06883309,0.00015853632],"about_ca_topic_score_codex":0.0008166439,"about_ca_topic_score_gemma":0.0011896867,"teacher_disagreement_score":0.0025917212,"about_ca_system_score_codex":0.00034310267,"about_ca_system_score_gemma":0.0003042743,"threshold_uncertainty_score":0.008670211},"labels":[],"label_agreement":null},{"id":"W2250730878","doi":"10.3115/v1/w15-0509","title":"From Argumentation Mining to Stance Classification","year":2015,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":82,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Argument (complex analysis); Computer science; Argumentation theory; Artificial intelligence; Latent Dirichlet allocation; Classifier (UML); Topic model; Machine learning; Set (abstract data type); Natural language processing; Linguistics","score_opus":0.09989513788844828,"score_gpt":0.3264486296949984,"score_spread":0.2265534918065501,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2250730878","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023557013,0.0060863486,0.9552552,0.0054243617,0.00035231383,0.0001797719,0.0008279537,0.0011097285,0.00720723],"genre_scores_gemma":[0.33247182,0.004244042,0.65413,0.0009715858,0.0013620508,0.0003254754,0.0027666606,0.00035384766,0.0033745868],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9931126,0.003502363,0.0005472114,0.0011527516,0.0014281212,0.00025696712],"domain_scores_gemma":[0.9816892,0.013161455,0.0013996307,0.0015055796,0.0017673359,0.0004767329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057995785,0.001481882,0.0015218578,0.0065662675,0.0013708809,0.0056918007,0.001993001,0.00265628,0.0024370654],"category_scores_gemma":[0.026578374,0.000707493,0.0014184288,0.005498748,0.0019445488,0.008148167,0.0034615612,0.003398697,0.0018548727],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029250374,0.00038497837,0.0072493968,0.0010037156,0.00022680043,0.00040610705,0.0011171667,0.021751123,0.0050596804,0.1390374,0.018440176,0.8050309],"study_design_scores_gemma":[0.000054288317,0.00007137746,0.0024994188,0.00028425927,0.00006097696,0.0003437073,0.00040875128,0.3077752,0.004624104,0.6601362,0.023684077,0.000057613135],"about_ca_topic_score_codex":0.000733193,"about_ca_topic_score_gemma":0.00076536776,"teacher_disagreement_score":0.0065662675,"about_ca_system_score_codex":0.0012864597,"about_ca_system_score_gemma":0.0012948896,"threshold_uncertainty_score":0.030671477},"labels":[],"label_agreement":null},{"id":"W2250850523","doi":"10.18653/v1/w15-2916","title":"How much does word sense disambiguation help in sentiment analysis of micropost data?","year":2015,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Word-sense disambiguation; Sentiment analysis; Computer science; Word (group theory); SemEval; Natural language processing; Social media; Short Message Service; Microblogging; Artificial intelligence; Information retrieval; World Wide Web; Linguistics","score_opus":0.06536210261191946,"score_gpt":0.30477371756209676,"score_spread":0.2394116149501773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2250850523","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31113577,0.016798524,0.5749309,0.03824433,0.0047603203,0.0007768264,0.0072910353,0.01200185,0.034060404],"genre_scores_gemma":[0.5077355,0.0056311437,0.46828842,0.0039029273,0.0021812045,0.0003196747,0.0051636845,0.0015249795,0.0052524256],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99481875,0.002064016,0.0005168732,0.0011016307,0.0012027133,0.00029602463],"domain_scores_gemma":[0.98372364,0.008544595,0.0015153877,0.0018990577,0.0039602285,0.00035713267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009419648,0.0015732268,0.001747597,0.0052392664,0.0017226993,0.0057135983,0.0010037204,0.001364419,0.0031377266],"category_scores_gemma":[0.028347526,0.00067691744,0.0015731219,0.005058431,0.00126959,0.009867777,0.0016560552,0.0020366604,0.0075523932],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009840211,0.00030279715,0.036885682,0.0019423546,0.0006204264,0.00030475992,0.0031330676,0.0020567433,0.054513175,0.004924762,0.03426955,0.8600627],"study_design_scores_gemma":[0.00038236275,0.0011761608,0.111386985,0.0025833903,0.0016008208,0.0022651004,0.026072957,0.18071549,0.21217145,0.15545648,0.30515006,0.0010388456],"about_ca_topic_score_codex":0.0027225462,"about_ca_topic_score_gemma":0.004766232,"teacher_disagreement_score":0.009419648,"about_ca_system_score_codex":0.00074392546,"about_ca_system_score_gemma":0.0012031427,"threshold_uncertainty_score":0.04981649},"labels":[],"label_agreement":null},{"id":"W2250988804","doi":"","title":"Classification of Emotion Words in Russian and Romanian Languages","year":2009,"lang":"en","type":"article","venue":"Recent Advances in Natural Language Processing","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Children's Hospital of Eastern Ontario","funders":"","keywords":"Surprise; Sadness; Anger; WordNet; Disgust; Root (linguistics); Spelling; Computer science; Natural language processing; Artificial intelligence; Emotion classification; Word (group theory); Romanian; Psychology; Linguistics; Communication; Social psychology","score_opus":0.008528628059636015,"score_gpt":0.30523296177927073,"score_spread":0.2967043337196347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2250988804","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98849726,0.0006226515,0.0055244993,0.0001291813,0.00004199674,0.000022701977,0.00048692105,0.0000385008,0.00463629],"genre_scores_gemma":[0.9935666,0.00026286457,0.004369452,0.000028075812,0.000018238294,0.000023347564,0.0009976354,0.00002248431,0.00071141095],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992299,0.000353111,0.0001000767,0.00013761138,0.000106876265,0.00007245471],"domain_scores_gemma":[0.9986802,0.0006906112,0.00028072158,0.00006836435,0.00023983537,0.000040162045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066390703,0.00025627823,0.00025974872,0.0018293512,0.0003848037,0.0007631287,0.00017386596,0.00019977675,0.0011370796],"category_scores_gemma":[0.0029668375,0.00009151538,0.00038069027,0.0013053206,0.0004854683,0.00096366764,0.0005085873,0.0004229713,0.00042266233],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020205274,0.00029220685,0.42950547,0.0011481195,0.0003041065,0.0016353526,0.020790452,0.005994046,0.099381365,0.01742468,0.00438354,0.41712013],"study_design_scores_gemma":[0.00007304696,0.00069856824,0.8651155,0.00030006663,0.00020168202,0.0035816974,0.020539518,0.047418293,0.021388585,0.008212774,0.03235018,0.000120096425],"about_ca_topic_score_codex":0.0014250475,"about_ca_topic_score_gemma":0.0009235585,"teacher_disagreement_score":0.0018293512,"about_ca_system_score_codex":0.0003515631,"about_ca_system_score_gemma":0.00018064203,"threshold_uncertainty_score":0.0038039088},"labels":[],"label_agreement":null},{"id":"W2251031731","doi":"","title":"A System for Multilingual Sentiment Learning On Large Data Sets","year":2012,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Sentiment analysis; Artificial intelligence; Generalization; Natural language processing; Set (abstract data type); Empirical research; Machine learning; Mathematics","score_opus":0.12093133476971414,"score_gpt":0.3898499392076454,"score_spread":0.26891860443793125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2251031731","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07903781,0.00047014892,0.7322468,0.0012425137,0.00047611955,0.001371924,0.00760974,0.17055616,0.0069887266],"genre_scores_gemma":[0.15697362,0.00020408665,0.8238915,0.00043451812,0.00017651822,0.0008512212,0.010566245,0.0010057451,0.0058965827],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988078,0.0002683138,0.00015773295,0.00040737764,0.00028239656,0.00007631964],"domain_scores_gemma":[0.9970751,0.000998505,0.00020621074,0.00043144493,0.0011369141,0.00015181553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029487354,0.001304644,0.00090336625,0.002234167,0.0013758182,0.0013797343,0.0010654158,0.00095588894,0.008701602],"category_scores_gemma":[0.007303646,0.0005195507,0.0009276399,0.0017975474,0.0003030433,0.004067365,0.0020000928,0.0013587122,0.0071189376],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008020649,0.00042084392,0.006246318,0.0003947467,0.000229393,0.0003741218,0.0006288321,0.0028720335,0.054232996,0.003021212,0.06156396,0.86921346],"study_design_scores_gemma":[0.00029676215,0.0005559405,0.0101968525,0.00015843488,0.0002661491,0.0007444558,0.00086354354,0.808055,0.0795351,0.015449131,0.08369618,0.00018246945],"about_ca_topic_score_codex":0.0033380743,"about_ca_topic_score_gemma":0.0052548544,"teacher_disagreement_score":0.008701602,"about_ca_system_score_codex":0.0008092294,"about_ca_system_score_gemma":0.0009870073,"threshold_uncertainty_score":0.029109716},"labels":[],"label_agreement":null},{"id":"W2251088109","doi":"","title":"What Sentiments Can Be Found in Medical Forums","year":2013,"lang":"en","type":"article","venue":"Recent Advances in Natural Language Processing","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Sentiment analysis; Gratitude; Categorization; Computer science; Confusion; Class (philosophy); Natural language processing; Artificial intelligence; Information retrieval; Psychology; Social psychology","score_opus":0.010537265998960346,"score_gpt":0.31498434294585226,"score_spread":0.30444707694689194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2251088109","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9383897,0.0050681587,0.011333013,0.008086378,0.00070938445,0.00010754278,0.0034980755,0.00026533607,0.032542303],"genre_scores_gemma":[0.98995715,0.0012830772,0.004389472,0.00058095227,0.00058612996,0.000039114577,0.0008744371,0.00006927392,0.0022203068],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987036,0.000565619,0.00010687744,0.00015155012,0.00033948384,0.00013293832],"domain_scores_gemma":[0.98837364,0.006431619,0.0029271515,0.0003026506,0.0014353709,0.00052953284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002326291,0.00027669818,0.00028166888,0.0032003422,0.0006244402,0.0016975768,0.00012788728,0.00041086224,0.0033651679],"category_scores_gemma":[0.013619452,0.00014467395,0.00025973708,0.00135986,0.00047167117,0.002073872,0.0006217672,0.0003348061,0.00084802986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011339667,0.00022999008,0.47192508,0.001890041,0.00039596445,0.0014865096,0.024829648,0.00068017264,0.039733883,0.007558876,0.027730357,0.4224055],"study_design_scores_gemma":[0.00006920555,0.00048500087,0.81420213,0.0014243084,0.00038902886,0.0031860566,0.023558695,0.009420719,0.010132159,0.016875163,0.12008475,0.00017284606],"about_ca_topic_score_codex":0.00047758766,"about_ca_topic_score_gemma":0.0008732674,"teacher_disagreement_score":0.0033651679,"about_ca_system_score_codex":0.00033096576,"about_ca_system_score_gemma":0.00022096779,"threshold_uncertainty_score":0.012302756},"labels":[],"label_agreement":null},{"id":"W2251505590","doi":"","title":"Sentiments and Opinions in Health-related Web messages","year":2011,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Annotation; Task (project management); Subjectivity; Information retrieval; World Wide Web; Data science; Artificial intelligence","score_opus":0.03951874963260962,"score_gpt":0.2736335238129268,"score_spread":0.2341147741803172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2251505590","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8811661,0.0017135845,0.074094325,0.0017046444,0.00056845025,0.0004424559,0.008656758,0.00083338487,0.030820223],"genre_scores_gemma":[0.94190377,0.00070399884,0.044217385,0.00028554918,0.00035971508,0.00025021937,0.006442101,0.00014123703,0.005696053],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.998664,0.0004885102,0.000122016034,0.00019391383,0.0004543712,0.00007717459],"domain_scores_gemma":[0.9941743,0.0029877252,0.0010800888,0.00020510319,0.0014327068,0.00011997406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001245794,0.0004913208,0.00032330793,0.002142025,0.0005687734,0.0012259464,0.00017957985,0.00043560375,0.0032503325],"category_scores_gemma":[0.0071425186,0.00012224136,0.000399767,0.001295831,0.00032806623,0.0013468682,0.00045089072,0.00053506123,0.0009959637],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023654916,0.00065177144,0.18232217,0.0028043422,0.0004174632,0.0013296554,0.013103313,0.004587344,0.19814913,0.010588328,0.01880871,0.5648722],"study_design_scores_gemma":[0.000075933574,0.00080429285,0.6954223,0.0006613069,0.00073931034,0.0013363595,0.00904985,0.106222875,0.085742205,0.02101557,0.07869412,0.0002358925],"about_ca_topic_score_codex":0.0010471955,"about_ca_topic_score_gemma":0.00142856,"teacher_disagreement_score":0.0032503325,"about_ca_system_score_codex":0.00049410964,"about_ca_system_score_gemma":0.00026078697,"threshold_uncertainty_score":0.010873437},"labels":[],"label_agreement":null},{"id":"W2251564457","doi":"","title":"Opinion Learning from Medical Forums","year":2013,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Support vector machine; Computer science; Annotation; Classifier (UML); Artificial intelligence; Sentiment analysis; Kappa; Machine learning; Logistic regression; Natural language processing; Linguistics","score_opus":0.017957806556843425,"score_gpt":0.25202471358813755,"score_spread":0.23406690703129412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2251564457","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6991237,0.004022091,0.24969883,0.0046352777,0.0013645228,0.0011317077,0.016797595,0.0028381564,0.02038817],"genre_scores_gemma":[0.91163665,0.000676172,0.069550134,0.0003573765,0.0010753993,0.00034969498,0.013222037,0.00007053232,0.0030619532],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9960775,0.0017558408,0.0002828725,0.0005741034,0.0010330093,0.00027668153],"domain_scores_gemma":[0.9782719,0.016562363,0.0013695867,0.0007331931,0.002628804,0.00043419798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047448594,0.00095002237,0.0006262894,0.0044624214,0.00065705227,0.001682044,0.0007391921,0.0011395187,0.0029441842],"category_scores_gemma":[0.022992618,0.00019376256,0.0008090298,0.0016524768,0.00036485484,0.0025957539,0.0011261625,0.0009454425,0.0014637567],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017478605,0.0012022216,0.057401504,0.0014678835,0.0003458001,0.0015275893,0.0032315173,0.016031297,0.03921614,0.004705678,0.036194235,0.83692825],"study_design_scores_gemma":[0.0002409682,0.0013370403,0.08636357,0.0004874753,0.00029499558,0.0015467943,0.0044717425,0.7914088,0.027956935,0.027403733,0.05828674,0.00020110255],"about_ca_topic_score_codex":0.0009264699,"about_ca_topic_score_gemma":0.001264366,"teacher_disagreement_score":0.0047448594,"about_ca_system_score_codex":0.0007260287,"about_ca_system_score_gemma":0.00042986215,"threshold_uncertainty_score":0.025093496},"labels":[],"label_agreement":null},{"id":"W2251902771","doi":"","title":"Cross-Linguistic Sentiment Analysis: From English to Spanish","year":2009,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":189,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Machine translation; Natural language processing; Baseline (sea); Focus (optics); Sentiment analysis; Artificial intelligence; Adaptation (eye); English language; Support vector machine; Rule-based machine translation; Automation; Linguistics; Engineering; Psychology","score_opus":0.014831674243199543,"score_gpt":0.2908510878003644,"score_spread":0.27601941355716486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2251902771","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8956769,0.0024429364,0.04276853,0.0017068161,0.0005341497,0.00024803248,0.003702928,0.0010008519,0.051918786],"genre_scores_gemma":[0.95474476,0.0013382818,0.027935196,0.00049928954,0.00020364736,0.00023038294,0.00658309,0.00039888726,0.008066479],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991565,0.00044118532,0.00006571677,0.00011970543,0.00012720637,0.00008972152],"domain_scores_gemma":[0.9969126,0.0012491212,0.00019349034,0.00027119103,0.0012375077,0.00013608606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017507193,0.0005768467,0.00035115512,0.001219148,0.000667679,0.0017154712,0.00034272528,0.00034987862,0.0060142814],"category_scores_gemma":[0.0071827485,0.000119583296,0.00051702355,0.0015889185,0.00030970774,0.0012966397,0.001868399,0.0005296317,0.0026573844],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016285876,0.0012874812,0.14161776,0.0010968309,0.00042003705,0.0016105475,0.009789336,0.0050206,0.06752093,0.006213369,0.03702383,0.72677064],"study_design_scores_gemma":[0.00037356693,0.0011202445,0.5206388,0.00071151444,0.0008919072,0.0022963663,0.033458307,0.10260112,0.0854373,0.021251896,0.23092473,0.00029429488],"about_ca_topic_score_codex":0.008041213,"about_ca_topic_score_gemma":0.00710807,"teacher_disagreement_score":0.008041213,"about_ca_system_score_codex":0.0005445142,"about_ca_system_score_gemma":0.00036017312,"threshold_uncertainty_score":0.020119786},"labels":[],"label_agreement":null},{"id":"W2252057809","doi":"10.3115/v1/s14-2076","title":"NRC-Canada-2014: Detecting Aspects and Sentiment in Customer Reviews","year":2014,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":709,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sentiment analysis; Computer science; Data science; Information retrieval; Natural language processing","score_opus":0.014155768735438393,"score_gpt":0.24017742150054788,"score_spread":0.2260216527651095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2252057809","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39431268,0.013743587,0.090910785,0.009736367,0.0062348195,0.005414748,0.38131592,0.050014183,0.04831687],"genre_scores_gemma":[0.20513283,0.002222277,0.15369073,0.0013815227,0.00069960114,0.0011325802,0.5669489,0.0028880902,0.06590348],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99577326,0.00095865584,0.0002533856,0.0007716324,0.0018216806,0.00042136773],"domain_scores_gemma":[0.9889686,0.0015037954,0.00035865474,0.00089108065,0.0072012655,0.0010765863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035367734,0.0030269627,0.0016065049,0.0042708167,0.0021273587,0.003981858,0.0022421724,0.0025004167,0.004695818],"category_scores_gemma":[0.013933418,0.00080982764,0.0011537282,0.0036094189,0.00074885925,0.0019108305,0.0018688987,0.0020644236,0.0050791767],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011185469,0.000666943,0.027000362,0.0013904567,0.00062863686,0.00063674315,0.0009957199,0.0025093732,0.017727392,0.0014590084,0.7739741,0.17189272],"study_design_scores_gemma":[0.0010659983,0.0010653596,0.22965753,0.00056027336,0.00061731244,0.0015700836,0.0038607318,0.17351651,0.034140166,0.0035713592,0.5498543,0.00052036485],"about_ca_topic_score_codex":0.4546265,"about_ca_topic_score_gemma":0.6655841,"teacher_disagreement_score":0.5453735,"about_ca_system_score_codex":0.0053235907,"about_ca_system_score_gemma":0.011225819,"threshold_uncertainty_score":0.9039606},"labels":[],"label_agreement":null},{"id":"W2266481775","doi":"10.1007/978-3-319-25252-0_19","title":"Tweets as a Vote: Exploring Political Sentiments on Twitter for Opinion Mining","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Sentiment analysis; Process (computing); Suspect; Data science; Order (exchange); Set (abstract data type); Domain (mathematical analysis); Aggregate (composite); Information retrieval; Artificial intelligence; Political science","score_opus":0.11443245735284817,"score_gpt":0.33066652893245413,"score_spread":0.21623407157960595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2266481775","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6548476,0.0037713118,0.2555677,0.002940775,0.0011458349,0.00066889677,0.045859236,0.0062884367,0.028910227],"genre_scores_gemma":[0.8144962,0.0014937043,0.13496135,0.00028687983,0.0009871334,0.00061273033,0.033059556,0.00054229645,0.013560211],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970764,0.00007988474,0.000020402696,0.000058332105,0.00009026515,0.000043548956],"domain_scores_gemma":[0.9994041,0.00033815426,0.0000676006,0.00003562594,0.000108504544,0.000046012203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049704406,0.000763261,0.00043721625,0.0019433256,0.00048163044,0.0014632703,0.00045698494,0.00049675565,0.003756495],"category_scores_gemma":[0.0022745598,0.00023510263,0.0007904661,0.0021204764,0.0001378514,0.0013940174,0.00075508066,0.000675276,0.0033910307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009277998,0.00056144013,0.07762177,0.00066392974,0.00049800595,0.0004980977,0.0012812334,0.00843577,0.0310337,0.005356173,0.089379996,0.7837422],"study_design_scores_gemma":[0.00009188825,0.00042377127,0.09804018,0.00018169911,0.0005160093,0.000629512,0.0042293537,0.769571,0.026731543,0.029904885,0.06954439,0.00013577976],"about_ca_topic_score_codex":0.0014266173,"about_ca_topic_score_gemma":0.0036879529,"teacher_disagreement_score":0.003756495,"about_ca_system_score_codex":0.00021707118,"about_ca_system_score_gemma":0.00021628983,"threshold_uncertainty_score":0.012566745},"labels":[],"label_agreement":null},{"id":"W2274912527","doi":"10.1613/jair.4787","title":"How Translation Alters Sentiment","year":2016,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Research","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":194,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; National Research Council Canada","funders":"","keywords":"Sentiment analysis; Computer science; Focus (optics); Lexicon; Natural language processing; Annotation; Artificial intelligence; Arabic; Machine translation; Linguistics; Social media; Resource (disambiguation); World Wide Web","score_opus":0.2633291444881119,"score_gpt":0.42435148853026977,"score_spread":0.16102234404215787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2274912527","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45776272,0.0023747021,0.115009256,0.02060382,0.0052713063,0.00047829672,0.0031214633,0.004523551,0.39085498],"genre_scores_gemma":[0.91668046,0.0017842898,0.031918477,0.0030713438,0.00064894935,0.00021309369,0.0022067423,0.0024727567,0.041003805],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9977748,0.0010680503,0.0001317173,0.00033062792,0.0005039575,0.00019087817],"domain_scores_gemma":[0.9953225,0.001418985,0.00033592418,0.0007901826,0.0019994613,0.00013283936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023941374,0.0006790144,0.00035522843,0.0009247794,0.0011730717,0.0047710454,0.00042636736,0.00070977997,0.01702052],"category_scores_gemma":[0.014749013,0.00042874945,0.0005320505,0.0012545019,0.0014630515,0.0035189162,0.0015298941,0.0012963761,0.0127033405],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00083097944,0.0002845692,0.023545807,0.0013873908,0.00021177216,0.001993182,0.029778084,0.0036643576,0.11334377,0.0991704,0.10189084,0.6238989],"study_design_scores_gemma":[0.00019872656,0.00040353538,0.050124668,0.0006311639,0.0003306621,0.0020713017,0.024971815,0.034844875,0.08888031,0.1564288,0.6408977,0.00021646854],"about_ca_topic_score_codex":0.0023195138,"about_ca_topic_score_gemma":0.002002365,"teacher_disagreement_score":0.01702052,"about_ca_system_score_codex":0.001279678,"about_ca_system_score_gemma":0.00090314,"threshold_uncertainty_score":0.056939304},"labels":[],"label_agreement":null},{"id":"W2276491879","doi":"10.1109/icdmw.2015.64","title":"Sentiment-Based Identification of Radical Authors (SIRA)","year":2015,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"International Centre for Comparative Criminology","funders":"","keywords":"Context (archaeology); Sentiment analysis; Computer science; Typology; Identification (biology); World Wide Web; Islam; Natural language processing; History","score_opus":0.04551268505535085,"score_gpt":0.2969205164148089,"score_spread":0.25140783135945804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2276491879","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9372547,0.0003440371,0.04259467,0.0003118291,0.00026632784,0.0006909633,0.0047376934,0.0009159754,0.012883687],"genre_scores_gemma":[0.9186762,0.00025099464,0.072124876,0.00011088028,0.00021647407,0.00041124228,0.003488514,0.00013221367,0.004588551],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813443,0.00046912,0.00028455304,0.0003320619,0.00064562715,0.00013428777],"domain_scores_gemma":[0.9889297,0.0041078213,0.0020100635,0.0005390785,0.004041924,0.0003714542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034355088,0.00062297314,0.0004857386,0.006183462,0.0008261695,0.0019373243,0.0004266341,0.00046093418,0.0020229213],"category_scores_gemma":[0.011262431,0.00023429081,0.00046548274,0.002672846,0.00035073856,0.0015143306,0.001183136,0.00053350086,0.0017032577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010973199,0.0003635793,0.46798965,0.0011752814,0.0002458991,0.0005463758,0.0072519146,0.0012192368,0.10565453,0.0035469143,0.0124790855,0.39843014],"study_design_scores_gemma":[0.00011401526,0.0008214228,0.7199383,0.00026532586,0.00048854813,0.0015379572,0.0119744195,0.12431947,0.077151954,0.0081244195,0.05499773,0.00026650686],"about_ca_topic_score_codex":0.0007355767,"about_ca_topic_score_gemma":0.0021678198,"teacher_disagreement_score":0.006183462,"about_ca_system_score_codex":0.00046129862,"about_ca_system_score_gemma":0.0005656581,"threshold_uncertainty_score":0.018168926},"labels":[],"label_agreement":null},{"id":"W2293441282","doi":"","title":"Aspect-Level Sentiment Analysis Based on a Generalized Probabilistic Topic and Syntax Model","year":2015,"lang":"en","type":"article","venue":"The Atrium (University of Guelph)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Sentiment analysis; Computer science; Natural language processing; Artificial intelligence; Syntax; Topic model; Feature selection; Part of speech; Probabilistic logic; Principle of maximum entropy; Classifier (UML); Entropy (arrow of time); Semantic analysis (machine learning)","score_opus":0.05093195286255131,"score_gpt":0.24104752923978673,"score_spread":0.19011557637723542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2293441282","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0300247,0.00024995403,0.9666254,0.0003309948,0.000048358022,0.00013093014,0.00027268048,0.00062701415,0.0016901023],"genre_scores_gemma":[0.69053984,0.0005685289,0.30281565,0.00024428222,0.00026207056,0.00046361526,0.0012580791,0.00023861809,0.0036092936],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988518,0.0004210017,0.0000816733,0.0002727831,0.00029123807,0.00008148859],"domain_scores_gemma":[0.99824834,0.00093189,0.0001944354,0.00015168979,0.00041474684,0.000058987647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019570366,0.00065686344,0.0009155584,0.0021435716,0.0005237275,0.0016051739,0.0008942125,0.00059883174,0.0015582592],"category_scores_gemma":[0.004470729,0.00041285774,0.0016608821,0.0014597977,0.00063202094,0.002995323,0.00074157305,0.0011616423,0.0008247913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005976136,0.0003345788,0.024428006,0.0005943009,0.00073060475,0.00054161437,0.001778524,0.19487949,0.05036178,0.10299706,0.012409836,0.61034656],"study_design_scores_gemma":[0.000014305368,0.000042215448,0.0030153352,0.000013672659,0.000054335458,0.00009833131,0.00007064729,0.9725954,0.0013233433,0.020814164,0.0019314043,0.000026828706],"about_ca_topic_score_codex":0.003414276,"about_ca_topic_score_gemma":0.004606652,"teacher_disagreement_score":0.003414276,"about_ca_system_score_codex":0.00088633463,"about_ca_system_score_gemma":0.00083051383,"threshold_uncertainty_score":0.010349989},"labels":[],"label_agreement":null},{"id":"W2294579401","doi":"10.1007/978-3-319-24282-8_21","title":"Evaluating the Effectiveness of Hashtags as Predictors of the Sentiment of Tweets","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Sentiment analysis; Computer science; Natural language processing; Psychology","score_opus":0.044643203631588024,"score_gpt":0.3243244597088126,"score_spread":0.27968125607722455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2294579401","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98798954,0.0014029284,0.0056939754,0.000294117,0.00019071583,0.000065221815,0.0017618249,0.00045590848,0.0021457751],"genre_scores_gemma":[0.9882648,0.00040982864,0.0063129137,0.00004274571,0.00015293906,0.000032863154,0.0035669967,0.000038123017,0.001178762],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99829715,0.0007850387,0.00014232378,0.00025537907,0.00036610715,0.00015410283],"domain_scores_gemma":[0.9726299,0.02410384,0.0007974141,0.0005606158,0.0012800758,0.000628239],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045570782,0.0012168834,0.00080374157,0.0019427948,0.00036912606,0.0013770347,0.00052798545,0.0012326878,0.0013486409],"category_scores_gemma":[0.013944127,0.00026523156,0.0006279728,0.0013912388,0.0002897426,0.0019259718,0.0006223758,0.0009615518,0.0013166901],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.014980623,0.0037683176,0.3884425,0.001432994,0.0017749215,0.00032830413,0.0007258239,0.06136575,0.036319196,0.00090839627,0.018270588,0.47168264],"study_design_scores_gemma":[0.0003194034,0.00471407,0.099626586,0.000079815974,0.0007245284,0.00020127007,0.0006651234,0.86754704,0.022674039,0.0015759474,0.001777885,0.000094349845],"about_ca_topic_score_codex":0.00256352,"about_ca_topic_score_gemma":0.0030140982,"teacher_disagreement_score":0.0045570782,"about_ca_system_score_codex":0.00033671936,"about_ca_system_score_gemma":0.00046194394,"threshold_uncertainty_score":0.024100423},"labels":[],"label_agreement":null},{"id":"W2294703018","doi":"","title":"Emotional Tweets","year":2012,"lang":"en","type":"article","venue":"Joint Conference on Lexical and Computational Semantics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":300,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"WordNet; Lexicon; Microblogging; Computer science; Social media; Natural language processing; Artificial intelligence; Task (project management); Sentiment analysis; Affect (linguistics); Emotion detection; Emotion classification; Word (group theory); Domain (mathematical analysis); Semantics (computer science); Emotion recognition; World Wide Web; Psychology; Linguistics","score_opus":0.07082908098884917,"score_gpt":0.2884091368977245,"score_spread":0.21758005590887536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2294703018","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3999804,0.0020778629,0.10564668,0.00560809,0.0043362086,0.0029683816,0.13792779,0.007328215,0.33412644],"genre_scores_gemma":[0.7764744,0.0014424154,0.05324656,0.0016672349,0.0013515058,0.0021119323,0.07527221,0.0008054509,0.08762823],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991916,0.00019519831,0.00008602962,0.00014899542,0.00028199417,0.00009628008],"domain_scores_gemma":[0.99823344,0.00065096957,0.00021938702,0.00016019531,0.00063943275,0.00009661433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057922077,0.0005612195,0.00023896672,0.0018168074,0.0008214273,0.0014659181,0.0002781365,0.00050420733,0.01880561],"category_scores_gemma":[0.0045553567,0.00020491934,0.00030850392,0.0013730302,0.0002287517,0.001277873,0.0009358565,0.0005779634,0.010681178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015863942,0.00042299952,0.06185696,0.0020150333,0.00016550136,0.00093255175,0.005013545,0.0027132062,0.077385835,0.020244576,0.28274757,0.54491585],"study_design_scores_gemma":[0.00009291748,0.00044335052,0.13322969,0.00035869118,0.00025036454,0.001280407,0.0059722206,0.027606223,0.059344877,0.017999938,0.753235,0.00018628647],"about_ca_topic_score_codex":0.0009989315,"about_ca_topic_score_gemma":0.0021566893,"teacher_disagreement_score":0.01880561,"about_ca_system_score_codex":0.00046922648,"about_ca_system_score_gemma":0.00030239942,"threshold_uncertainty_score":0.06291109},"labels":[],"label_agreement":null},{"id":"W2295182362","doi":"10.3166/isi.20.4.63-84","title":"Une approche de filtrage d’opinions à base de crédibilité dans un contexte de réseaux sociaux","year":2015,"lang":"fr","type":"article","venue":"Ingénierie des systèmes d information","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Humanities; Computer science; Philosophy","score_opus":0.04588700816953806,"score_gpt":0.2828475182366679,"score_spread":0.23696051006712987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295182362","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03626246,0.00065906945,0.9435546,0.002132253,0.00039539617,0.00033648201,0.0011744467,0.0068593416,0.008625953],"genre_scores_gemma":[0.33385617,0.00057132856,0.6493006,0.00044693335,0.00029994757,0.00028589077,0.002335561,0.00025499286,0.012648578],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99684274,0.000600864,0.00020821126,0.0007663987,0.0013692032,0.00021262687],"domain_scores_gemma":[0.9954189,0.0020066116,0.00025512505,0.0007684642,0.0013304336,0.00022041069],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002620136,0.0010258631,0.0011106102,0.002994937,0.0016785908,0.004580556,0.00211504,0.0026406895,0.007087497],"category_scores_gemma":[0.01110262,0.00050488574,0.0017227788,0.0021262222,0.00077155343,0.0055319057,0.0019664078,0.0027010066,0.0030502437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00091552595,0.00066122814,0.0076369,0.00053625664,0.000328704,0.0007146268,0.002015802,0.029402971,0.015641436,0.048963748,0.023949616,0.8692332],"study_design_scores_gemma":[0.0001853224,0.0002649792,0.0045966413,0.00029054104,0.0002572257,0.00068035256,0.0010666413,0.85727,0.017409855,0.04824611,0.069596216,0.00013615385],"about_ca_topic_score_codex":0.012006111,"about_ca_topic_score_gemma":0.0142732505,"teacher_disagreement_score":0.012006111,"about_ca_system_score_codex":0.0012162235,"about_ca_system_score_gemma":0.0014309884,"threshold_uncertainty_score":0.023872435},"labels":[],"label_agreement":null},{"id":"W2295652631","doi":"10.13053/rcs-85-1-1","title":"Bilingual and Cross Domain Politics Analysis","year":2014,"lang":"en","type":"article","venue":"Research in Computing Science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Micralyne","funders":"Agence Nationale de la Recherche","keywords":"Politics; Domain (mathematical analysis); Political science; Mathematics; Law","score_opus":0.07652549299388199,"score_gpt":0.4573049371530154,"score_spread":0.3807794441591334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295652631","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7260719,0.00079397275,0.18898596,0.00077675836,0.00019090295,0.00019030146,0.004855278,0.001166562,0.07696823],"genre_scores_gemma":[0.953926,0.00020337205,0.031824775,0.0001646759,0.000095803116,0.00014265954,0.005453538,0.0002579454,0.007931276],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99857235,0.0007045932,0.00006106711,0.00028382966,0.00017916181,0.00019901409],"domain_scores_gemma":[0.9980453,0.0006637837,0.00018259292,0.00024507823,0.0007216988,0.00014149709],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019031768,0.00031548142,0.00035104767,0.0030439775,0.0010364042,0.0015630405,0.00028525482,0.0003864121,0.009230641],"category_scores_gemma":[0.004472085,0.00018320266,0.00054602895,0.00227628,0.00040797447,0.001262179,0.0014074723,0.00054359634,0.0030030694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026812302,0.000868273,0.15930127,0.00064451114,0.00051697076,0.002851536,0.0058199163,0.015699387,0.08449415,0.07004432,0.033196665,0.6238818],"study_design_scores_gemma":[0.00021247956,0.000555321,0.3218637,0.00019305134,0.00042929908,0.0026739275,0.018708749,0.3207751,0.06275666,0.0898934,0.18175898,0.0001793335],"about_ca_topic_score_codex":0.0032525517,"about_ca_topic_score_gemma":0.0037662974,"teacher_disagreement_score":0.009230641,"about_ca_system_score_codex":0.00068795704,"about_ca_system_score_gemma":0.0006916022,"threshold_uncertainty_score":0.030879557},"labels":[],"label_agreement":null},{"id":"W2295710275","doi":"10.3115/v1/n15-1078","title":"Sentiment after Translation: A Case-Study on Arabic Social Media Posts","year":2015,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":146,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of Alberta","funders":"","keywords":"Arabic; Computer science; Social media; Natural language processing; Computational linguistics; Linguistics; Artificial intelligence; Machine translation; World Wide Web; Philosophy","score_opus":0.09888186726437186,"score_gpt":0.31321616528342416,"score_spread":0.2143342980190523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295710275","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97409004,0.0003540294,0.0048667793,0.002028874,0.0002189966,0.0002240756,0.0005311588,0.00012659945,0.017559381],"genre_scores_gemma":[0.9826177,0.00052292936,0.0070664613,0.00056208245,0.00014316497,0.00009349236,0.0008017088,0.00015802743,0.008034458],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9983254,0.00094166124,0.000095581454,0.00012446202,0.00035983487,0.00015307077],"domain_scores_gemma":[0.99105597,0.004869569,0.00087261206,0.0004866531,0.0024271354,0.00028804876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022382108,0.00045976278,0.00023339962,0.0010773075,0.0024181954,0.0018702291,0.00046712696,0.0011196614,0.0043289238],"category_scores_gemma":[0.01287425,0.0001504767,0.00026449672,0.0017958747,0.0007587933,0.0019090727,0.0009972545,0.00070151256,0.0021779682],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017260573,0.0022039479,0.12852989,0.0019450941,0.00012083038,0.048030246,0.27691087,0.00158031,0.038867556,0.0079835905,0.037017245,0.45508444],"study_design_scores_gemma":[0.00015992633,0.0017094285,0.19168046,0.0009425195,0.00037750805,0.026305202,0.43218082,0.030056486,0.05616575,0.008549333,0.2516165,0.00025609415],"about_ca_topic_score_codex":0.0030142583,"about_ca_topic_score_gemma":0.004713076,"teacher_disagreement_score":0.0043289238,"about_ca_system_score_codex":0.0006590896,"about_ca_system_score_gemma":0.0007241453,"threshold_uncertainty_score":0.014481723},"labels":[],"label_agreement":null},{"id":"W2321728098","doi":"10.18178/ijlll.2015.1.3.32","title":"Tracking Emotions in Hot Topics: Exploiting Event-Specific Emotional Words","year":2015,"lang":"en","type":"article","venue":"International Journal of Languages Literature and Linguistics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Institute for Cancer Research","funders":"National Natural Science Foundation of China","keywords":"Event (particle physics); Tracking (education); Cognitive psychology; Psychology; Computer science; Emotion detection; Natural language processing; Artificial intelligence; Emotion recognition; Astrophysics","score_opus":0.030830888818105577,"score_gpt":0.32238898457406817,"score_spread":0.2915580957559626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2321728098","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7641498,0.0026080362,0.21853815,0.00054946047,0.0004665757,0.00033263004,0.0023719256,0.0013531849,0.009630222],"genre_scores_gemma":[0.94599706,0.0006450274,0.048610654,0.00012012655,0.0003125688,0.000120076256,0.001932927,0.00007174904,0.0021897848],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999418,0.00010953681,0.000061043334,0.00021496518,0.00012216241,0.000074320305],"domain_scores_gemma":[0.9984835,0.0006505684,0.00030138157,0.00009562476,0.00039427803,0.00007473418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005558369,0.0007263346,0.0005826517,0.002429246,0.00037993124,0.0012272818,0.0004769908,0.00064018974,0.00077773386],"category_scores_gemma":[0.0025019015,0.00018096389,0.0006463071,0.0014080751,0.0002457394,0.0018760687,0.0006620169,0.0006407358,0.0010914146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012626541,0.0009171788,0.11818095,0.0006702184,0.0004237438,0.0010404639,0.0027762474,0.0060132043,0.14471835,0.0025865368,0.011588241,0.70982224],"study_design_scores_gemma":[0.00008764587,0.00095904863,0.27287322,0.00018535212,0.0008455418,0.0020937163,0.004699128,0.61294276,0.06660296,0.01615507,0.022372646,0.00018292824],"about_ca_topic_score_codex":0.0008779421,"about_ca_topic_score_gemma":0.0015096623,"teacher_disagreement_score":0.002429246,"about_ca_system_score_codex":0.00022289506,"about_ca_system_score_gemma":0.00019017907,"threshold_uncertainty_score":0.0029396415},"labels":[],"label_agreement":null},{"id":"W2340160601","doi":"10.1145/2911451.2911490","title":"<i>Retracted May 3, 2018:</i> Transfer Learning for Cross-Lingual Sentiment Classification with Weakly Shared Deep Neural Networks","year":2016,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Artificial neural network; Transfer of learning; Artificial intelligence; Transfer (computing); Deep learning; Natural language processing; Deep neural networks; Sentiment analysis; Machine learning; Parallel computing","score_opus":0.027669109612380765,"score_gpt":0.2797963566478073,"score_spread":0.2521272470354265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2340160601","genre_codex":"editorial","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0075646346,0.0029934412,0.096662216,0.17930305,0.35554984,0.0010626393,0.08517183,0.10461024,0.1670822],"genre_scores_gemma":[0.058137532,0.0018132983,0.039628465,0.029328149,0.025005793,0.00084724097,0.12797076,0.02576249,0.69150627],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967939,0.00033204095,0.00019348114,0.00044246737,0.0017058791,0.0005322242],"domain_scores_gemma":[0.98456603,0.001346396,0.00024292435,0.002139075,0.010153177,0.0015524022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046910015,0.0018830595,0.0015956843,0.0023926788,0.0036525475,0.0062715453,0.004097463,0.0037734788,0.19353217],"category_scores_gemma":[0.027954588,0.00082452694,0.0017578949,0.0020978604,0.0014978916,0.006169474,0.005919455,0.007463783,0.20416859],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000070113885,0.000018203047,0.00016821163,0.000043026685,0.000010839371,0.000073334784,0.000030966523,0.00017241244,0.000626564,0.0005497943,0.9846635,0.013572967],"study_design_scores_gemma":[0.00008854707,0.000073729374,0.0013485649,0.00012989697,0.000030986255,0.00021784501,0.00011471844,0.008912275,0.0059932065,0.0067040036,0.97629935,0.000086943306],"about_ca_topic_score_codex":0.016855516,"about_ca_topic_score_gemma":0.035296783,"teacher_disagreement_score":0.19353217,"about_ca_system_score_codex":0.0027510815,"about_ca_system_score_gemma":0.0038431734,"threshold_uncertainty_score":0.6474296},"labels":[],"label_agreement":null},{"id":"W2341303028","doi":"10.1007/978-3-319-24282-8_4","title":"No Sentiment is an Island:","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Sentiment analysis; Class (philosophy); Natural language processing; Artificial intelligence; Empirical research; Information retrieval; World Wide Web","score_opus":0.035289028624197576,"score_gpt":0.27978056681490887,"score_spread":0.2444915381907113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2341303028","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43086863,0.004413329,0.25606892,0.018929085,0.0029532446,0.00033085953,0.0013516124,0.0013419826,0.28374228],"genre_scores_gemma":[0.93771267,0.0010973263,0.032454174,0.0026846856,0.0010432083,0.00011928005,0.0007901736,0.00033219915,0.023766253],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999127,0.00019038586,0.00004097414,0.00029482532,0.0002621006,0.000084760024],"domain_scores_gemma":[0.9975999,0.0009979289,0.00037197763,0.00026871794,0.0005429241,0.00021863934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014651547,0.00042306606,0.0005612254,0.0009106745,0.0014071998,0.0032769346,0.0005757572,0.00081259693,0.006272809],"category_scores_gemma":[0.0057141753,0.0002348779,0.00042360707,0.0009729475,0.0017358764,0.0059168437,0.0014473014,0.0013484881,0.0018303049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007445552,0.00021907045,0.07419045,0.001014678,0.00031980555,0.001345065,0.011954649,0.0019971845,0.020144003,0.2809614,0.051084567,0.5560246],"study_design_scores_gemma":[0.000068094276,0.00039651277,0.093194924,0.00077651715,0.00048600943,0.003389869,0.015517834,0.06514648,0.008846989,0.4812045,0.33080855,0.0001637043],"about_ca_topic_score_codex":0.0012620192,"about_ca_topic_score_gemma":0.001611777,"teacher_disagreement_score":0.006272809,"about_ca_system_score_codex":0.0005084935,"about_ca_system_score_gemma":0.00047424363,"threshold_uncertainty_score":0.02098471},"labels":[],"label_agreement":null},{"id":"W2344499866","doi":"10.3233/web-160333","title":"Predicting political conflicts from polarized social media","year":2016,"lang":"en","type":"article","venue":"Web Intelligence","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Politics; Social media; Political science; Political economy; Social psychology; Sociology; Psychology; Law","score_opus":0.04654038883628945,"score_gpt":0.28706160598265235,"score_spread":0.2405212171463629,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2344499866","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98451865,0.0004421682,0.0066680526,0.00047002936,0.000074586715,0.00008887006,0.0013523265,0.00009147694,0.006293798],"genre_scores_gemma":[0.9946519,0.00016354384,0.003025127,0.00005714768,0.0001590107,0.00005300723,0.0013850073,0.000007945857,0.00049731805],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990127,0.00029744304,0.00007630662,0.000101191516,0.00033568862,0.00017658163],"domain_scores_gemma":[0.9940288,0.0031623335,0.0013226491,0.0001758472,0.00096578634,0.00034456918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014402234,0.00061303965,0.00049852184,0.0067804973,0.00088158227,0.0021543435,0.00036241882,0.00092707615,0.0012924315],"category_scores_gemma":[0.007229074,0.00023700442,0.00035095363,0.0029274279,0.000520267,0.0017831365,0.0011858642,0.00080447906,0.0007613516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019893232,0.000758711,0.7027613,0.0003722355,0.00039665584,0.0021468233,0.0028398533,0.018009558,0.01722389,0.006317535,0.012200605,0.23498352],"study_design_scores_gemma":[0.0000831164,0.00035088445,0.39732775,0.00013677569,0.00025169546,0.0010924616,0.010208342,0.5451266,0.010599562,0.019373,0.015353505,0.000096235184],"about_ca_topic_score_codex":0.0019081542,"about_ca_topic_score_gemma":0.002219133,"teacher_disagreement_score":0.0067804973,"about_ca_system_score_codex":0.00055918464,"about_ca_system_score_gemma":0.0003008509,"threshold_uncertainty_score":0.0076167583},"labels":[],"label_agreement":null},{"id":"W2346770464","doi":"10.1007/978-3-319-73618-1_79","title":"A Unified Probabilistic Model for Aspect-Level Sentiment Analysis","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Probabilistic logic; Sentiment analysis; Artificial intelligence; Statistical model; Topic model; Natural language processing","score_opus":0.051898437707895914,"score_gpt":0.2836540105116508,"score_spread":0.23175557280375486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2346770464","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002293122,0.00015024218,0.9958723,0.00012330025,0.000036555117,0.000038023278,0.00020666873,0.0005064043,0.00077335135],"genre_scores_gemma":[0.23125242,0.0010593723,0.7552608,0.00037844188,0.00039665625,0.00056867284,0.0025180362,0.0006465101,0.007918994],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859315,0.00038768,0.00012381033,0.00030655248,0.0004769057,0.000111932924],"domain_scores_gemma":[0.9981111,0.00095070625,0.00016281263,0.00026792142,0.00043932337,0.00006816276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023945167,0.00077462924,0.0011910899,0.0013159347,0.000668877,0.0026021665,0.0024526336,0.0012851176,0.0047900383],"category_scores_gemma":[0.0069879447,0.00079997454,0.0020981291,0.002117714,0.0005772518,0.0037360254,0.0018591984,0.0020694556,0.0029009846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028737696,0.000245665,0.0028606297,0.00031927944,0.00037060838,0.00023040113,0.00037544378,0.3108454,0.012763279,0.18005173,0.01931478,0.47233543],"study_design_scores_gemma":[0.000007642066,0.000013246419,0.00022340441,0.000009209989,0.000030080717,0.000038128826,0.000008307322,0.9571667,0.00043703392,0.040069096,0.0019844198,0.000012712767],"about_ca_topic_score_codex":0.0046321885,"about_ca_topic_score_gemma":0.008312897,"teacher_disagreement_score":0.0047900383,"about_ca_system_score_codex":0.0009247564,"about_ca_system_score_gemma":0.0013446964,"threshold_uncertainty_score":0.016024292},"labels":[],"label_agreement":null},{"id":"W2347127863","doi":"10.1145/3003433","title":"Stance and Sentiment in Tweets","year":2017,"lang":"en","type":"article","venue":"ACM Transactions on Internet Technology","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":435,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; National Research Council Canada","funders":"","keywords":"Computer science; Task (project management); SemEval; Sentiment analysis; Natural language processing; Artificial intelligence; Word (group theory); Simple (philosophy); Test (biology); Linguistics","score_opus":0.02073649958369465,"score_gpt":0.28626518874210666,"score_spread":0.265528689158412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2347127863","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83069235,0.0023164444,0.03555588,0.0016613883,0.0010839705,0.0003885931,0.091060795,0.0015498172,0.0356908],"genre_scores_gemma":[0.89457333,0.0008108951,0.027437417,0.0003274026,0.00047077972,0.0003349413,0.06565226,0.00022343469,0.010169532],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998757,0.00030175867,0.00016362741,0.00027047822,0.00039101715,0.00011622533],"domain_scores_gemma":[0.99685234,0.0011128838,0.0007463368,0.00027653447,0.00076906674,0.00024286042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074799696,0.00059699354,0.000365776,0.0023827597,0.0007061919,0.0012959149,0.0002771539,0.00074809603,0.0033405249],"category_scores_gemma":[0.006165423,0.00018047211,0.0003450439,0.0021666114,0.00032121598,0.0018369673,0.001079507,0.00069736916,0.0029577734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0032679224,0.00042815195,0.3475066,0.0028624726,0.00029640424,0.0021383031,0.010256093,0.003958648,0.13036346,0.01074724,0.118703686,0.36947098],"study_design_scores_gemma":[0.00012548867,0.0006466849,0.57703143,0.00066686934,0.00022820485,0.0026331968,0.009225561,0.07009761,0.053360123,0.01977903,0.26596275,0.00024299209],"about_ca_topic_score_codex":0.0019461383,"about_ca_topic_score_gemma":0.0041306657,"teacher_disagreement_score":0.0033405249,"about_ca_system_score_codex":0.00048257987,"about_ca_system_score_gemma":0.0003223185,"threshold_uncertainty_score":0.011175156},"labels":[],"label_agreement":null},{"id":"W2387397982","doi":"10.1075/dapsac.55.05hir","title":"Text to Ideology or Text to Party Status?","year":2014,"lang":"en","type":"book-chapter","venue":"Discourse approaches to politics, society and culture","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Ideology; Opposition (politics); Politics; Political science; Swap (finance); Classifier (UML); Linguistics; Computer science; Artificial intelligence; Law; Economics; Philosophy","score_opus":0.08528079697147986,"score_gpt":0.28521599346385446,"score_spread":0.1999351964923746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2387397982","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14326933,0.020930292,0.10483427,0.038451016,0.013081114,0.00031110263,0.030582601,0.0041863513,0.6443539],"genre_scores_gemma":[0.70795304,0.009734483,0.041408736,0.0054424917,0.00675232,0.0003643454,0.0189002,0.001331314,0.20811296],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99961495,0.0001222119,0.000025707459,0.000112977854,0.00009113973,0.000032975648],"domain_scores_gemma":[0.9984078,0.0010790708,0.00014358721,0.000108818036,0.00021090278,0.000049861454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005770962,0.00036272578,0.0003055589,0.0018042171,0.00032443771,0.0027044755,0.00033648126,0.00057384575,0.031574734],"category_scores_gemma":[0.0054465956,0.00012731954,0.00019981047,0.0029273524,0.0007269695,0.004199555,0.00059193344,0.00074157445,0.016041636],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019509399,0.000052186984,0.004971333,0.00072856713,0.000042192958,0.0001161659,0.0019221493,0.0004408496,0.004372852,0.067311026,0.17064407,0.74920356],"study_design_scores_gemma":[0.00002981398,0.00008789415,0.02307318,0.0007327737,0.000046442845,0.0004884436,0.0034877127,0.011798887,0.006674453,0.112514265,0.8410228,0.000043312284],"about_ca_topic_score_codex":0.00059118296,"about_ca_topic_score_gemma":0.0005160779,"teacher_disagreement_score":0.031574734,"about_ca_system_score_codex":0.00037803306,"about_ca_system_score_gemma":0.00017168134,"threshold_uncertainty_score":0.105627954},"labels":[],"label_agreement":null},{"id":"W2395936782","doi":"","title":"A Comparative Study of Different Sentiment Lexica for Sentiment Analysis of Tweets","year":2015,"lang":"en","type":"article","venue":"Recent Advances in Natural Language Processing","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Sentiment analysis; Computer science; Natural language processing; Artificial intelligence; Information retrieval","score_opus":0.03576092369102291,"score_gpt":0.3703291697388255,"score_spread":0.3345682460478026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2395936782","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93485725,0.002347174,0.035323624,0.0005831309,0.00015521515,0.00021482557,0.0030686932,0.00043207937,0.02301795],"genre_scores_gemma":[0.952366,0.0013366468,0.03926075,0.000098647964,0.00011261396,0.00013875168,0.0043372125,0.00019209956,0.0021572427],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987985,0.0005737917,0.00014511267,0.00009573012,0.00030346675,0.000083291314],"domain_scores_gemma":[0.9922071,0.004871798,0.00038190963,0.00023977576,0.0020859863,0.00021344775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016946574,0.00028041942,0.000308197,0.004585302,0.0008016652,0.0017828625,0.00022628739,0.00030093436,0.003053707],"category_scores_gemma":[0.008923487,0.00013297751,0.00061963696,0.0038617328,0.00049820956,0.0021931932,0.00053072395,0.00039353952,0.0009299678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005585454,0.000885453,0.16430928,0.0035556355,0.00073659304,0.0008629082,0.009145123,0.0021197994,0.15972207,0.0184591,0.012286818,0.62233174],"study_design_scores_gemma":[0.0005082184,0.003310381,0.6336344,0.0011532098,0.0025581531,0.0039087795,0.038193136,0.11788017,0.099661544,0.022231825,0.076522276,0.0004380277],"about_ca_topic_score_codex":0.0017458735,"about_ca_topic_score_gemma":0.0031336646,"teacher_disagreement_score":0.004585302,"about_ca_system_score_codex":0.0005578285,"about_ca_system_score_gemma":0.00049964886,"threshold_uncertainty_score":0.01021564},"labels":[],"label_agreement":null},{"id":"W2397944984","doi":"10.2200/s00659ed1v01y201508hlt030","title":"Natural Language Processing for Social Media","year":2015,"lang":"en","type":"article","venue":"Synthesis lectures on human language technologies","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":93,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Ottawa; University of Toronto; University of Southern California","keywords":"Social media; Interpersonal communication; Natural (archaeology); Computer science; Internet privacy; Data science; Psychology; Sociology; Linguistics; World Wide Web; Communication; History","score_opus":0.05859163540961687,"score_gpt":0.3354402399822846,"score_spread":0.27684860457266774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2397944984","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055716615,0.006105964,0.95382416,0.0036641585,0.0020913125,0.00027526577,0.0042514936,0.01053123,0.013684787],"genre_scores_gemma":[0.124353915,0.0063325544,0.80330366,0.0009648246,0.0024991254,0.00072848436,0.017697133,0.0021933217,0.04192698],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99867165,0.00042403303,0.00014630142,0.0003012872,0.0003748661,0.00008186365],"domain_scores_gemma":[0.9970054,0.0016545185,0.00012513621,0.00038790482,0.00074142206,0.00008565076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018860822,0.0008224569,0.0007402682,0.0014226157,0.00068037014,0.0031387394,0.00083939114,0.00069028686,0.021021036],"category_scores_gemma":[0.005654442,0.00046259258,0.0010469935,0.0010447607,0.0006593472,0.0038865933,0.0011367259,0.0016633581,0.010637992],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012302218,0.00014971105,0.00047125435,0.0007716816,0.000101059384,0.0002215802,0.00027126702,0.005093881,0.016153043,0.059482433,0.17893524,0.73822576],"study_design_scores_gemma":[0.00005067442,0.00011190891,0.0020338034,0.00033668557,0.0000981426,0.00031111104,0.00045273817,0.27666283,0.021333858,0.3350658,0.3634468,0.00009572953],"about_ca_topic_score_codex":0.0025095718,"about_ca_topic_score_gemma":0.0033824118,"teacher_disagreement_score":0.021021036,"about_ca_system_score_codex":0.0008936925,"about_ca_system_score_gemma":0.00096396194,"threshold_uncertainty_score":0.070322394},"labels":[],"label_agreement":null},{"id":"W2398614220","doi":"","title":"Overview of the TAC2013 Knowledge Base Population Evaluation: English Sentiment Slot Filling.","year":2013,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sentiment analysis; Variety (cybernetics); Polarity (international relations); Computer science; Task (project management); Knowledge base; Population; Base (topology); Entity linking; Track (disk drive); Natural language processing; Artificial intelligence; Information retrieval; Engineering; Mathematics","score_opus":0.02827833491277492,"score_gpt":0.29375629374832396,"score_spread":0.26547795883554903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2398614220","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21695116,0.021545224,0.34619626,0.004621554,0.0033299653,0.009797626,0.16324173,0.08598532,0.14833117],"genre_scores_gemma":[0.22599171,0.0037626643,0.32747036,0.002207216,0.0005390613,0.0050722556,0.39881095,0.0036512527,0.03249452],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98887026,0.0041322387,0.0009092552,0.0013976292,0.0040032305,0.0006874433],"domain_scores_gemma":[0.98569435,0.0048947576,0.00037017488,0.0015363191,0.0068115457,0.000692864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013038445,0.0024615265,0.0015664734,0.009491207,0.00223648,0.004153801,0.004016831,0.002259027,0.0137552],"category_scores_gemma":[0.034532707,0.0005846694,0.0012851413,0.006147801,0.00062240043,0.0049693747,0.0028430016,0.0019613628,0.010373457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00083953794,0.0016492548,0.0054009906,0.0014523439,0.00043545762,0.00020811951,0.0004282619,0.010000265,0.0073880358,0.0017752266,0.29869694,0.6717256],"study_design_scores_gemma":[0.0010637741,0.0021422957,0.02759275,0.001555579,0.0012441974,0.0008949251,0.0026739552,0.43372095,0.05422841,0.011345396,0.46315143,0.0003863338],"about_ca_topic_score_codex":0.030361017,"about_ca_topic_score_gemma":0.031830665,"teacher_disagreement_score":0.030361017,"about_ca_system_score_codex":0.0027939011,"about_ca_system_score_gemma":0.004475445,"threshold_uncertainty_score":0.068954706},"labels":[],"label_agreement":null},{"id":"W2400231251","doi":"10.1007/978-1-4614-7163-9_351-1","title":"Multi-Classifier System for Sentiment Analysis and Opinion Mining","year":2017,"lang":"en","type":"book-chapter","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Sentiment analysis; Computer science; Classifier (UML); Artificial intelligence","score_opus":0.06107811014860572,"score_gpt":0.2962557055938176,"score_spread":0.23517759544521186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2400231251","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009721183,0.002722168,0.94392014,0.0005982234,0.001354654,0.00044537772,0.0020229302,0.018240143,0.020975225],"genre_scores_gemma":[0.061377913,0.0018188483,0.86912173,0.0005918817,0.0006371176,0.00048708057,0.0058589,0.00088200165,0.05922459],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923265,0.00007417624,0.00007322458,0.00020300028,0.00035414516,0.00006282134],"domain_scores_gemma":[0.999132,0.00016856885,0.00003465965,0.00007933862,0.0005357094,0.000049699956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009044058,0.00093868695,0.0011318619,0.001921675,0.00096409826,0.0016979341,0.0012976292,0.0011080275,0.019502187],"category_scores_gemma":[0.0015815821,0.00038680848,0.0010064793,0.0019565457,0.00015650036,0.001833326,0.0009271345,0.0013302827,0.0180862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011504878,0.00013081495,0.0006801567,0.0002267575,0.000072884024,0.00014650579,0.000079498546,0.001405655,0.025169829,0.0034232766,0.060947042,0.90760255],"study_design_scores_gemma":[0.00007863949,0.00028222162,0.0051015015,0.00022966653,0.00035458765,0.001312249,0.0002164412,0.58976734,0.10262161,0.021904118,0.27798006,0.00015163017],"about_ca_topic_score_codex":0.0020144763,"about_ca_topic_score_gemma":0.0037766155,"teacher_disagreement_score":0.019502187,"about_ca_system_score_codex":0.00062072516,"about_ca_system_score_gemma":0.00080593117,"threshold_uncertainty_score":0.06524134},"labels":[],"label_agreement":null},{"id":"W2402187144","doi":"","title":"A Large Wordnet-based Sentiment Lexicon for Polish.","year":2015,"lang":"en","type":"article","venue":"Recent Advances in Natural Language Processing","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"WordNet; Lexicon; Computer science; Sentiment analysis; Annotation; Natural language processing; Selection (genetic algorithm); Artificial intelligence; Resource (disambiguation); Process (computing); Information retrieval; Point (geometry)","score_opus":0.018838462704943215,"score_gpt":0.33234407663710946,"score_spread":0.31350561393216625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2402187144","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10291558,0.001737843,0.44565478,0.0021291038,0.0010572575,0.0025149637,0.3082911,0.025386617,0.110312805],"genre_scores_gemma":[0.19034919,0.0016515412,0.32867277,0.0004432782,0.0001765566,0.0036718247,0.43577352,0.0058696154,0.03339174],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993412,0.00011650401,0.00018316403,0.00015753869,0.00016295112,0.000038500846],"domain_scores_gemma":[0.9986388,0.00032193682,0.00018805577,0.00019898103,0.0005523689,0.000099821395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093859166,0.0010211325,0.00050700334,0.0047492436,0.0009992822,0.0017615176,0.00061593985,0.0004694656,0.01625445],"category_scores_gemma":[0.0046711755,0.0007210153,0.0005030768,0.003779347,0.00046298385,0.004994878,0.0020771734,0.0009577815,0.014004831],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006322758,0.00019133915,0.008574314,0.004704412,0.00011661077,0.0015931835,0.006006107,0.0036609673,0.058364503,0.08089661,0.33407623,0.50118345],"study_design_scores_gemma":[0.00004967387,0.000064051506,0.009548044,0.0003744931,0.00006601266,0.00095379195,0.001067053,0.00856407,0.011507248,0.019286746,0.94846356,0.00005532633],"about_ca_topic_score_codex":0.0029819314,"about_ca_topic_score_gemma":0.005476093,"teacher_disagreement_score":0.01625445,"about_ca_system_score_codex":0.00085121795,"about_ca_system_score_gemma":0.0022378347,"threshold_uncertainty_score":0.054376543},"labels":[],"label_agreement":null},{"id":"W2403668970","doi":"","title":"Using Roget's Thesaurus for Fine-grained Emotion Recognition.","year":2008,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Thesaurus; Computer science; WordNet; Natural language processing; Artificial intelligence; Lexicon; Emotive; Task (project management); Information retrieval","score_opus":0.16717582495874173,"score_gpt":0.3072526675717412,"score_spread":0.14007684261299946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2403668970","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08754715,0.0022304712,0.8639063,0.00084800826,0.00065706065,0.0014193809,0.0054879193,0.016783474,0.021120159],"genre_scores_gemma":[0.22848935,0.00067109294,0.7511139,0.00029193333,0.0001236015,0.00079690723,0.010787688,0.00056612294,0.0071594887],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986235,0.00036266953,0.00025587706,0.00041304738,0.00029632144,0.00004865954],"domain_scores_gemma":[0.9979724,0.00061183306,0.00026330026,0.00041743534,0.00066321605,0.0000718345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012994807,0.0008032791,0.0005697629,0.003692144,0.0005812922,0.0020463704,0.00084224984,0.0011294428,0.003661528],"category_scores_gemma":[0.009858627,0.00041241676,0.000949185,0.0017488103,0.0004547245,0.0038418027,0.0012637175,0.0008433066,0.004979916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003509434,0.00027316748,0.0067154258,0.0011371803,0.00030850817,0.00055757683,0.0017202147,0.0030214516,0.08389239,0.008730079,0.030457012,0.862836],"study_design_scores_gemma":[0.00027241863,0.0013572397,0.058528803,0.0012197237,0.0007283935,0.0057323272,0.0027211835,0.3497336,0.15512371,0.055835404,0.3682053,0.00054188387],"about_ca_topic_score_codex":0.0027551898,"about_ca_topic_score_gemma":0.003300914,"teacher_disagreement_score":0.003692144,"about_ca_system_score_codex":0.00046687832,"about_ca_system_score_gemma":0.0005984911,"threshold_uncertainty_score":0.012248993},"labels":[],"label_agreement":null},{"id":"W2404629843","doi":"","title":"Distant-supervised Language Model for Detecting Emotional Upsurge on Twitter","year":2015,"lang":"en","type":"article","venue":"Waseda University Repository (Waseda University)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Japan Society for the Promotion of Science","keywords":"Computer science; Spike (software development); Event (particle physics); Natural language processing; Artificial intelligence; Task (project management); Social media; Speech recognition; World Wide Web","score_opus":0.04007110618614262,"score_gpt":0.22434411415850086,"score_spread":0.18427300797235824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2404629843","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74570495,0.0013503402,0.2359758,0.0011180118,0.0006943866,0.00023449374,0.0033546286,0.003966085,0.007601363],"genre_scores_gemma":[0.9583656,0.00020421627,0.027968233,0.00016873273,0.00026065062,0.00010032191,0.003929948,0.00011221793,0.0088900095],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995065,0.00013373929,0.000031767315,0.00012788807,0.00010507372,0.000095010604],"domain_scores_gemma":[0.99926704,0.00028217473,0.000060971513,0.00005760513,0.00026754112,0.000064620595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075002125,0.0007511628,0.00063801615,0.0010745227,0.00063743326,0.00059672596,0.0006114464,0.00073883997,0.0018252243],"category_scores_gemma":[0.001592637,0.00019482557,0.00069062045,0.00062386674,0.0002009323,0.0011276153,0.00084518985,0.0011319316,0.0021706345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002617128,0.002353152,0.050664715,0.00039786694,0.000517766,0.00090958417,0.0005511612,0.06054759,0.08989832,0.0035051366,0.040168073,0.7478695],"study_design_scores_gemma":[0.000022865652,0.00013768596,0.007651745,0.00000906075,0.00005837646,0.00009901234,0.00012795479,0.9830388,0.006148666,0.0010764352,0.0016056414,0.000023923769],"about_ca_topic_score_codex":0.0037530346,"about_ca_topic_score_gemma":0.0077555403,"teacher_disagreement_score":0.0037530346,"about_ca_system_score_codex":0.00044796852,"about_ca_system_score_gemma":0.0005902824,"threshold_uncertainty_score":0.0074623823},"labels":[],"label_agreement":null},{"id":"W2405306037","doi":"","title":"Columbia NLP: Sentiment Slot Filling.","year":2013,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Polarity (international relations); Sentiment analysis; Natural language processing; Artificial intelligence; Computer science; Subjectivity; Philosophy; Chemistry","score_opus":0.008689956435664586,"score_gpt":0.23452303117930906,"score_spread":0.22583307474364447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2405306037","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045493464,0.0016306771,0.18627484,0.0021519787,0.0010671553,0.0009693587,0.47614834,0.25494945,0.07225885],"genre_scores_gemma":[0.0424929,0.0013098618,0.24638395,0.0009855704,0.00036599964,0.0024809134,0.6530269,0.020847421,0.032106448],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99840313,0.00031967298,0.00017769082,0.00043835986,0.00052139844,0.00013976378],"domain_scores_gemma":[0.99789244,0.0010608288,0.00013863863,0.00032639844,0.0004754625,0.00010632927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016327463,0.002617563,0.0014676069,0.0048542535,0.0025120082,0.0030301127,0.002396447,0.0021188396,0.14638627],"category_scores_gemma":[0.00920515,0.0015987074,0.0015933447,0.005185896,0.00080019113,0.004634476,0.0037618862,0.002746526,0.114621595],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021612791,0.000063938554,0.000903768,0.0021933464,0.000080749014,0.00050219055,0.00040465442,0.00071366585,0.0045770123,0.010895111,0.88331115,0.09613828],"study_design_scores_gemma":[0.00023611388,0.000037762515,0.0022045341,0.0004291512,0.00007978406,0.0006622678,0.000428197,0.023321161,0.009024477,0.033521876,0.92993695,0.000117741714],"about_ca_topic_score_codex":0.0121114375,"about_ca_topic_score_gemma":0.0155500695,"teacher_disagreement_score":0.14638627,"about_ca_system_score_codex":0.0013289358,"about_ca_system_score_gemma":0.003752295,"threshold_uncertainty_score":0.48971087},"labels":[],"label_agreement":null},{"id":"W2406891755","doi":"","title":"Dependency-based Topic-Oriented Sentiment Analysis in Microposts.","year":2015,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; SemEval; Artificial intelligence; Natural language processing; Dependency (UML); Sentence; Classifier (UML); Dependency grammar; Sentiment analysis; Exploit; Polarity (international relations); Parsing; Task (project management)","score_opus":0.023937413707166937,"score_gpt":0.2748734118358511,"score_spread":0.25093599812868417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2406891755","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0799745,0.0007558349,0.8650552,0.0006797264,0.0005881432,0.00065041997,0.014445674,0.025270786,0.01257978],"genre_scores_gemma":[0.49009687,0.00035457558,0.46055788,0.0003954709,0.00051067065,0.00067007996,0.033548255,0.0017674167,0.012098813],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990884,0.00021942692,0.00007951552,0.00020914413,0.0003097755,0.00009366537],"domain_scores_gemma":[0.9978897,0.0007967398,0.0003122379,0.0002190807,0.00068205403,0.00010007094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001078531,0.0009010212,0.0005405981,0.0022166881,0.0006373081,0.0014042921,0.0008109076,0.0006332769,0.0058148084],"category_scores_gemma":[0.0039388873,0.000354036,0.0011323878,0.001283388,0.00031384616,0.0021381837,0.0008489289,0.0010362037,0.004591189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001043211,0.0004142564,0.0194373,0.0011289937,0.00044004418,0.00089010346,0.0010149733,0.015250853,0.08922652,0.0149733,0.091369346,0.76481104],"study_design_scores_gemma":[0.00008647303,0.0003527152,0.0306273,0.00019773026,0.00030037158,0.0014701046,0.0009159712,0.7215158,0.08013755,0.045766097,0.11845928,0.00017062653],"about_ca_topic_score_codex":0.0025549557,"about_ca_topic_score_gemma":0.005952226,"teacher_disagreement_score":0.0058148084,"about_ca_system_score_codex":0.0007321066,"about_ca_system_score_gemma":0.00088958273,"threshold_uncertainty_score":0.019452512},"labels":[],"label_agreement":null},{"id":"W2406949971","doi":"","title":"All Blogs Are Not Made Equal: Exploring Genre Differences in Sentiment Tagging of Blogs.","year":2007,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Sentence; Annotation; Valence (chemistry); Negation; Subjectivity; Sentiment analysis; Natural language processing; German; Artificial intelligence; Information retrieval; Linguistics","score_opus":0.13486695081477929,"score_gpt":0.2983925222328046,"score_spread":0.16352557141802532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2406949971","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.968734,0.00093551027,0.021660484,0.00026405996,0.00016677447,0.000106538566,0.0022342936,0.0004986398,0.00539972],"genre_scores_gemma":[0.9466582,0.0005148056,0.046996456,0.00015017045,0.00014866017,0.00009642361,0.003223143,0.00012076366,0.002091431],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9992937,0.00027197413,0.00007144721,0.00015628412,0.00014578651,0.000060755065],"domain_scores_gemma":[0.99162346,0.005217234,0.0011810492,0.0005845522,0.0009876405,0.00040605865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018624204,0.00030564543,0.00031855027,0.0024473951,0.0005719586,0.0011705629,0.0002663646,0.0004445043,0.00086531125],"category_scores_gemma":[0.0104566645,0.00019678552,0.0002941021,0.0019978713,0.00029437782,0.0017461078,0.0006266757,0.00043068576,0.00071614434],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014085228,0.00039978634,0.31506604,0.00087817514,0.00020175407,0.00061851274,0.005149078,0.0008275609,0.09614784,0.0010832909,0.009221466,0.5689979],"study_design_scores_gemma":[0.000096796444,0.0005849194,0.8737613,0.00018692038,0.00036218498,0.0014799113,0.0055974573,0.06424501,0.028255364,0.005563745,0.019750161,0.00011628463],"about_ca_topic_score_codex":0.0015807276,"about_ca_topic_score_gemma":0.0046152007,"teacher_disagreement_score":0.0024473951,"about_ca_system_score_codex":0.0002898357,"about_ca_system_score_gemma":0.00025147147,"threshold_uncertainty_score":0.009849548},"labels":[],"label_agreement":null},{"id":"W2407706885","doi":"10.1609/icwsm.v6i1.14310","title":"Do You Feel What I Feel? Social Aspects of Emotions in Twitter Conversations","year":2021,"lang":"en","type":"article","venue":"Proceedings of the International AAAI Conference on Web and Social Media","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":89,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Conversation; Sympathy; Influencer marketing; Social media; Psychology; Emotion classification; Microblogging; Social psychology; Complaint; Cognitive psychology; Computer science; Communication; World Wide Web","score_opus":0.048433404314499724,"score_gpt":0.2802729538227877,"score_spread":0.231839549508288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2407706885","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96555114,0.0007650906,0.013433118,0.002417536,0.00011987815,0.0000681685,0.0020459339,0.00009013246,0.01550905],"genre_scores_gemma":[0.9955047,0.00019362291,0.0025715628,0.00015080761,0.00006143903,0.000053907355,0.0004519644,0.000020799198,0.0009912025],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993687,0.00032524287,0.000034577326,0.00010473761,0.000102353624,0.00006443375],"domain_scores_gemma":[0.9975781,0.0014276063,0.00054869143,0.000085738306,0.0002221284,0.00013771471],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006601473,0.00024470015,0.00019499657,0.0007934224,0.0010379629,0.0016634499,0.00020278906,0.0006230705,0.0017516421],"category_scores_gemma":[0.0059896116,0.00016289104,0.00022714598,0.0009264497,0.0008533075,0.002290844,0.0008525695,0.0006987902,0.0005230847],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001748099,0.00022209805,0.5417467,0.0007883362,0.00032671468,0.002390787,0.15505442,0.0067282584,0.057136867,0.024727566,0.020031217,0.18909888],"study_design_scores_gemma":[0.000033939978,0.00017762685,0.7103466,0.00029377185,0.0001508981,0.0011406686,0.09630086,0.087764576,0.008819988,0.03723768,0.057510525,0.00022277188],"about_ca_topic_score_codex":0.0029846206,"about_ca_topic_score_gemma":0.00439495,"teacher_disagreement_score":0.0029846206,"about_ca_system_score_codex":0.00054196746,"about_ca_system_score_gemma":0.00018785706,"threshold_uncertainty_score":0.005934477},"labels":[],"label_agreement":null},{"id":"W2412122638","doi":"10.1080/17470218.2016.1195417","title":"Extrapolating human judgments from skip-gram vector representations of word meaning","year":2016,"lang":"en","type":"article","venue":"Quarterly Journal of Experimental Psychology","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":122,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Concreteness; Psychology; Cognitive psychology; Psychological research; Age of Acquisition; Lexicon; Valence (chemistry); Lexical decision task; Semantics (computer science); Cognition; Natural language processing; Social psychology; Computer science","score_opus":0.04247953613387008,"score_gpt":0.3738679220245193,"score_spread":0.33138838589064923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2412122638","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62592566,0.00044268704,0.36248252,0.00032569942,0.00013083608,0.00015285933,0.0014130459,0.0018092258,0.007317442],"genre_scores_gemma":[0.8831614,0.00022535822,0.1141107,0.0000758282,0.000029803183,0.00009726677,0.0014646861,0.00010839597,0.00072651816],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998847,0.00046358243,0.00009184221,0.00026814014,0.00027719137,0.000052284817],"domain_scores_gemma":[0.9941894,0.003493129,0.00042376015,0.00091048056,0.00086479686,0.0001183517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018223788,0.0006402911,0.00031007113,0.0013514285,0.0002601165,0.0011533787,0.0003829333,0.00055324694,0.002851253],"category_scores_gemma":[0.026304517,0.00028729063,0.00038864123,0.00089925877,0.00048834586,0.0025978573,0.0010285121,0.00083683315,0.0012816057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012051,0.00033032306,0.046517253,0.00057724415,0.00028340274,0.00039991664,0.004781136,0.03763707,0.10163343,0.012921797,0.007468093,0.7862453],"study_design_scores_gemma":[0.00006508246,0.00066891295,0.06666819,0.00013718719,0.00007896885,0.00047768067,0.0019358794,0.80228585,0.03309455,0.0871638,0.0072599365,0.00016400688],"about_ca_topic_score_codex":0.0013792963,"about_ca_topic_score_gemma":0.0029259361,"teacher_disagreement_score":0.002851253,"about_ca_system_score_codex":0.00032588543,"about_ca_system_score_gemma":0.00032611788,"threshold_uncertainty_score":0.009637773},"labels":[],"label_agreement":null},{"id":"W2460077910","doi":"10.18653/v1/n16-1095","title":"Capturing Reliable Fine-Grained Sentiment Associations by Crowdsourcing and Best–Worst Scaling","year":2016,"lang":"en","type":"preprint","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Crowdsourcing; Computer science; Sentiment analysis; Annotation; Natural language processing; Ranking (information retrieval); Artificial intelligence; Set (abstract data type); Association (psychology); Process (computing); Word (group theory); Arabic; Linguistics; Psychology; World Wide Web","score_opus":0.01920080534226783,"score_gpt":0.25870920161596633,"score_spread":0.2395083962736985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2460077910","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25610915,0.0006121536,0.72191715,0.0007822156,0.000392597,0.00040356678,0.0023339887,0.0026281122,0.0148210665],"genre_scores_gemma":[0.8186915,0.00014821334,0.17635953,0.00017015412,0.00016292681,0.00036074538,0.0019031432,0.0004537638,0.0017500052],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9934097,0.0022761554,0.00048193033,0.001806317,0.0016474983,0.00037836022],"domain_scores_gemma":[0.9810441,0.0074774595,0.0022811792,0.0050091106,0.0036787256,0.00050929585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008048036,0.0018573692,0.0014229334,0.003201651,0.0019202771,0.0035123879,0.001214745,0.0013023422,0.0019129489],"category_scores_gemma":[0.052170213,0.0006419344,0.0009800559,0.0040972587,0.0014796279,0.0031668174,0.003092938,0.001471912,0.0015680881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016896793,0.00050364254,0.0848294,0.0009302285,0.0007966757,0.00096556847,0.0060762335,0.18880942,0.06617192,0.029276663,0.023540718,0.59640986],"study_design_scores_gemma":[0.00008744288,0.00016395822,0.025659315,0.000095011994,0.000097539356,0.00021106229,0.0016702381,0.8328072,0.017302098,0.10840846,0.013319094,0.00017856661],"about_ca_topic_score_codex":0.004616318,"about_ca_topic_score_gemma":0.006376962,"teacher_disagreement_score":0.008048036,"about_ca_system_score_codex":0.0010768387,"about_ca_system_score_gemma":0.0013060252,"threshold_uncertainty_score":0.042562604},"labels":[],"label_agreement":null},{"id":"W2460159515","doi":"10.18653/v1/s16-1003","title":"SemEval-2016 Task 6: Detecting Stance in Tweets","year":2016,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":857,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; National Research Council Canada","funders":"","keywords":"SemEval; Task (project management); Computer science; Natural language processing; Artificial intelligence; Engineering","score_opus":0.015373119276034547,"score_gpt":0.2510792161289844,"score_spread":0.23570609685294983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2460159515","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.394786,0.0058880686,0.09832889,0.005072936,0.010447895,0.0045498665,0.32685632,0.11018437,0.043885767],"genre_scores_gemma":[0.27246237,0.0006359637,0.1645146,0.0012477164,0.0012904514,0.002076005,0.52514195,0.004089105,0.028541846],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9919018,0.0025987723,0.0010126344,0.0019929404,0.0017729492,0.0007208758],"domain_scores_gemma":[0.9833348,0.0058825007,0.0011307545,0.004002733,0.0038828987,0.0017662985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073875887,0.0045671044,0.0026432776,0.003935443,0.0022279317,0.0033284603,0.0028221537,0.0057989215,0.008881683],"category_scores_gemma":[0.018141763,0.00072189607,0.002325499,0.00226506,0.0009011097,0.0049026366,0.005642655,0.0030557679,0.016703505],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033686236,0.0019893625,0.020341417,0.00405806,0.0007174536,0.0015441956,0.0013049097,0.007702335,0.03239281,0.0019041239,0.65302765,0.27164915],"study_design_scores_gemma":[0.0018186242,0.0032964402,0.056306113,0.00077690737,0.0005981102,0.004869314,0.0040743276,0.23258863,0.12473058,0.013631438,0.5566594,0.00065016944],"about_ca_topic_score_codex":0.0039718435,"about_ca_topic_score_gemma":0.008696359,"teacher_disagreement_score":0.008881683,"about_ca_system_score_codex":0.0012985255,"about_ca_system_score_gemma":0.002430315,"threshold_uncertainty_score":0.03906977},"labels":[],"label_agreement":null},{"id":"W2464521204","doi":"10.18653/v1/s16-1004","title":"SemEval-2016 Task 7: Determining Sentiment Intensity of English and Arabic Phrases","year":2016,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":97,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Natural language processing; Phrase; Task (project management); Artificial intelligence; SemEval; Arabic; Word (group theory); Domain (mathematical analysis); Linguistics; Mathematics","score_opus":0.014374076737246735,"score_gpt":0.23474342979531748,"score_spread":0.22036935305807076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2464521204","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4639881,0.006562969,0.10153637,0.0029933474,0.005170977,0.0041162,0.23210306,0.1258306,0.05769835],"genre_scores_gemma":[0.29214618,0.00070218695,0.20346777,0.0014034079,0.00067949854,0.0028050977,0.4668465,0.004402355,0.027546901],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961153,0.0010297174,0.00048500058,0.0012367875,0.00077589473,0.00035734547],"domain_scores_gemma":[0.9932869,0.0023121196,0.0003354399,0.0015679596,0.0019477353,0.0005498807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037593192,0.0045499043,0.0021201365,0.0034488216,0.001653443,0.0025646081,0.0021922835,0.0033654978,0.014480785],"category_scores_gemma":[0.012049214,0.0005971683,0.0020179255,0.0019107898,0.00082399126,0.0044628363,0.005154361,0.0025675711,0.020210769],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019548167,0.0011143348,0.013164647,0.0029564223,0.00053348934,0.0011764503,0.0012154204,0.0036926111,0.052035753,0.0015273923,0.4972045,0.42342407],"study_design_scores_gemma":[0.0013844203,0.002101949,0.07159308,0.0007893913,0.00070547516,0.00434777,0.005186663,0.23429166,0.19149601,0.011763499,0.47569996,0.0006400982],"about_ca_topic_score_codex":0.004447782,"about_ca_topic_score_gemma":0.008317021,"teacher_disagreement_score":0.014480785,"about_ca_system_score_codex":0.0012524846,"about_ca_system_score_gemma":0.0016693335,"threshold_uncertainty_score":0.04844308},"labels":[],"label_agreement":null},{"id":"W2467186984","doi":"10.18653/v1/s15-2078","title":"SemEval-2015 Task 10: Sentiment Analysis in Twitter","year":2015,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":367,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"National Research Council Canada","keywords":"SemEval; Sentiment analysis; Computer science; Task (project management); Context (archaeology); Phrase; Polarity (international relations); Natural language processing; Set (abstract data type); Artificial intelligence; History","score_opus":0.04611232732077405,"score_gpt":0.31021908039271456,"score_spread":0.26410675307194054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2467186984","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32996157,0.008051025,0.18468848,0.00819768,0.010121481,0.006105721,0.3019909,0.08519627,0.06568688],"genre_scores_gemma":[0.27399254,0.0009990304,0.22008666,0.0024432507,0.0014993061,0.004511158,0.45698187,0.0045240032,0.034962185],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99508697,0.002174644,0.00038336837,0.0009882402,0.0008520276,0.0005147053],"domain_scores_gemma":[0.9941889,0.0026920892,0.0003533454,0.0008882301,0.0012592126,0.0006182275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053858063,0.0040583545,0.0015858194,0.0025636493,0.0022720583,0.003035622,0.0021999278,0.0029429428,0.011383862],"category_scores_gemma":[0.014047675,0.00053849065,0.002286937,0.0018742987,0.0006723032,0.004020576,0.004572946,0.002807454,0.014276967],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014274548,0.0010777393,0.011484704,0.0016939857,0.00046225454,0.0006474751,0.0009966214,0.004879272,0.016420884,0.00252211,0.746649,0.21173833],"study_design_scores_gemma":[0.0011739292,0.0018712627,0.048826087,0.00054378517,0.00042650974,0.0016978586,0.003642272,0.2289357,0.053198278,0.018802619,0.64040846,0.00047319857],"about_ca_topic_score_codex":0.006315813,"about_ca_topic_score_gemma":0.020870088,"teacher_disagreement_score":0.011383862,"about_ca_system_score_codex":0.0017042933,"about_ca_system_score_gemma":0.0023420823,"threshold_uncertainty_score":0.038082838},"labels":[],"label_agreement":null},{"id":"W2469813598","doi":"","title":"A Pragma-Semantic Analysis of the Emotion/SentimentRelation in Debates","year":2016,"lang":"en","type":"preprint","venue":"Institutional Research Information System University of Turin (University of Turin)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Sentiment analysis; Argumentative; Computer science; Relation (database); Polarity (international relations); Emotion detection; Natural language processing; Emotion classification; Artificial intelligence; Emotion recognition; Semantics (computer science); Linguistics; Data mining","score_opus":0.037477471395331106,"score_gpt":0.25761251030031745,"score_spread":0.22013503890498634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2469813598","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40107095,0.0008965909,0.54901385,0.0029645301,0.00027853445,0.00052543293,0.006168285,0.0025509668,0.036530852],"genre_scores_gemma":[0.86709535,0.00016216969,0.1260335,0.00009154185,0.000116648596,0.0001629083,0.002985122,0.00016137515,0.0031913514],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9987219,0.0005337843,0.00009491412,0.0003162516,0.00022581704,0.0001073014],"domain_scores_gemma":[0.9983917,0.0006159958,0.00018952141,0.00023447293,0.00046203646,0.000106372725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014920833,0.00035675478,0.00035703598,0.0038765399,0.0014186997,0.0033324363,0.00065076066,0.000681114,0.006762996],"category_scores_gemma":[0.0057918523,0.0002376176,0.0013736712,0.0029770161,0.0008677726,0.0047992896,0.0018556118,0.0011752129,0.0014356347],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009343066,0.00051607715,0.048918463,0.0006875699,0.00037202393,0.0005057981,0.006911915,0.008085302,0.03449282,0.37829912,0.018599952,0.5016766],"study_design_scores_gemma":[0.00008171186,0.00023903321,0.07892943,0.00020502393,0.00041388295,0.0005641706,0.0072476007,0.51288706,0.01781733,0.3216943,0.059803814,0.00011663677],"about_ca_topic_score_codex":0.0021781186,"about_ca_topic_score_gemma":0.0020434307,"teacher_disagreement_score":0.006762996,"about_ca_system_score_codex":0.0010212443,"about_ca_system_score_gemma":0.0009915475,"threshold_uncertainty_score":0.022624433},"labels":[],"label_agreement":null},{"id":"W2469995694","doi":"10.18653/v1/s16-1060","title":"UWaterloo at SemEval-2016 Task 5: Minimally Supervised Approaches to Aspect-Based Sentiment Analysis","year":2016,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"SemEval; Computer science; Sentiment analysis; Task (project management); Artificial intelligence; Natural language processing; Machine learning; Engineering","score_opus":0.061100851783364434,"score_gpt":0.23923174691600835,"score_spread":0.1781308951326439,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2469995694","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.100230075,0.009752053,0.38043675,0.008702392,0.0055941106,0.0062387977,0.13152272,0.25178036,0.10574286],"genre_scores_gemma":[0.17348766,0.0019475843,0.5074293,0.002308945,0.0010072273,0.0028021326,0.23895068,0.009610759,0.062455706],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99349976,0.0024891722,0.0005699046,0.0014573634,0.0016645628,0.0003191806],"domain_scores_gemma":[0.99156404,0.0016290797,0.0005076349,0.0017344968,0.0038402712,0.00072436896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009636706,0.002013168,0.0016086211,0.0038782342,0.0014785491,0.0030890827,0.0014745201,0.0015738094,0.014132369],"category_scores_gemma":[0.01700113,0.0008142184,0.0010579313,0.0018474463,0.00066922576,0.0043655853,0.0035680232,0.0022304794,0.01758633],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005244997,0.00047948823,0.0039555933,0.0006906448,0.00014509984,0.00025826765,0.0006429471,0.0010916969,0.019688526,0.0025423837,0.64025676,0.32972404],"study_design_scores_gemma":[0.00052313576,0.0005854757,0.01860705,0.0003505694,0.00017057515,0.00088248693,0.0007442413,0.10614484,0.060320284,0.011379966,0.80008304,0.00020833951],"about_ca_topic_score_codex":0.015367845,"about_ca_topic_score_gemma":0.033348735,"teacher_disagreement_score":0.015367845,"about_ca_system_score_codex":0.0021941483,"about_ca_system_score_gemma":0.0037454811,"threshold_uncertainty_score":0.050964355},"labels":[],"label_agreement":null},{"id":"W2471707884","doi":"10.1016/j.dss.2016.06.010","title":"Modeling customer satisfaction from unstructured data using a Bayesian approach","year":2016,"lang":"en","type":"article","venue":"Decision Support Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":90,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Customer satisfaction; Computer science; Bayesian probability; Set (abstract data type); Sentiment analysis; Probabilistic logic; Unstructured data; The Internet; Customer intelligence; Data mining; Service (business); Product (mathematics); Bayesian network; Variety (cybernetics); Data set; Data science; Artificial intelligence; Service quality; Customer retention; World Wide Web; Big data; Marketing; Mathematics","score_opus":0.08640592321187143,"score_gpt":0.3129794778408794,"score_spread":0.226573554629008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2471707884","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.092826866,0.00044366735,0.9038727,0.0006835309,0.000046591755,0.0000806631,0.00055848743,0.00024623005,0.0012412583],"genre_scores_gemma":[0.8833651,0.0007835208,0.11136953,0.00021693822,0.00019350261,0.0002914258,0.0013174086,0.00005617451,0.00240648],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998566,0.00068056444,0.000082764825,0.00021782369,0.00031098453,0.000142025],"domain_scores_gemma":[0.9912096,0.0074879806,0.00046162168,0.00017228736,0.0005601388,0.000108403896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003307156,0.0007852183,0.0014297018,0.0013067394,0.00041456879,0.0016153305,0.0014045166,0.0014139392,0.0014364265],"category_scores_gemma":[0.0130973365,0.001106301,0.0012130436,0.0014746035,0.000576412,0.0026602915,0.00079206534,0.0018382155,0.00044448627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016716229,0.00020496432,0.006000773,0.00009929942,0.00020545984,0.00013147498,0.00014052882,0.9299975,0.00088733353,0.015178951,0.0013222964,0.04566432],"study_design_scores_gemma":[0.0000054057373,0.000010129196,0.00033052868,0.00000381289,0.000008980999,0.00000575,0.000006549894,0.9944956,0.000056626443,0.0049888906,0.00008312038,0.0000047300196],"about_ca_topic_score_codex":0.012841521,"about_ca_topic_score_gemma":0.014358096,"teacher_disagreement_score":0.012841521,"about_ca_system_score_codex":0.0009780453,"about_ca_system_score_gemma":0.0009780467,"threshold_uncertainty_score":0.025533557},"labels":[],"label_agreement":null},{"id":"W2473556710","doi":"10.18653/v1/n16-1128","title":"Sentiment Composition of Words with Opposing Polarities","year":2016,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Phrase; Polarity (international relations); Natural language processing; Computer science; Artificial intelligence; Composition (language); Sentiment analysis; Word (group theory); Word embedding; Association (psychology); Embedding; Linguistics; Psychology; Chemistry","score_opus":0.014600829723415393,"score_gpt":0.23658352064396862,"score_spread":0.22198269092055323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2473556710","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90643615,0.0015501259,0.046622198,0.0005541421,0.0004790697,0.00037484925,0.016653145,0.0008522453,0.02647804],"genre_scores_gemma":[0.9372954,0.0005192247,0.03937418,0.00019939798,0.00029205752,0.00033095558,0.017113302,0.00023354821,0.0046418356],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852955,0.00028368272,0.0002057033,0.00033369492,0.00050335034,0.00014396755],"domain_scores_gemma":[0.9965843,0.0012019275,0.0006475444,0.00016520754,0.0012292007,0.00017175556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010270708,0.0006182949,0.00044169207,0.002822464,0.00087326043,0.0012526152,0.00024651873,0.00037927498,0.0033148339],"category_scores_gemma":[0.005463268,0.0001730547,0.00054599147,0.0027423422,0.00051473774,0.0013530884,0.0007884755,0.0005382367,0.0015289511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025090105,0.0003000851,0.28239062,0.0025280912,0.00057966757,0.0014623943,0.004856222,0.0024796627,0.22194488,0.010942993,0.03792568,0.43208063],"study_design_scores_gemma":[0.00020284356,0.00086688914,0.62060577,0.0005913897,0.0010147605,0.0039490215,0.0081830975,0.06041152,0.08572305,0.02517056,0.19298735,0.00029380506],"about_ca_topic_score_codex":0.001367992,"about_ca_topic_score_gemma":0.002634424,"teacher_disagreement_score":0.0033148339,"about_ca_system_score_codex":0.0005085737,"about_ca_system_score_gemma":0.00047329796,"threshold_uncertainty_score":0.011089206},"labels":[],"label_agreement":null},{"id":"W2474547845","doi":"10.1007/978-3-319-34111-8_33","title":"Improving Conversation Engagement Through Data-Driven Agent Behavior Modification","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University","funders":"","keywords":"Computer science; Conversation; Human–computer interaction; Architecture; Interface (matter); Artificial intelligence","score_opus":0.08574202323540375,"score_gpt":0.3107764220444766,"score_spread":0.22503439880907283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2474547845","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13278805,0.0006988887,0.847951,0.0005280227,0.00045398728,0.00036554056,0.0009310307,0.011793612,0.004489839],"genre_scores_gemma":[0.76793987,0.00016765957,0.22568338,0.00024995714,0.00016242902,0.00043037703,0.0016207661,0.00060214027,0.0031434402],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998309,0.0005262181,0.00010244293,0.0005587381,0.0003851535,0.00011841357],"domain_scores_gemma":[0.9960109,0.0021952156,0.00024962908,0.00044462972,0.0009282562,0.00017135726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014230893,0.0011920938,0.0012311185,0.0007870863,0.00035829126,0.0011126625,0.001209422,0.00078710396,0.002108893],"category_scores_gemma":[0.009627658,0.0004288324,0.00056373177,0.00056385266,0.0002536536,0.001503026,0.0013009259,0.0018325007,0.0021255305],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013262333,0.001614259,0.008593163,0.00038365932,0.00027003355,0.00012087053,0.00069144845,0.037242588,0.12611522,0.001182109,0.008527849,0.8139326],"study_design_scores_gemma":[0.000057862973,0.00027022386,0.0031131373,0.000020366104,0.00010087902,0.000055771554,0.00015047884,0.9598234,0.030905064,0.0021509563,0.003317132,0.000034671215],"about_ca_topic_score_codex":0.0017401145,"about_ca_topic_score_gemma":0.0025595666,"teacher_disagreement_score":0.002108893,"about_ca_system_score_codex":0.00038819967,"about_ca_system_score_gemma":0.0005514422,"threshold_uncertainty_score":0.0075260997},"labels":[],"label_agreement":null},{"id":"W2475106992","doi":"10.5815/ijisa.2016.07.06","title":"An Automated Real-Time System for Opinion Mining using a Hybrid Approach","year":2016,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems and Applications","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Parsing; Variety (cybernetics); Dependency grammar; Sentiment analysis; Artificial intelligence; Graph; Grammar; Natural language processing; Negation; Dependency (UML); Phrase; Programming language; Theoretical computer science; Linguistics","score_opus":0.03658204835830141,"score_gpt":0.33413022122271246,"score_spread":0.29754817286441104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2475106992","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016795885,0.0003203752,0.90955484,0.00028086506,0.00015565194,0.00039893627,0.0018065339,0.06735184,0.0033351262],"genre_scores_gemma":[0.21406025,0.00028481727,0.77113104,0.0004922149,0.00016829543,0.0006810845,0.0056007067,0.00074005726,0.0068415627],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895203,0.00016430617,0.0001251087,0.000369843,0.00031335797,0.00007533655],"domain_scores_gemma":[0.99820817,0.0005009222,0.00015861122,0.0002754787,0.00075763336,0.00009915059],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011537073,0.0009918918,0.000999058,0.002969403,0.00045536517,0.0015714923,0.0014175734,0.001031394,0.005245085],"category_scores_gemma":[0.0025815377,0.0004337665,0.0007717939,0.001379465,0.0001987919,0.0022405814,0.0009129442,0.00065111864,0.0057219802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009046294,0.0002854697,0.0044438294,0.00049006514,0.00027516106,0.0006227797,0.000391504,0.0039436677,0.08261861,0.0034005556,0.0322135,0.8704103],"study_design_scores_gemma":[0.00019279668,0.00042975435,0.0060454705,0.00008572247,0.00032125993,0.0009551937,0.00040602157,0.84863615,0.08519906,0.009122157,0.04842037,0.00018601009],"about_ca_topic_score_codex":0.0020851176,"about_ca_topic_score_gemma":0.002463183,"teacher_disagreement_score":0.005245085,"about_ca_system_score_codex":0.000523734,"about_ca_system_score_gemma":0.0005153013,"threshold_uncertainty_score":0.017546594},"labels":[],"label_agreement":null},{"id":"W2489924657","doi":"10.4018/978-1-4666-6114-1.ch028","title":"Analytics and Performance Measurement Frameworks for Social Customer Relationship Management","year":2014,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Analytics; Big data; Computer science; Data science; Customer relationship management; Business analytics; Knowledge management; Process management; Business; Business model; Business analysis; Data mining; Database; Marketing","score_opus":0.05554410048392946,"score_gpt":0.2728302912194287,"score_spread":0.21728619073549924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2489924657","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005696957,0.0604789,0.716459,0.02538684,0.001445062,0.0006962263,0.0014128889,0.00159634,0.18682784],"genre_scores_gemma":[0.21683079,0.08333673,0.6470911,0.004076832,0.0041765273,0.0021956384,0.002740341,0.0006927026,0.03885927],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99286956,0.0026044594,0.0004768089,0.00070660084,0.0029932463,0.00034944137],"domain_scores_gemma":[0.9927348,0.004168584,0.000755653,0.0005323258,0.0015874229,0.00022121317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008028987,0.0024275975,0.0010034986,0.008262028,0.0016590212,0.009082363,0.002352069,0.0026108054,0.007522134],"category_scores_gemma":[0.013201666,0.0006497647,0.0012535403,0.009596205,0.003382595,0.013044005,0.0036893617,0.00457846,0.0033073213],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010509611,0.00006842535,0.0009484077,0.0005817996,0.00003750415,0.00006145178,0.00089597213,0.004607673,0.00033378348,0.8402428,0.022775525,0.12943602],"study_design_scores_gemma":[0.0000064737183,0.000059764887,0.002220696,0.0016817189,0.00003602801,0.0002510965,0.0014900306,0.032681238,0.0006906304,0.6690645,0.29172668,0.00009113978],"about_ca_topic_score_codex":0.0042136707,"about_ca_topic_score_gemma":0.002666674,"teacher_disagreement_score":0.009082363,"about_ca_system_score_codex":0.0060873698,"about_ca_system_score_gemma":0.003154744,"threshold_uncertainty_score":0.0441671},"labels":[],"label_agreement":null},{"id":"W2490032828","doi":"10.4018/978-1-60566-766-9.ch015","title":"Machine Learning Applications in Mega-Text Processing","year":2010,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Agricultural Research Institute of Ontario","funders":"","keywords":"Computer science; Focus (optics); Selection (genetic algorithm); Feature selection; Text processing; The Internet; Prime (order theory); Feature (linguistics); Artificial intelligence; Representation (politics); Natural language processing; Word (group theory); Sentiment analysis; Data science; Information retrieval; World Wide Web; Linguistics","score_opus":0.01676752531484623,"score_gpt":0.2603800503594275,"score_spread":0.24361252504458128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2490032828","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0062086326,0.12447805,0.669308,0.005856611,0.0025417148,0.00027347336,0.00079936057,0.0026379996,0.18789606],"genre_scores_gemma":[0.088521674,0.119943686,0.6144997,0.0032902556,0.0037776136,0.00051780336,0.0031049955,0.0009651945,0.16537909],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945325,0.00011720515,0.000051148425,0.00011683631,0.00023024355,0.000031418847],"domain_scores_gemma":[0.99875057,0.0008529918,0.00004147701,0.00012860227,0.00019503644,0.000031330295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059990224,0.0009209715,0.00065347756,0.0016441004,0.00053859025,0.0023356928,0.0011699059,0.0013722727,0.017445354],"category_scores_gemma":[0.002294521,0.00031844672,0.0006625417,0.0038140337,0.0006970419,0.0031688819,0.0009880343,0.0017758074,0.012529997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003396198,0.000107124935,0.00047474224,0.0013139282,0.00004744691,0.00041549074,0.00034032163,0.008311986,0.00345426,0.1053623,0.05962686,0.82051164],"study_design_scores_gemma":[0.000012033049,0.000053125274,0.0008179442,0.0006744344,0.000025887759,0.0010345441,0.00019566364,0.048069496,0.0044696014,0.32922253,0.61538255,0.000042259577],"about_ca_topic_score_codex":0.0004115969,"about_ca_topic_score_gemma":0.00060547155,"teacher_disagreement_score":0.017445354,"about_ca_system_score_codex":0.00059362274,"about_ca_system_score_gemma":0.00043171548,"threshold_uncertainty_score":0.058360517},"labels":[],"label_agreement":null},{"id":"W2511477721","doi":"10.18653/v1/w16-0410","title":"The Effect of Negators, Modals, and Degree Adverbs on Sentiment Composition","year":2016,"lang":"en","type":"preprint","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Heuristics; Degree (music); Natural language processing; Computer science; Artificial intelligence; Task (project management); Modal verb; Affect (linguistics); Sentiment analysis; Verb; Linguistics; Psychology; Communication","score_opus":0.014959711594090066,"score_gpt":0.2620764908116417,"score_spread":0.24711677921755162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2511477721","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97513235,0.00051441527,0.014115649,0.0002905881,0.0000927285,0.00009800566,0.0049078716,0.0003632473,0.0044851485],"genre_scores_gemma":[0.9771767,0.0002930857,0.014511813,0.00013997233,0.000049139737,0.000092744776,0.006839064,0.00011103579,0.0007864015],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978587,0.00087125204,0.00019628368,0.00048417505,0.0004354553,0.00015409804],"domain_scores_gemma":[0.9888115,0.008851226,0.00078480056,0.0006486957,0.0006806641,0.00022314934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026046182,0.0006698712,0.0004866713,0.0011951762,0.0005121034,0.0011096738,0.00034522687,0.00049054186,0.0013361699],"category_scores_gemma":[0.01016625,0.0002829843,0.0008609486,0.0010303236,0.000787335,0.0013100941,0.0006585829,0.0009858594,0.0005884265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003220754,0.0006595553,0.49020824,0.0018142096,0.00092191616,0.0011389401,0.0021313329,0.023991005,0.19252777,0.005305884,0.015438749,0.26264158],"study_design_scores_gemma":[0.00023000917,0.0013626936,0.5805547,0.00026367613,0.0007926418,0.0014235735,0.0014779364,0.27347568,0.09542425,0.016593497,0.028193835,0.00020748231],"about_ca_topic_score_codex":0.0019142714,"about_ca_topic_score_gemma":0.004976734,"teacher_disagreement_score":0.0026046182,"about_ca_system_score_codex":0.000525008,"about_ca_system_score_gemma":0.0003692123,"threshold_uncertainty_score":0.013774693},"labels":[],"label_agreement":null},{"id":"W2511558409","doi":"10.18653/v1/w16-0429","title":"A Practical Guide to Sentiment Annotation: Challenges and Solutions","year":2016,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":101,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Annotation; Computer science; Sentiment analysis; Natural language processing; Information retrieval; Artificial intelligence; Simple (philosophy)","score_opus":0.08541566248393216,"score_gpt":0.34934320484660775,"score_spread":0.26392754236267557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2511558409","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008064732,0.0039858553,0.87666214,0.02595125,0.0028089846,0.0018172524,0.009858028,0.024924261,0.053185653],"genre_scores_gemma":[0.0024932665,0.003733859,0.93391776,0.0040545715,0.00057857425,0.0013218971,0.006087585,0.0023781862,0.045434218],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99710387,0.001132819,0.00047185467,0.0003038109,0.0008712184,0.00011631715],"domain_scores_gemma":[0.9853284,0.005867403,0.0005063196,0.0013616858,0.0063871313,0.000549059],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.005718189,0.0020286138,0.0010191862,0.0043281266,0.0019921125,0.005599234,0.0030045344,0.003514877,0.072586454],"category_scores_gemma":[0.021984078,0.0020347077,0.00078708556,0.0041525275,0.0013430282,0.0070440467,0.0028072963,0.004914818,0.1031075],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038690403,0.000052392057,0.00018990834,0.00057045167,0.000011185305,0.0001601377,0.00041022588,0.00038019483,0.0028556865,0.019836893,0.7865552,0.18893905],"study_design_scores_gemma":[0.000025075396,0.000026334108,0.00025701287,0.00036149228,0.0000073558454,0.0004822796,0.00044687747,0.0039831977,0.0011377658,0.031994034,0.96123195,0.00004660366],"about_ca_topic_score_codex":0.003860498,"about_ca_topic_score_gemma":0.011068376,"teacher_disagreement_score":0.9942818,"about_ca_system_score_codex":0.0013703327,"about_ca_system_score_gemma":0.004046115,"threshold_uncertainty_score":0.24282587},"labels":[],"label_agreement":null},{"id":"W2515059321","doi":"10.18653/v1/p16-1031","title":"Bi-Transferring Deep Neural Networks for Domain Adaptation","year":2016,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Fundamental Research Funds for the Central Universities; Central China Normal University; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Classifier (UML); Domain adaptation; Artificial intelligence; Domain (mathematical analysis); Transfer of learning; Benchmark (surveying); Labeled data; Sentiment analysis; Artificial neural network; Machine learning; Pattern recognition (psychology); Mathematics","score_opus":0.02694865210684847,"score_gpt":0.2521152033244132,"score_spread":0.2251665512175647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2515059321","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051547404,0.0004651509,0.94188046,0.00021962283,0.00009218498,0.00006516584,0.00017932104,0.0024910416,0.0030595968],"genre_scores_gemma":[0.74178773,0.00055392046,0.24897417,0.00039793222,0.00008929475,0.00023945539,0.0013350095,0.00019748243,0.0064250147],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997203,0.00007358318,0.000018041985,0.000084314386,0.00006826444,0.000035553803],"domain_scores_gemma":[0.99961144,0.00011108629,0.000046532543,0.00009380704,0.00011504522,0.00002222709],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000626378,0.00084745104,0.00038482863,0.0004986656,0.0002581009,0.00040303904,0.0009839981,0.0006195906,0.0014822687],"category_scores_gemma":[0.0018460046,0.000260026,0.0005914555,0.00062652223,0.0003949282,0.0013029098,0.0011186214,0.0013500525,0.00086933887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000251193,0.00024737907,0.0028332574,0.00011990772,0.00011059583,0.00017898194,0.00017451165,0.32642832,0.040291138,0.007142988,0.0076092277,0.61461246],"study_design_scores_gemma":[0.0000075593066,0.000023328736,0.0003762759,0.0000056330664,0.000009352149,0.000031203752,0.00002150087,0.98743606,0.006619081,0.004016071,0.0014462463,0.00000763804],"about_ca_topic_score_codex":0.002895181,"about_ca_topic_score_gemma":0.0036675327,"teacher_disagreement_score":0.002895181,"about_ca_system_score_codex":0.0006912678,"about_ca_system_score_gemma":0.0005603808,"threshold_uncertainty_score":0.005756676},"labels":[],"label_agreement":null},{"id":"W2519464461","doi":"10.1007/978-3-319-45814-4_39","title":"CoDS: Co-training with Domain Similarity for Cross-Domain Image Sentiment Classification","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Similarity (geometry); Domain (mathematical analysis); Weighting; Image (mathematics); Artificial intelligence; Pattern recognition (psychology); Set (abstract data type); Training set; Sentiment analysis; Contextual image classification; Machine learning; Mathematics","score_opus":0.03762484011830417,"score_gpt":0.3087592115937882,"score_spread":0.271134371475484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2519464461","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06230584,0.0018833736,0.8742275,0.00045867654,0.001061599,0.0005947462,0.0042839167,0.048992194,0.0061921333],"genre_scores_gemma":[0.23848675,0.0006153387,0.7198565,0.0006346261,0.0003131472,0.0007499602,0.02275371,0.002136352,0.014453587],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869895,0.0002465068,0.00010068696,0.00046605596,0.00029875722,0.00018905975],"domain_scores_gemma":[0.99813974,0.0006069588,0.000098181335,0.00046004972,0.00057057774,0.00012448613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025601876,0.0022336885,0.0016908395,0.0024093462,0.00089957414,0.0012916932,0.002289907,0.0019541737,0.009387352],"category_scores_gemma":[0.0040148515,0.00066608563,0.0017053612,0.002347055,0.00050584454,0.0020646516,0.0029123458,0.0023596112,0.007890091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066083187,0.00049856,0.0027340208,0.00022586314,0.00030229593,0.00013006844,0.00009075219,0.009178821,0.020995017,0.0010889875,0.059111785,0.9049829],"study_design_scores_gemma":[0.00016269104,0.00038275355,0.0031719962,0.000059812235,0.00017239504,0.00025186548,0.00020966584,0.94631165,0.026387388,0.0045209415,0.018316602,0.000052156818],"about_ca_topic_score_codex":0.00514073,"about_ca_topic_score_gemma":0.009993283,"teacher_disagreement_score":0.009387352,"about_ca_system_score_codex":0.00060337083,"about_ca_system_score_gemma":0.0011612871,"threshold_uncertainty_score":0.03140378},"labels":[],"label_agreement":null},{"id":"W2530745782","doi":"10.4018/ijban.2017010101","title":"Exploring Insurance and Natural Disaster Tweets Using Text Analytics","year":2016,"lang":"en","type":"article","venue":"International Journal of Business Analytics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Brock University","funders":"","keywords":"Natural disaster; Insurance fraud; Analytics; Business; Flood myth; Flood insurance; Sentiment analysis; Data science; Computer science; Actuarial science; Geography; Natural language processing","score_opus":0.09667198861335495,"score_gpt":0.296674369333476,"score_spread":0.20000238072012105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2530745782","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9648891,0.0007446125,0.007459882,0.0015040524,0.0001513915,0.00021268986,0.01405514,0.0001936506,0.010789418],"genre_scores_gemma":[0.97439563,0.0008771759,0.012976666,0.00026651056,0.00020382497,0.00016881124,0.008652459,0.000042249525,0.0024166883],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99954706,0.00010318898,0.00004616208,0.00005874464,0.00017250767,0.000072341885],"domain_scores_gemma":[0.9980604,0.0011736506,0.00035346329,0.00005143986,0.00029288526,0.000068114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000491233,0.0003359682,0.0001881959,0.0049489695,0.00067675003,0.0013426578,0.0002623616,0.00037088533,0.0013841215],"category_scores_gemma":[0.0030028096,0.00011972067,0.00027367237,0.003983125,0.00021036755,0.0020509535,0.00068017957,0.00034694056,0.00052325224],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011670823,0.00063193013,0.46826658,0.0027696209,0.0002593879,0.0048177657,0.030135332,0.0074148313,0.06449972,0.010047212,0.032002453,0.37798807],"study_design_scores_gemma":[0.0000619528,0.00036942953,0.56659,0.00085757114,0.00028291938,0.0022128578,0.10404192,0.108519696,0.036033064,0.013350785,0.16748227,0.00019753179],"about_ca_topic_score_codex":0.0053341906,"about_ca_topic_score_gemma":0.0097645195,"teacher_disagreement_score":0.0053341906,"about_ca_system_score_codex":0.0005616433,"about_ca_system_score_gemma":0.00045636916,"threshold_uncertainty_score":0.010606289},"labels":[],"label_agreement":null},{"id":"W2532696200","doi":"10.1016/j.is.2016.10.004","title":"HarVis: An integrated social media content analysis framework for YouTube platform","year":2016,"lang":"en","type":"article","venue":"Information Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Influencer marketing; Popularity; Social media; Entertainment; World Wide Web; Content analysis; Visualization; Vocabulary; Social network analysis; Exploratory analysis; User-generated content; Globe; Data science; Multimedia","score_opus":0.09981051094323912,"score_gpt":0.29717849900780147,"score_spread":0.19736798806456235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2532696200","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02093351,0.00043892738,0.8276537,0.0006027412,0.00016170969,0.0015857203,0.037276845,0.10214277,0.009204122],"genre_scores_gemma":[0.120073065,0.0005807315,0.79168016,0.00028845153,0.00012420524,0.0018531535,0.061890747,0.00629967,0.01720978],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952936,0.00008245569,0.000045028984,0.00008912832,0.00020140003,0.000052608935],"domain_scores_gemma":[0.99939466,0.00018341331,0.0000617714,0.00007342136,0.0002216207,0.00006521916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095845934,0.0012706388,0.0005589731,0.0050981347,0.0008336326,0.0020435893,0.0009048096,0.00051423383,0.0054477733],"category_scores_gemma":[0.0025294577,0.00048877666,0.0012003554,0.002153497,0.00028697608,0.0023359403,0.0018279804,0.0010099501,0.0033357448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005783186,0.00043868204,0.016274327,0.0012373283,0.0007721269,0.0006908299,0.0026694436,0.015047518,0.051160827,0.04666529,0.22256581,0.6418995],"study_design_scores_gemma":[0.00008034973,0.0001992407,0.015938574,0.00024700558,0.00028022676,0.00035010782,0.0014997049,0.626958,0.033015475,0.044887222,0.27635542,0.00018865765],"about_ca_topic_score_codex":0.027856566,"about_ca_topic_score_gemma":0.057887044,"teacher_disagreement_score":0.027856566,"about_ca_system_score_codex":0.0011978928,"about_ca_system_score_gemma":0.0014518484,"threshold_uncertainty_score":0.05538881},"labels":[],"label_agreement":null},{"id":"W2541728594","doi":"10.1109/iscbi.2013.67","title":"Sentiment Analysis of Online News Using MALLET","year":2013,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Sentiment analysis; Computer science; Vocabulary; Natural language processing; Focus (optics); Context (archaeology); Meaning (existential); Tone (literature); Artificial intelligence; Contrast (vision); Task (project management); Mallet; Speech recognition; Linguistics; Psychology; Engineering","score_opus":0.03659986483848303,"score_gpt":0.2929007280443981,"score_spread":0.25630086320591505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2541728594","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43496042,0.0012935696,0.5030275,0.0011474733,0.00089275796,0.0010162422,0.018016664,0.026541097,0.013104303],"genre_scores_gemma":[0.5277904,0.00046361046,0.4236312,0.00031058295,0.00029266745,0.0006356478,0.039273478,0.00052713364,0.0070751747],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897766,0.00023853272,0.0001696672,0.00019438141,0.00030805112,0.00011165239],"domain_scores_gemma":[0.99777406,0.0009519408,0.00020960192,0.0001396435,0.000846059,0.0000786986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016216224,0.0008840186,0.000733331,0.0049878964,0.000765125,0.0014497829,0.0006646094,0.00073709484,0.0029570577],"category_scores_gemma":[0.004419656,0.0003095392,0.000873426,0.0026280757,0.00023093943,0.0015930503,0.00068162795,0.0011282247,0.002619831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013837948,0.0008936654,0.023110416,0.0006550553,0.0004542092,0.0006860173,0.00046815028,0.0449662,0.045335323,0.0037494139,0.043013323,0.8352845],"study_design_scores_gemma":[0.000115752395,0.00024068385,0.0083714845,0.00004175619,0.000070870265,0.0002078972,0.000278545,0.9531728,0.020923933,0.004633376,0.011896991,0.00004580876],"about_ca_topic_score_codex":0.002667132,"about_ca_topic_score_gemma":0.0050476505,"teacher_disagreement_score":0.0049878964,"about_ca_system_score_codex":0.0006569878,"about_ca_system_score_gemma":0.00055212434,"threshold_uncertainty_score":0.009892285},"labels":[],"label_agreement":null},{"id":"W2542179328","doi":"10.1007/978-3-319-48674-1_55","title":"An Empirical Study and Comparison for Tweet Sentiment Analysis","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Priority Academic Program Development of Jiangsu Higher Education Institutions","keywords":"Computer science; Sentiment analysis; Artificial intelligence; Support vector machine; Random forest; Machine learning; Feature selection; Domain (mathematical analysis); Selection (genetic algorithm); Empirical research; Feature (linguistics); Deep learning; Natural language processing","score_opus":0.03888278192669729,"score_gpt":0.3434988057895813,"score_spread":0.304616023862884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2542179328","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9480561,0.001845835,0.032357506,0.00047410748,0.00022147158,0.0006248552,0.0019154089,0.00015931854,0.01434538],"genre_scores_gemma":[0.97122955,0.0008391013,0.021933468,0.00008824374,0.00014796745,0.0004618014,0.0029644053,0.00007255867,0.0022629413],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9937691,0.003416563,0.0006310912,0.0005069113,0.0015169657,0.00015930439],"domain_scores_gemma":[0.9532597,0.03460913,0.0020849297,0.0022521557,0.0072180172,0.00057614374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0071996753,0.0004050831,0.0003631271,0.003803907,0.00088649977,0.001495036,0.0006151681,0.00061069534,0.00456394],"category_scores_gemma":[0.054209255,0.00016124632,0.00058073096,0.00473723,0.0006107434,0.0030729554,0.0011020515,0.0005814867,0.0010029462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0038356523,0.0033824102,0.37744725,0.002484872,0.0007287853,0.0004990606,0.0073887473,0.0025401763,0.020202853,0.014271061,0.016190674,0.55102843],"study_design_scores_gemma":[0.00068524573,0.006548361,0.7485571,0.0008254597,0.0019945763,0.0029313774,0.023638828,0.13116777,0.019556163,0.0127275,0.051128697,0.00023895959],"about_ca_topic_score_codex":0.001561744,"about_ca_topic_score_gemma":0.0016224085,"teacher_disagreement_score":0.0071996753,"about_ca_system_score_codex":0.00078023586,"about_ca_system_score_gemma":0.00045261194,"threshold_uncertainty_score":0.038075984},"labels":[],"label_agreement":null},{"id":"W2555115574","doi":"10.1109/honet.2016.7753440","title":"Comparison of emotion lexicons","year":2016,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Lexicon; Computer science; Construct (python library); Natural language processing; Emotion classification; Artificial intelligence; Emotion detection; Process (computing); Test (biology); Emotion recognition","score_opus":0.05720946544017155,"score_gpt":0.34406399205216104,"score_spread":0.2868545266119895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2555115574","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7006553,0.013389331,0.13535278,0.0018054951,0.0016616993,0.0012308215,0.047514822,0.011945161,0.08644467],"genre_scores_gemma":[0.73522043,0.00473646,0.095743,0.00065035705,0.00021510466,0.0009930985,0.15354301,0.0014743273,0.007424283],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977919,0.00046540407,0.00050880265,0.00035143478,0.00073533616,0.000147138],"domain_scores_gemma":[0.99561644,0.001983388,0.00023311513,0.0003831532,0.001615225,0.00016877861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017396227,0.000910921,0.0006433688,0.0040962393,0.00058418675,0.002375556,0.0007178026,0.00054757216,0.0031643843],"category_scores_gemma":[0.01234183,0.0002611186,0.00086438993,0.0030029395,0.0003725084,0.003277019,0.0015351658,0.00073137926,0.0019241021],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024431115,0.0006492582,0.036296517,0.0029209587,0.0007469368,0.0004796129,0.0011606977,0.01158992,0.037485372,0.011319427,0.075089104,0.8198191],"study_design_scores_gemma":[0.0009419726,0.0016202844,0.17270553,0.0012862034,0.0016861537,0.0036183323,0.007648005,0.3101844,0.06687672,0.038868695,0.39410242,0.00046129333],"about_ca_topic_score_codex":0.0040293364,"about_ca_topic_score_gemma":0.005112116,"teacher_disagreement_score":0.0040962393,"about_ca_system_score_codex":0.0010902575,"about_ca_system_score_gemma":0.0011719877,"threshold_uncertainty_score":0.010585904},"labels":[],"label_agreement":null},{"id":"W2556427464","doi":"10.1016/j.inffus.2016.11.011","title":"Fusing and mining opinions for reputation generation","year":2016,"lang":"en","type":"article","venue":"Information Fusion","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Higher Education Discipline Innovation Project; National Natural Science Foundation of China","keywords":"Reputation; Computer science; Popularity; Sentiment analysis; Generality; Preference; Public opinion; Principal (computer security); The Internet; Data science; World Wide Web; Information retrieval; Artificial intelligence; Psychology; Computer security; Mathematics","score_opus":0.02967303705003585,"score_gpt":0.26702199492535544,"score_spread":0.2373489578753196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2556427464","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14070709,0.0012704886,0.84466296,0.0012295821,0.00045730587,0.00034205354,0.0011403476,0.0022651271,0.007925064],"genre_scores_gemma":[0.7701158,0.00047172175,0.22387107,0.00015470789,0.0004348753,0.00014162729,0.0015090726,0.00012464855,0.0031764335],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997769,0.000515757,0.00022804464,0.00039062643,0.00091664953,0.00017993458],"domain_scores_gemma":[0.995139,0.001503325,0.0004859093,0.00040908883,0.0022609716,0.00020169315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027146665,0.0010702121,0.0012391809,0.004602218,0.00078450487,0.0016139239,0.00086770725,0.0010552126,0.0024293049],"category_scores_gemma":[0.010774667,0.00032613467,0.0010858647,0.0028996388,0.00024432087,0.002506853,0.0009524684,0.0010245743,0.0018706305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006266124,0.00057871896,0.016414966,0.000352668,0.00045490864,0.00038747082,0.0004905974,0.018817043,0.044506002,0.00549474,0.015252834,0.89662355],"study_design_scores_gemma":[0.000031574567,0.00037453967,0.010138953,0.00005729036,0.00036502737,0.00022437121,0.00033387466,0.9460776,0.023489585,0.013126719,0.0057019764,0.00007848165],"about_ca_topic_score_codex":0.0017043053,"about_ca_topic_score_gemma":0.003199597,"teacher_disagreement_score":0.004602218,"about_ca_system_score_codex":0.00059249136,"about_ca_system_score_gemma":0.00072134624,"threshold_uncertainty_score":0.014356673},"labels":[],"label_agreement":null},{"id":"W2560450848","doi":"","title":"Empirical Evaluation of Automated Sentiment Analysis as a Decision Aid","year":2016,"lang":"en","type":"article","venue":"International Conference on Information Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Sentiment analysis; Decision tree; Artificial intelligence; Data science","score_opus":0.08401158892468724,"score_gpt":0.3881525171092334,"score_spread":0.30414092818454613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2560450848","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98290193,0.00058767834,0.0088138515,0.0003171209,0.00011511753,0.00022647556,0.0009248555,0.0002934685,0.0058195447],"genre_scores_gemma":[0.9853205,0.00016156609,0.011076706,0.0000661145,0.00006970814,0.0001105881,0.0015994709,0.000040474424,0.0015548798],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98971784,0.0071934513,0.00048533597,0.00048080788,0.0018878898,0.00023469135],"domain_scores_gemma":[0.9155972,0.067584656,0.0022588177,0.002758523,0.010849352,0.0009514253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011270775,0.0007193222,0.00044246283,0.0018894811,0.0005966631,0.0014146967,0.0008985479,0.0008327548,0.0032402077],"category_scores_gemma":[0.048654336,0.00018652307,0.0003529813,0.0012547636,0.0004528694,0.0013867147,0.000928204,0.0006019042,0.0011507915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.01683127,0.012374695,0.24313812,0.0018473929,0.0010768002,0.00054537685,0.002241314,0.027691899,0.033625226,0.003277311,0.025035089,0.63231564],"study_design_scores_gemma":[0.0016127217,0.010413781,0.25895247,0.0002814829,0.0010509354,0.000718222,0.0022728853,0.66820675,0.03605351,0.0034289756,0.016868418,0.00013986463],"about_ca_topic_score_codex":0.0018958325,"about_ca_topic_score_gemma":0.0022216286,"teacher_disagreement_score":0.011270775,"about_ca_system_score_codex":0.0005923251,"about_ca_system_score_gemma":0.00055242336,"threshold_uncertainty_score":0.059606254},"labels":[],"label_agreement":null},{"id":"W2562910494","doi":"10.1162/coli_a_00278","title":"Evaluative Language Beyond Bags of Words: Linguistic Insights and Computational Applications","year":2016,"lang":"en","type":"article","venue":"Computational Linguistics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":80,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computational linguistics; Perspective (graphical); Computer science; Linguistics; Field (mathematics); Sociocultural linguistics; Subjectivity; Multidisciplinary approach; Interpretation (philosophy); Sentiment analysis; Affect (linguistics); Artificial intelligence; Natural language processing; Sociology; Natural language; Epistemology; Social science; Comprehension approach","score_opus":0.01678670568591051,"score_gpt":0.3042419730166649,"score_spread":0.2874552673307544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2562910494","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043684166,0.037960634,0.8640242,0.013294141,0.00076194137,0.0002352429,0.000699972,0.00058639835,0.03875321],"genre_scores_gemma":[0.6299666,0.022022178,0.33824098,0.0018994726,0.0014595254,0.00047672717,0.00082015904,0.00024918318,0.0048651593],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972778,0.0016311021,0.00024260473,0.00032613953,0.00042961826,0.000092631184],"domain_scores_gemma":[0.9872922,0.01020006,0.0009754097,0.0005496491,0.0008190267,0.00016367527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004224278,0.00086807733,0.0010691411,0.004118989,0.0010108777,0.007102072,0.0014045929,0.0009520444,0.0037692043],"category_scores_gemma":[0.022440495,0.00059029367,0.0011767072,0.00466716,0.0039264117,0.010803313,0.0020930595,0.002412041,0.00073902524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000083247294,0.00004321932,0.0023965575,0.0011548261,0.00013612454,0.00018294078,0.0021890574,0.009194092,0.0012210296,0.830022,0.0063043823,0.1470725],"study_design_scores_gemma":[0.000008590311,0.000020067258,0.0013323373,0.00031298582,0.000030632196,0.00011579095,0.000697417,0.046039723,0.00043262204,0.9341123,0.016865829,0.00003172772],"about_ca_topic_score_codex":0.0013691096,"about_ca_topic_score_gemma":0.0012955748,"teacher_disagreement_score":0.007102072,"about_ca_system_score_codex":0.0017056763,"about_ca_system_score_gemma":0.0008935955,"threshold_uncertainty_score":0.022340417},"labels":[],"label_agreement":null},{"id":"W2566371330","doi":"10.1109/dsaa.2016.80","title":"Word Segmentation Algorithms with Lexical Resources for Hashtag Classification","year":2016,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Artificial intelligence; Natural language processing; Segmentation; Precision and recall; Word (group theory); Text segmentation; Baseline (sea); Information retrieval; Linguistics","score_opus":0.04404161615383883,"score_gpt":0.2917415200383319,"score_spread":0.2476999038844931,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2566371330","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029148405,0.00061050704,0.95191586,0.0002701647,0.00014949794,0.0006360661,0.0010758372,0.010841793,0.005351744],"genre_scores_gemma":[0.12376046,0.00031022573,0.8682378,0.00022218043,0.00015031431,0.0007396504,0.0034690825,0.0006655609,0.0024446668],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982565,0.0003659273,0.00026253064,0.00047196963,0.0005104274,0.00013269723],"domain_scores_gemma":[0.99658966,0.0015539977,0.00034513333,0.00045246113,0.0009310632,0.00012775391],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013739086,0.0014717649,0.0011963772,0.0071853194,0.001263318,0.0025645238,0.001409357,0.0012256906,0.0057267486],"category_scores_gemma":[0.006277617,0.00067393354,0.0012468998,0.0052682813,0.00082841975,0.0045847846,0.0015445711,0.001575647,0.0070124627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044537208,0.00036615034,0.00533215,0.00052265363,0.00022399469,0.00029861784,0.0006530464,0.007592435,0.07827613,0.011542101,0.014102084,0.8806452],"study_design_scores_gemma":[0.00022216316,0.00043983877,0.009915112,0.00021584582,0.00031846628,0.00097198214,0.001292034,0.74748224,0.10899243,0.07587354,0.05402921,0.00024712764],"about_ca_topic_score_codex":0.003475944,"about_ca_topic_score_gemma":0.005895203,"teacher_disagreement_score":0.0071853194,"about_ca_system_score_codex":0.0008989414,"about_ca_system_score_gemma":0.0016060813,"threshold_uncertainty_score":0.019157887},"labels":[],"label_agreement":null},{"id":"W2567305905","doi":"10.1109/cbi.2016.42","title":"Predicting Political Donations Using Twitter Hashtags and Character N-Grams","year":2016,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Psychographic; Social media; General election; Sentiment analysis; Voting; Federal election; Politics; Data science; Artificial intelligence; World Wide Web; Advertising; Political science","score_opus":0.04700899733343586,"score_gpt":0.28962073781591835,"score_spread":0.2426117404824825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2567305905","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8845311,0.00061776454,0.09880752,0.00072692777,0.00015203905,0.00024039135,0.0050819246,0.0019740618,0.007868269],"genre_scores_gemma":[0.9571357,0.00018462416,0.036087107,0.00004746513,0.00008836317,0.00006593187,0.003354307,0.000024597219,0.0030118856],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997137,0.000069896494,0.00002499043,0.00004897361,0.00008653474,0.00005586767],"domain_scores_gemma":[0.9989895,0.00043876775,0.00017060054,0.00008088673,0.000224653,0.00009550494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006927757,0.0008364039,0.00035593638,0.0033281092,0.00046476716,0.00075192953,0.00031911078,0.00050272065,0.0023179937],"category_scores_gemma":[0.002066298,0.00017091434,0.00039539026,0.0016693121,0.00018176388,0.0012527996,0.00060457794,0.0005403911,0.0014947348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010036636,0.0008826173,0.36938247,0.00024491406,0.00017527037,0.000324343,0.00045491866,0.034663796,0.015951019,0.0025543275,0.0090555055,0.5653071],"study_design_scores_gemma":[0.00002604789,0.00030334908,0.13714685,0.000043388955,0.00006294204,0.00019522992,0.0006569688,0.8372907,0.011974337,0.0047058426,0.0075465827,0.00004775854],"about_ca_topic_score_codex":0.005033959,"about_ca_topic_score_gemma":0.015088486,"teacher_disagreement_score":0.005033959,"about_ca_system_score_codex":0.000490296,"about_ca_system_score_gemma":0.00054388505,"threshold_uncertainty_score":0.010009348},"labels":[],"label_agreement":null},{"id":"W2568245765","doi":"10.1109/besc.2016.7804475","title":"Interpolative self-training approach for sentiment analysis","year":2016,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Sentiment analysis; Weighting; Task (project management); Artificial intelligence; Selection (genetic algorithm); Training (meteorology); Training set; Extension (predicate logic); Machine learning; Baseline (sea); Co-training; Natural language processing; Semi-supervised learning","score_opus":0.035914935477438494,"score_gpt":0.2789614180251327,"score_spread":0.2430464825476942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2568245765","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036966696,0.0003885552,0.95883226,0.00015597748,0.00007868701,0.00012249961,0.000093733826,0.001351789,0.002009845],"genre_scores_gemma":[0.6203972,0.0004137819,0.37239638,0.00027999553,0.00015765829,0.00033236993,0.00068072084,0.0001884024,0.0051534264],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994066,0.00020413318,0.00004722419,0.00015114654,0.00013815345,0.00005273759],"domain_scores_gemma":[0.9985688,0.000711799,0.00011646726,0.00015953399,0.0004036075,0.000039807877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001782433,0.00065851986,0.00064333086,0.0008749336,0.00039106904,0.00052782014,0.0010638189,0.0007961608,0.0022919767],"category_scores_gemma":[0.0034121447,0.0003382671,0.0007797717,0.0007287991,0.0004538742,0.0012760819,0.00082873914,0.0012140122,0.0010130208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003562661,0.00037409164,0.0043466548,0.0002303088,0.00012616727,0.00015851794,0.00047292365,0.20471138,0.024326872,0.0055479337,0.005593618,0.7537553],"study_design_scores_gemma":[0.0000068908716,0.00005992477,0.00047702642,0.000010553259,0.000011795025,0.000030915806,0.000022785518,0.992149,0.004066365,0.0019393465,0.001218165,0.0000071967247],"about_ca_topic_score_codex":0.0013554855,"about_ca_topic_score_gemma":0.001954357,"teacher_disagreement_score":0.0022919767,"about_ca_system_score_codex":0.00041725335,"about_ca_system_score_gemma":0.00051243196,"threshold_uncertainty_score":0.009426534},"labels":[],"label_agreement":null},{"id":"W2572163506","doi":"10.63317/4do4wcj98df7","title":"A Dataset for Detecting Stance in Tweets","year":2016,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":80,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; University of Ottawa","funders":"","keywords":"Computer science; Crowdsourcing; Sentiment analysis; Inference; Set (abstract data type); Information retrieval; Natural language processing; Annotation; Quality (philosophy); Artificial intelligence; Product (mathematics); World Wide Web","score_opus":0.03759109563344783,"score_gpt":0.3030580805172854,"score_spread":0.2654669848838376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2572163506","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049432162,0.00039308597,0.0021819551,0.0005949978,0.00030092188,0.0005538323,0.9382572,0.0018908115,0.0063949474],"genre_scores_gemma":[0.025787046,0.00020147409,0.008189361,0.0001491815,0.00010321534,0.0006720378,0.9612549,0.0000728893,0.0035699334],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992269,0.00010692651,0.0001306454,0.00012790364,0.00029695523,0.000110572044],"domain_scores_gemma":[0.99724007,0.0007042154,0.00039141567,0.000353162,0.0009366688,0.0003744455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060918333,0.0010028998,0.00046750513,0.004741868,0.0010418121,0.0008784516,0.0007353475,0.0013966019,0.0059650117],"category_scores_gemma":[0.0030237827,0.00025395706,0.00053892663,0.0035672481,0.00020233827,0.00089539634,0.0009443207,0.00080348615,0.0070730057],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008060776,0.0008473598,0.026774636,0.0017372845,0.00016122058,0.0006036044,0.0005247994,0.0014915871,0.02161566,0.0022585562,0.8521111,0.09106816],"study_design_scores_gemma":[0.00060281757,0.00051114155,0.14941399,0.00034447908,0.00020364756,0.0012230676,0.0016700957,0.015540017,0.017942818,0.0029249017,0.80945116,0.00017186682],"about_ca_topic_score_codex":0.0079627065,"about_ca_topic_score_gemma":0.023315502,"teacher_disagreement_score":0.0079627065,"about_ca_system_score_codex":0.0008218282,"about_ca_system_score_gemma":0.0015133324,"threshold_uncertainty_score":0.01995498},"labels":[],"label_agreement":null},{"id":"W2572452825","doi":"","title":"Selective Co-occurrences for Word-Emotion Association","year":2016,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Word (group theory); Computer science; Association (psychology); Natural language processing; Word Association; Artificial intelligence; Task (project management); Emotion classification; Semantic similarity; Psychology; Linguistics","score_opus":0.05802061708972685,"score_gpt":0.35514580214277025,"score_spread":0.2971251850530434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2572452825","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20722497,0.0011216436,0.7810143,0.00030619334,0.00018040436,0.0003273053,0.0016468927,0.0043106615,0.0038675084],"genre_scores_gemma":[0.6869684,0.00050257344,0.30145225,0.0001298968,0.00020384461,0.00061737053,0.006273242,0.00052315043,0.0033293131],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99828655,0.0003995385,0.00016314095,0.0007592672,0.00026350198,0.00012794044],"domain_scores_gemma":[0.9954513,0.0022633947,0.0005541928,0.00085824996,0.0006903378,0.00018259451],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014017279,0.0011320242,0.0010856941,0.003874016,0.0007703872,0.0010659498,0.0009654629,0.0008716413,0.002212543],"category_scores_gemma":[0.008338585,0.00039286222,0.0010626677,0.003845321,0.0007743163,0.0030649172,0.002146328,0.0015118719,0.0023064031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009174516,0.00054486806,0.04328621,0.0005027491,0.00034205228,0.00026408673,0.0011132419,0.013343102,0.038434748,0.0050218888,0.008052931,0.88817674],"study_design_scores_gemma":[0.00008639182,0.00039840225,0.04320947,0.00009351523,0.0002173042,0.0010567806,0.0009764948,0.8788162,0.03489234,0.028416092,0.011714363,0.0001227022],"about_ca_topic_score_codex":0.0010896896,"about_ca_topic_score_gemma":0.0035490287,"teacher_disagreement_score":0.003874016,"about_ca_system_score_codex":0.00036824387,"about_ca_system_score_gemma":0.0009595037,"threshold_uncertainty_score":0.0074130893},"labels":[],"label_agreement":null},{"id":"W2573147668","doi":"10.1109/wi.2016.0097","title":"Tweet Sentiment Analysis by Incorporating Sentiment-Specific Word Embedding and Weighted Text Features","year":2016,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sentiment analysis; Computer science; Word embedding; Artificial intelligence; Weighting; Word (group theory); Feature (linguistics); Negation; Natural language processing; Embedding; Mathematics; Linguistics","score_opus":0.011883222000929691,"score_gpt":0.2532983838070067,"score_spread":0.241415161806077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2573147668","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.119850986,0.0004221123,0.8716898,0.00034810504,0.0002817194,0.0002820592,0.0012730318,0.0018401449,0.0040121],"genre_scores_gemma":[0.69088006,0.00048680732,0.3001765,0.000092782226,0.00025108247,0.00024162719,0.002993929,0.00014588177,0.004731366],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972886,0.00006221271,0.000032231143,0.00005633205,0.000094462135,0.000025861184],"domain_scores_gemma":[0.99932826,0.00017715228,0.00011703937,0.000059875543,0.0002932542,0.000024479406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004979007,0.00072899426,0.00040050913,0.002068223,0.00018881203,0.0006497908,0.00032390305,0.00032704478,0.0011496142],"category_scores_gemma":[0.0022629478,0.00016039601,0.00062880345,0.0012146762,0.00015782163,0.0018619496,0.0004386201,0.0005512901,0.0008244644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032117282,0.00035819918,0.016635412,0.00033713877,0.00029662807,0.00021028084,0.00025037664,0.027226778,0.068619184,0.0057615247,0.010975011,0.86900836],"study_design_scores_gemma":[0.000019052506,0.000204658,0.008548469,0.000028761979,0.00009703364,0.00015259188,0.00016715491,0.9619539,0.015572591,0.006304668,0.0069084237,0.00004274016],"about_ca_topic_score_codex":0.0014290184,"about_ca_topic_score_gemma":0.0024266972,"teacher_disagreement_score":0.002068223,"about_ca_system_score_codex":0.0002629155,"about_ca_system_score_gemma":0.00027175428,"threshold_uncertainty_score":0.0038458705},"labels":[],"label_agreement":null},{"id":"W2575509578","doi":"","title":"Cultural micro-blog Contextualization 2016 Workshop Overview: data and pilot tasks","year":2016,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Contextualization; Clef; Computer science; Set (abstract data type); World Wide Web; Data science; Social media; Microblogging; Information retrieval; Engineering","score_opus":0.05803436753107027,"score_gpt":0.2942271212126183,"score_spread":0.23619275368154802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2575509578","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.518971,0.005036166,0.057669982,0.0054185884,0.0038149368,0.03809154,0.27783942,0.009078427,0.08407991],"genre_scores_gemma":[0.4415896,0.0025168706,0.09403708,0.0018278023,0.0012699041,0.065670826,0.31275576,0.002662035,0.077670135],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970601,0.0009673366,0.00023298475,0.0005908663,0.00065537967,0.00049325224],"domain_scores_gemma":[0.98820937,0.0020586739,0.00056975585,0.0018678014,0.0043825684,0.0029118871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057723764,0.0013393838,0.0011664301,0.002315677,0.0024975722,0.0027094563,0.0012292818,0.001234605,0.01602577],"category_scores_gemma":[0.011453438,0.0005878205,0.0011139537,0.002677893,0.0005713647,0.0019352529,0.005000813,0.001819873,0.018658048],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005360811,0.003858676,0.063188225,0.008029328,0.0002901351,0.0009524497,0.018425247,0.0033249692,0.046498504,0.0023945724,0.48352653,0.3641506],"study_design_scores_gemma":[0.0009193328,0.0022124618,0.263854,0.0015868929,0.0003610994,0.0004349841,0.025897538,0.0050648344,0.03269249,0.0042117853,0.66236746,0.0003970976],"about_ca_topic_score_codex":0.0069597443,"about_ca_topic_score_gemma":0.015535864,"teacher_disagreement_score":0.01602577,"about_ca_system_score_codex":0.00089996203,"about_ca_system_score_gemma":0.0038043407,"threshold_uncertainty_score":0.053611577},"labels":[],"label_agreement":null},{"id":"W2576441922","doi":"10.21700/ijcis.2016.118","title":"Sentiment Analysis of Arabic Tweets Using Semantic Resources","year":2016,"lang":"en","type":"article","venue":"International Journal of Computing and Information Sciences","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Arabic; Sentiment analysis; Natural language processing; Computer science; Artificial intelligence; Information retrieval; Linguistics; Philosophy","score_opus":0.023742938545668366,"score_gpt":0.3167267670036387,"score_spread":0.29298382845797033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2576441922","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9075865,0.0010596173,0.051830336,0.001268987,0.00064341916,0.00047779686,0.009548723,0.0010679498,0.026516682],"genre_scores_gemma":[0.94842523,0.000576121,0.03796107,0.00012175579,0.00025501073,0.000232827,0.0076023606,0.00008423818,0.0047413297],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995747,0.000117687036,0.000053847358,0.000048566464,0.00014633835,0.000058815556],"domain_scores_gemma":[0.99908876,0.00030905002,0.000101745,0.000030689233,0.00043144965,0.000038283146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005602196,0.0005066369,0.00028456326,0.002733602,0.00075419503,0.0010624845,0.0001493087,0.00025040488,0.0026947919],"category_scores_gemma":[0.0020693196,0.00007849648,0.00047024176,0.0020369356,0.00019017163,0.0009989662,0.00045605,0.00039159355,0.001203592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0031020057,0.0006267759,0.058219638,0.0020545323,0.00035873713,0.0017635077,0.0051849773,0.0044116946,0.20047852,0.011354302,0.044286013,0.6681592],"study_design_scores_gemma":[0.00018965479,0.0013298357,0.23100327,0.0007823757,0.0010940651,0.0025975239,0.025981918,0.34686494,0.19192767,0.018793155,0.1791201,0.00031548375],"about_ca_topic_score_codex":0.0012981273,"about_ca_topic_score_gemma":0.0016207919,"teacher_disagreement_score":0.002733602,"about_ca_system_score_codex":0.00042178112,"about_ca_system_score_gemma":0.0004125068,"threshold_uncertainty_score":0.009014964},"labels":[],"label_agreement":null},{"id":"W2577073647","doi":"10.1109/ictai.2016.0069","title":"Improving Deep Belief Networks via Delta Rule for Sentiment Classification","year":2016,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Deep belief network; Boltzmann machine; Computer science; Artificial intelligence; Restricted Boltzmann machine; Layer (electronics); Artificial neural network; Deep learning; Backpropagation; Sentiment analysis; Machine learning; Unsupervised learning; Pattern recognition (psychology); Natural language processing","score_opus":0.0200385010352133,"score_gpt":0.2538341298858324,"score_spread":0.23379562885061914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2577073647","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055001628,0.00091070833,0.93870974,0.00039606134,0.00019404844,0.00007984446,0.00014481368,0.0017113016,0.0028518364],"genre_scores_gemma":[0.6855514,0.00070057623,0.3079783,0.00050611846,0.00015912144,0.00016930832,0.00088373455,0.00017222235,0.003879191],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991689,0.00021925612,0.00008375959,0.00016920031,0.0002746463,0.00008426129],"domain_scores_gemma":[0.99821836,0.00076549297,0.00015767083,0.00016215217,0.0006287148,0.00006765544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020113487,0.0009933795,0.0010453176,0.001018961,0.00038269648,0.0010017543,0.0015496048,0.0009261505,0.0014679353],"category_scores_gemma":[0.0061853174,0.00045653628,0.00070630596,0.00079454074,0.00041491253,0.0018538638,0.0010431206,0.00224239,0.00093420886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002674038,0.00028974898,0.0037483985,0.00013821859,0.00014786917,0.00012277797,0.00009515347,0.29398537,0.008661843,0.007572407,0.0046509095,0.68031996],"study_design_scores_gemma":[0.000007365865,0.000021803844,0.00012067628,0.0000054309326,0.000008474737,0.000008262274,0.0000044291755,0.99608153,0.0011628574,0.0023224645,0.00025332315,0.0000033778106],"about_ca_topic_score_codex":0.0039642486,"about_ca_topic_score_gemma":0.0043139937,"teacher_disagreement_score":0.0039642486,"about_ca_system_score_codex":0.0007350445,"about_ca_system_score_gemma":0.0008723543,"threshold_uncertainty_score":0.0106371045},"labels":[],"label_agreement":null},{"id":"W2577454849","doi":"10.63317/28kytu8w5m87","title":"Sentiment Lexicons for Arabic Social Media","year":2016,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Lexicon; Computer science; Natural language processing; Sentiment analysis; Artificial intelligence; Arabic; Sentence; Modern Standard Arabic; Word (group theory); Machine translation; Linguistics","score_opus":0.0433223304705549,"score_gpt":0.28254421852597483,"score_spread":0.23922188805541994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2577454849","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0713475,0.0019610838,0.6091612,0.0020349638,0.0014148232,0.0037874638,0.13955757,0.06527697,0.10545834],"genre_scores_gemma":[0.24616905,0.0016589351,0.5519627,0.0007236284,0.0003841124,0.003380502,0.16621558,0.0055792066,0.023926344],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99933654,0.00016869407,0.00016713406,0.000092054746,0.00018593886,0.00004958504],"domain_scores_gemma":[0.99777335,0.00052756304,0.00033929962,0.00019870188,0.0010776644,0.0000833627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096618565,0.001267926,0.00040321893,0.004102329,0.00097331096,0.0017360087,0.0005095939,0.0003439039,0.025304986],"category_scores_gemma":[0.0059979525,0.00049534795,0.00087766437,0.00226651,0.0003578204,0.0023355694,0.001330116,0.0009084326,0.019261302],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00080364576,0.00023050429,0.007924085,0.0032039823,0.00017315583,0.0013963104,0.0029305308,0.0037965572,0.07508566,0.03398195,0.29521707,0.5752566],"study_design_scores_gemma":[0.00015230443,0.00017315563,0.013514283,0.00071335444,0.0001337104,0.0014680916,0.0019315293,0.069375835,0.045530137,0.030629732,0.8361753,0.00020255605],"about_ca_topic_score_codex":0.0028672758,"about_ca_topic_score_gemma":0.0037526942,"teacher_disagreement_score":0.025304986,"about_ca_system_score_codex":0.00097397517,"about_ca_system_score_gemma":0.0011968993,"threshold_uncertainty_score":0.08465356},"labels":[],"label_agreement":null},{"id":"W2582886532","doi":"10.1609/aaai.v31i1.11111","title":"Discovering Conversational Dependencies between Messages in Dialogs","year":2017,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Natural language processing; Probabilistic logic; Classifier (UML); Task (project management); Set (abstract data type); Customer service; Artificial intelligence; Information retrieval; Heuristic; Online chat; Service (business); World Wide Web; The Internet","score_opus":0.12033444732769798,"score_gpt":0.3256685017205043,"score_spread":0.20533405439280633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2582886532","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.708367,0.002329317,0.2767064,0.0009580941,0.000104360675,0.0003127857,0.0032871603,0.002161723,0.005773199],"genre_scores_gemma":[0.9552625,0.00034551098,0.040142577,0.00007560337,0.0001413702,0.000106133724,0.0025253533,0.00007729173,0.0013236941],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986614,0.00043053651,0.00008052265,0.00043343435,0.00025162695,0.00014248332],"domain_scores_gemma":[0.99082094,0.006998223,0.000734719,0.00032977344,0.00079257984,0.0003237938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015696461,0.0010845058,0.0006897479,0.002690762,0.0012044929,0.0010345569,0.00082715566,0.0009847662,0.0013677536],"category_scores_gemma":[0.009615527,0.00053469563,0.0006803939,0.0010200659,0.00040754766,0.0026599346,0.00097404805,0.0013373393,0.001078787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002232578,0.0018138739,0.18451405,0.0012227994,0.00040662297,0.0019305972,0.0057243016,0.053004336,0.080012105,0.0064091166,0.01249752,0.65023214],"study_design_scores_gemma":[0.000036625122,0.00023834936,0.057520613,0.000069841204,0.00017927717,0.00041642663,0.00081016903,0.910394,0.015861312,0.009139872,0.0052680112,0.000065653425],"about_ca_topic_score_codex":0.007282442,"about_ca_topic_score_gemma":0.01142543,"teacher_disagreement_score":0.007282442,"about_ca_system_score_codex":0.0006091819,"about_ca_system_score_gemma":0.0011392881,"threshold_uncertainty_score":0.014480114},"labels":[],"label_agreement":null},{"id":"W2583888098","doi":"10.1109/icdmw.2016.0139","title":"Multi-sentiment Modeling with Scalable Systematic Labeled Data Generation via Word2Vec Clustering","year":2016,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Systems, Applications & Products in Data Processing (Canada)","funders":"","keywords":"Word2vec; Computer science; Sentiment analysis; Scalability; Cluster analysis; Emoji; Social media; Classifier (UML); Binary classification; Artificial intelligence; Machine learning; Big data; Data mining; Data science; World Wide Web; Support vector machine","score_opus":0.09569589603432707,"score_gpt":0.2842106160174,"score_spread":0.18851471998307293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2583888098","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07918423,0.00029408978,0.9024179,0.0005277823,0.0003396848,0.00039983718,0.0030511692,0.011429038,0.0023561942],"genre_scores_gemma":[0.42895082,0.0002024728,0.5491069,0.000505687,0.00015406133,0.0009186009,0.015858488,0.0007525508,0.0035504291],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993969,0.00016598536,0.00004083243,0.00019202943,0.00012332643,0.000080833175],"domain_scores_gemma":[0.9987419,0.00041142563,0.00007974366,0.00018930894,0.0005370026,0.00004064418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084177766,0.0012816235,0.00074753666,0.000804243,0.0005606643,0.00079735083,0.0014571492,0.0008761691,0.001847658],"category_scores_gemma":[0.0032635892,0.0006225499,0.0010952266,0.000898048,0.00036155817,0.0012251711,0.00095828995,0.0014122791,0.0018896853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042454456,0.00050640275,0.0070139803,0.0002653151,0.00023114293,0.00029338614,0.0004041033,0.44309184,0.024444843,0.0052092266,0.04038633,0.4777289],"study_design_scores_gemma":[0.000014288603,0.000026410255,0.00029522696,0.0000044070607,0.0000071599243,0.000012466235,0.000028664645,0.9941497,0.002640477,0.0019198275,0.00089368055,0.0000078143285],"about_ca_topic_score_codex":0.011460707,"about_ca_topic_score_gemma":0.021140544,"teacher_disagreement_score":0.011460707,"about_ca_system_score_codex":0.0008001556,"about_ca_system_score_gemma":0.001156453,"threshold_uncertainty_score":0.022788048},"labels":[],"label_agreement":null},{"id":"W2592238845","doi":"10.1007/978-3-319-55209-5_8","title":"Topic-Based Sentiment Analysis","year":2017,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; SemEval; Sentence; Natural language processing; Classifier (UML); Artificial intelligence; Dependency (UML); Parsing; Polarity (international relations); Dependency grammar; Sentiment analysis; Exploit; Task (project management); Information retrieval","score_opus":0.050679811316329725,"score_gpt":0.32025495745484667,"score_spread":0.26957514613851696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2592238845","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026527455,0.00953869,0.8716933,0.0020432845,0.00267529,0.0004063792,0.0039772694,0.0050573195,0.07808098],"genre_scores_gemma":[0.32458818,0.013097388,0.53568095,0.0007967381,0.003120366,0.000731991,0.01721417,0.0020829923,0.10268729],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99941397,0.00012397948,0.00005170028,0.000118982614,0.00023930488,0.000052053074],"domain_scores_gemma":[0.9991547,0.00027312772,0.00006503371,0.00005629465,0.00042001953,0.000030924686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011862263,0.00090052607,0.0006131609,0.0027981235,0.00053515815,0.0021529438,0.00057855225,0.00046906952,0.008803517],"category_scores_gemma":[0.0029482492,0.00026779363,0.0009953063,0.003168588,0.00023889198,0.0016758172,0.000829845,0.0011756484,0.0071851937],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000094094365,0.000073488896,0.0015747836,0.00035172098,0.00014641897,0.00006993874,0.00031767905,0.0027903873,0.019391783,0.014667193,0.07731038,0.883212],"study_design_scores_gemma":[0.000060163045,0.00021435937,0.019187337,0.0004902165,0.0005030192,0.0010053526,0.0010876209,0.36640128,0.045060173,0.093425326,0.47240275,0.00016237116],"about_ca_topic_score_codex":0.0007709284,"about_ca_topic_score_gemma":0.0011655416,"teacher_disagreement_score":0.008803517,"about_ca_system_score_codex":0.00050927466,"about_ca_system_score_gemma":0.00049171434,"threshold_uncertainty_score":0.029450715},"labels":[],"label_agreement":null},{"id":"W2592926491","doi":"10.1109/eisic.2016.027","title":"Sentiment-based Classification of Radical Text on the Web","year":2016,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Web page; Web crawler; The Internet; Information retrieval; World Wide Web; Sentiment analysis; Simple (philosophy); Islam; Artificial intelligence; History; Philosophy","score_opus":0.036888769979658065,"score_gpt":0.2654429356297391,"score_spread":0.22855416565008102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2592926491","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.986927,0.000100863086,0.00646438,0.00010329921,0.00004529689,0.0001943147,0.0017756578,0.00017626402,0.004212924],"genre_scores_gemma":[0.97795063,0.00010476099,0.01616979,0.000033815697,0.000049046714,0.000114592156,0.0038207003,0.000029895344,0.0017267722],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995857,0.00011376141,0.000057207246,0.00005407809,0.00013686855,0.0000523852],"domain_scores_gemma":[0.99777776,0.000885152,0.00020495609,0.00009987334,0.0009332914,0.00009898466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090419216,0.00027251188,0.00032428434,0.0021504795,0.0003442785,0.0005947522,0.00021488528,0.0002542199,0.0012328746],"category_scores_gemma":[0.0031246496,0.0000846262,0.00030304215,0.0011199543,0.00017724857,0.00057346985,0.00030865218,0.0003126635,0.0007357919],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026870563,0.001248012,0.33142537,0.00081012904,0.000224146,0.0010912056,0.0031217816,0.008709687,0.13751759,0.0022271443,0.020671347,0.49026656],"study_design_scores_gemma":[0.00008718836,0.000675589,0.59009635,0.000105488274,0.0002167757,0.00047915388,0.0036340617,0.35159963,0.040736392,0.0023864417,0.009919431,0.00006341764],"about_ca_topic_score_codex":0.0017715017,"about_ca_topic_score_gemma":0.0024217984,"teacher_disagreement_score":0.0021504795,"about_ca_system_score_codex":0.00033880144,"about_ca_system_score_gemma":0.00020714113,"threshold_uncertainty_score":0.004781842},"labels":[],"label_agreement":null},{"id":"W2596174778","doi":"10.1109/bigdataservice.2017.11","title":"Improved Sentiment Classification by Multi-Modal Fusion","year":2017,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Lakehead University","funders":"","keywords":"Computer science; Sentiment analysis; Modal; Focus (optics); Task (project management); Microblogging; Social media; Metric (unit); Artificial intelligence; Fusion; Natural language processing; Machine learning; Information fusion; Information retrieval; World Wide Web; Linguistics; Engineering","score_opus":0.04390466990785003,"score_gpt":0.3092105111693825,"score_spread":0.26530584126153245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2596174778","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058594957,0.0008375455,0.9327912,0.00048029152,0.0003296492,0.00012580262,0.00031471413,0.0020263412,0.0044995486],"genre_scores_gemma":[0.60946864,0.00052810763,0.38333765,0.00036768857,0.00034374322,0.00017496513,0.0013860576,0.00019127247,0.004201991],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905974,0.0002154216,0.000067005916,0.00021284344,0.00032040194,0.00012461084],"domain_scores_gemma":[0.99891746,0.00019835627,0.00008211252,0.00010622614,0.00065344933,0.00004245799],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016427258,0.000885124,0.0010409326,0.0016807418,0.0005972263,0.0013089492,0.000743952,0.00093874463,0.0025214313],"category_scores_gemma":[0.0025782418,0.0002585738,0.0013900978,0.0012070477,0.00031542004,0.0019646203,0.0012082009,0.0010871078,0.001605381],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048788506,0.00026703737,0.0027830473,0.00020423684,0.0002397011,0.00014050682,0.00024357499,0.028263934,0.09273976,0.0041504414,0.010228709,0.8602512],"study_design_scores_gemma":[0.000017519395,0.00010379438,0.0026235885,0.000022067969,0.000086326625,0.000088151995,0.00009921986,0.9714707,0.017332742,0.0050315475,0.003083456,0.000040808463],"about_ca_topic_score_codex":0.0015772172,"about_ca_topic_score_gemma":0.0015920753,"teacher_disagreement_score":0.0025214313,"about_ca_system_score_codex":0.00049149274,"about_ca_system_score_gemma":0.00041440025,"threshold_uncertainty_score":0.008687615},"labels":[],"label_agreement":null},{"id":"W2604547414","doi":"10.1108/pmm-07-2016-0031","title":"Constructing a sentiment analysis model for LibQUAL+ comments","year":2017,"lang":"en","type":"article","venue":"Performance Measurement and Metrics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Sentiment analysis; Originality; Terminology; Service (business); Set (abstract data type); Process (computing); Information retrieval; Data science; Operations research; World Wide Web; Artificial intelligence; Linguistics; Qualitative research; Sociology; Marketing; Mathematics","score_opus":0.14843098060347337,"score_gpt":0.31420248474130114,"score_spread":0.16577150413782776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604547414","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3835181,0.00023873088,0.5885126,0.0024489793,0.00020528022,0.0022023132,0.008871211,0.00295027,0.011052531],"genre_scores_gemma":[0.7525613,0.00018341685,0.2289682,0.0003054076,0.00010408029,0.0024874886,0.0081537925,0.00018020853,0.007056122],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99787784,0.0007893045,0.00018049491,0.00056638446,0.00043650146,0.00014963839],"domain_scores_gemma":[0.9932828,0.0038326334,0.0007377977,0.00028113538,0.0017409435,0.00012461597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00455832,0.0010144768,0.0005421245,0.002315926,0.00070054055,0.0018607546,0.0010625463,0.0007008193,0.0045781694],"category_scores_gemma":[0.01143962,0.0003793039,0.0014508098,0.0013025568,0.0005278272,0.001761925,0.000870136,0.0013150759,0.002848387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013480864,0.0011368574,0.17889369,0.000997956,0.00041128625,0.0009183152,0.006611041,0.13709766,0.02162749,0.019345531,0.034551367,0.5970608],"study_design_scores_gemma":[0.000027285714,0.00015250598,0.020407347,0.000094418705,0.000038944192,0.00011152128,0.0012456265,0.95892346,0.0030040347,0.0075385,0.008407523,0.000048785136],"about_ca_topic_score_codex":0.008081497,"about_ca_topic_score_gemma":0.008046676,"teacher_disagreement_score":0.008081497,"about_ca_system_score_codex":0.0024625126,"about_ca_system_score_gemma":0.0013218921,"threshold_uncertainty_score":0.024107039},"labels":[],"label_agreement":null},{"id":"W2606902231","doi":"10.1177/0165551517703514","title":"Lexicon-based sentiment analysis: Comparative evaluation of six sentiment lexicons","year":2017,"lang":"en","type":"article","venue":"Journal of Information Science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":267,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Nanyang Technological University","keywords":"Lexicon; Sentiment analysis; Computer science; Sentence; Natural language processing; Artificial intelligence; Word (group theory); Product (mathematics); Linguistics; Mathematics","score_opus":0.09062241115274372,"score_gpt":0.3905624171277131,"score_spread":0.2999400059749694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606902231","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.624468,0.009798467,0.23330022,0.001718922,0.0013608242,0.004381546,0.020937733,0.017697703,0.086336575],"genre_scores_gemma":[0.70758206,0.004328355,0.2231202,0.0005764152,0.00029309106,0.0023091773,0.05244909,0.0010298679,0.0083116945],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.995353,0.0012948952,0.00084933767,0.0003611438,0.0019585805,0.0001830933],"domain_scores_gemma":[0.988208,0.00663909,0.0006514842,0.00047391493,0.0036738,0.0003536909],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005324058,0.0012370051,0.0011371808,0.009666724,0.0007351054,0.0030408665,0.00092863257,0.0006309821,0.0032108282],"category_scores_gemma":[0.020762514,0.00031793074,0.0013320594,0.0049733985,0.0004705698,0.0037193224,0.0017864949,0.00067550864,0.0021312383],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017886919,0.0006289107,0.036366556,0.0034624236,0.0009902864,0.0002870038,0.0016556678,0.0048320936,0.02602847,0.003535861,0.044481322,0.87594277],"study_design_scores_gemma":[0.0013080753,0.0024512827,0.24243736,0.0018795406,0.0034683575,0.001846021,0.0092353225,0.51255393,0.065685265,0.01577494,0.14264338,0.0007165645],"about_ca_topic_score_codex":0.0048939995,"about_ca_topic_score_gemma":0.006326232,"teacher_disagreement_score":0.009666724,"about_ca_system_score_codex":0.0016216177,"about_ca_system_score_gemma":0.0013820713,"threshold_uncertainty_score":0.028156579},"labels":[],"label_agreement":null},{"id":"W2612628297","doi":"10.1109/aina.2017.147","title":"Mining Opinion Leaders in Big Social Network","year":2017,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Popularity; Opinion leadership; Public opinion; Computer science; Social network (sociolinguistics); Task (project management); Sentiment analysis; Data science; Social media; Social network analysis; Cluster analysis; Big data; World Wide Web; Data mining; Public relations; Artificial intelligence; Political science; Politics; Engineering","score_opus":0.0963872969228617,"score_gpt":0.329557024168559,"score_spread":0.2331697272456973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2612628297","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5358268,0.0008749467,0.45548293,0.00087543455,0.00009097047,0.00022023192,0.0012247773,0.0006918448,0.004711942],"genre_scores_gemma":[0.91082996,0.00039172004,0.085546434,0.00013014884,0.00008662963,0.00018218445,0.0014341564,0.000032662127,0.001366143],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992545,0.00022995379,0.00003995624,0.00016843443,0.0002147278,0.00009235345],"domain_scores_gemma":[0.9970289,0.0016300397,0.0005116521,0.00012660351,0.0005843484,0.000118494296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011931116,0.00064469705,0.00061815756,0.0025728252,0.00077228906,0.00076841266,0.00082821096,0.0007200138,0.00063371495],"category_scores_gemma":[0.005020109,0.00029555897,0.000581816,0.0012095984,0.00034874986,0.0014112587,0.00059776107,0.00046887837,0.0003127133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012560239,0.000594482,0.10968469,0.0009647712,0.0005246878,0.0041717063,0.0029631963,0.32890287,0.038719174,0.03849118,0.022522371,0.45120484],"study_design_scores_gemma":[0.000025040412,0.00006436128,0.0048004496,0.000021191192,0.000041370622,0.00018298613,0.00046337294,0.9756246,0.003628281,0.012764735,0.0023701368,0.000013422188],"about_ca_topic_score_codex":0.0014911026,"about_ca_topic_score_gemma":0.002301278,"teacher_disagreement_score":0.0025728252,"about_ca_system_score_codex":0.00048200728,"about_ca_system_score_gemma":0.00033554825,"threshold_uncertainty_score":0.0063098073},"labels":[],"label_agreement":null},{"id":"W2616739164","doi":"10.1007/978-3-319-59226-8_24","title":"Mining the Urdu Language-Based Web Content for Opinion Extraction","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lambton College","funders":"","keywords":"Computer science; Lexicon; Urdu; Natural language processing; Artificial intelligence; Sentiment analysis; Sentence; The Internet; Information retrieval; Linguistics; World Wide Web","score_opus":0.054301314640873,"score_gpt":0.309224164330501,"score_spread":0.254922849689628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2616739164","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49995917,0.008616838,0.3618102,0.0015735654,0.0015155065,0.0015818144,0.05741639,0.008480337,0.05904623],"genre_scores_gemma":[0.7633795,0.0038087664,0.165336,0.00037576284,0.0008662517,0.00080490834,0.050656743,0.0006334618,0.014138564],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994106,0.00009801651,0.0000627548,0.00010516579,0.00024042268,0.000083043175],"domain_scores_gemma":[0.9989034,0.0002499854,0.00010506201,0.000059356695,0.00063262024,0.00004956336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004174011,0.00080814713,0.0005333643,0.005520687,0.0005512751,0.0011999724,0.0004106592,0.00043101725,0.003722441],"category_scores_gemma":[0.0027437157,0.00018586281,0.00064160227,0.004214149,0.00019521087,0.001679414,0.00062936015,0.00054218987,0.0052203434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004937896,0.00026306705,0.01485442,0.0011947784,0.00014687478,0.0011153433,0.0008520918,0.0014857735,0.13800167,0.0033791722,0.055531807,0.78268117],"study_design_scores_gemma":[0.00010265507,0.0006815057,0.11487809,0.00093176775,0.0010246801,0.0034719585,0.0058318046,0.40103397,0.20759043,0.020965226,0.24321008,0.0002778543],"about_ca_topic_score_codex":0.0024110153,"about_ca_topic_score_gemma":0.0036476103,"teacher_disagreement_score":0.005520687,"about_ca_system_score_codex":0.00048671075,"about_ca_system_score_gemma":0.00059546577,"threshold_uncertainty_score":0.012452781},"labels":[],"label_agreement":null},{"id":"W2618843390","doi":"10.1145/3057270","title":"Current State of Text Sentiment Analysis from Opinion to Emotion Mining","year":2017,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":485,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Sentiment analysis; Computer science; Perspective (graphical); Emotion classification; Focus (optics); Data science; Emotion detection; Concept mining; Natural language processing; Artificial intelligence; Web mining; Emotion recognition; World Wide Web","score_opus":0.14543300333498774,"score_gpt":0.40614922833944267,"score_spread":0.2607162250044549,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2618843390","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055804523,0.88797826,0.060289208,0.010611299,0.0031404446,0.0003201775,0.00090071827,0.0008013382,0.030378034],"genre_scores_gemma":[0.02997253,0.90826684,0.042198848,0.003565154,0.0055619534,0.00046127386,0.0017852752,0.00025945765,0.0079286825],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99829465,0.0004969021,0.00018281244,0.00032320688,0.00061350566,0.000088995635],"domain_scores_gemma":[0.99151915,0.0050542275,0.0003980843,0.00029104447,0.002602202,0.00013519308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037201212,0.0010753757,0.0015403653,0.004443833,0.0006292531,0.0031709294,0.0017392518,0.0011483572,0.0058742147],"category_scores_gemma":[0.010283952,0.00046114365,0.0013674624,0.0062683187,0.000995111,0.0048345784,0.001153019,0.0017777822,0.0048988066],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054450546,0.000055895485,0.0009573728,0.006002518,0.000081730905,0.000040702234,0.00026981454,0.0003162901,0.000889482,0.008227047,0.043216143,0.9398886],"study_design_scores_gemma":[0.000048272344,0.00012381762,0.006502678,0.0072087958,0.00033662317,0.00058266416,0.0010488636,0.006513797,0.0027075957,0.036394257,0.93844366,0.000088987246],"about_ca_topic_score_codex":0.0017099784,"about_ca_topic_score_gemma":0.0013568935,"teacher_disagreement_score":0.0058742147,"about_ca_system_score_codex":0.001005538,"about_ca_system_score_gemma":0.0015962131,"threshold_uncertainty_score":0.019674122},"labels":[],"label_agreement":null},{"id":"W2620773589","doi":"","title":"Identifying the Conditions under Which Online Reviews Translate into Product Sales: A Sentiment Analysis Approach","year":2017,"lang":"en","type":"article","venue":"Journal of the Association for Information Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Sentiment analysis; Computer science; Product (mathematics); Data science; Artificial intelligence; Mathematics","score_opus":0.05243727107187047,"score_gpt":0.339396311698,"score_spread":0.28695904062612954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2620773589","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9703803,0.00057735125,0.017090209,0.00061268033,0.00009794834,0.00038369873,0.0024780482,0.00008982443,0.008289825],"genre_scores_gemma":[0.9887106,0.00028529507,0.008857162,0.00010730916,0.00012766538,0.0002331259,0.001100071,0.000029341058,0.00054953195],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99368435,0.0025210183,0.0010225297,0.00090087025,0.0015852731,0.000285922],"domain_scores_gemma":[0.9208162,0.049931776,0.0149890855,0.0018950228,0.011739346,0.0006285786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009035407,0.00041780833,0.000579552,0.003478548,0.00061653275,0.00291308,0.00030687908,0.00043464813,0.0013004179],"category_scores_gemma":[0.05103915,0.00022152302,0.0005989752,0.0036811228,0.00066811056,0.0021582763,0.0008701568,0.0007927914,0.0004988682],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015500677,0.00062479253,0.70831746,0.0016906008,0.0004996534,0.0007331699,0.012199617,0.0017774359,0.042166945,0.005915516,0.006859406,0.21766534],"study_design_scores_gemma":[0.000061419676,0.0006505621,0.931313,0.0002510208,0.00032598057,0.00044852434,0.010103936,0.02731459,0.015067275,0.005285687,0.009067061,0.00011088127],"about_ca_topic_score_codex":0.0025543703,"about_ca_topic_score_gemma":0.0026552873,"teacher_disagreement_score":0.009035407,"about_ca_system_score_codex":0.0011238599,"about_ca_system_score_gemma":0.0013587867,"threshold_uncertainty_score":0.047784388},"labels":[],"label_agreement":null},{"id":"W2621664531","doi":"10.1137/1.9781611974973.53","title":"User-guided Cross-domain Sentiment Classification","year":2017,"lang":"en","type":"book-chapter","venue":"Society for Industrial and Applied Mathematics eBooks","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cascades (Canada)","funders":"","keywords":"Sentiment analysis; Computer science; Domain adaptation; Artificial intelligence; Domain (mathematical analysis); Polarity (international relations); Transfer of learning; Labeled data; Graph; Context (archaeology); Factor (programming language); Machine learning; Natural language processing; Data mining; Classifier (UML); Theoretical computer science","score_opus":0.12776270931769543,"score_gpt":0.31405869816159077,"score_spread":0.18629598884389534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2621664531","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38309097,0.001944986,0.5957405,0.00055420364,0.00039388658,0.0005139322,0.0024048474,0.0052921656,0.010064473],"genre_scores_gemma":[0.8676147,0.00029111418,0.12012539,0.00024813766,0.00014735723,0.00019069771,0.005519632,0.00028049867,0.0055824514],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99845386,0.0004909563,0.00009841409,0.000544037,0.00024303146,0.00016960556],"domain_scores_gemma":[0.9975268,0.000874676,0.0002546392,0.00040201397,0.000809718,0.00013224661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029107125,0.00150491,0.0011867094,0.0034666853,0.00073271384,0.0010328267,0.0012796706,0.0012773466,0.0025118613],"category_scores_gemma":[0.0038975263,0.0002761787,0.00094879937,0.0022330107,0.0005566974,0.0018594753,0.0015519868,0.0010332917,0.0023171457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014624824,0.001367781,0.045462403,0.0004528717,0.00050783256,0.00044369616,0.00073117425,0.069835246,0.03539952,0.0045103254,0.026582088,0.81324464],"study_design_scores_gemma":[0.000036193003,0.00015226944,0.0076845945,0.000016568929,0.000055787925,0.00018595869,0.0002789487,0.9735353,0.009818648,0.00402092,0.004184927,0.000029958277],"about_ca_topic_score_codex":0.0021305315,"about_ca_topic_score_gemma":0.0031291188,"teacher_disagreement_score":0.0034666853,"about_ca_system_score_codex":0.00067437463,"about_ca_system_score_gemma":0.00050890056,"threshold_uncertainty_score":0.015393555},"labels":[],"label_agreement":null},{"id":"W2724114645","doi":"10.1007/978-3-319-62701-4_1","title":"Incorporating Positional Information into Deep Belief Networks for Sentiment Classification","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Artificial intelligence; Deep belief network; Information retrieval; Natural language processing; Deep learning","score_opus":0.020165617173073854,"score_gpt":0.26621136890084995,"score_spread":0.2460457517277761,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2724114645","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030775681,0.0015239476,0.96011126,0.00073972443,0.00031791796,0.000047833288,0.0004539614,0.0013771666,0.004652669],"genre_scores_gemma":[0.702434,0.001812954,0.2806804,0.00044491736,0.00041153195,0.00014312318,0.0019444259,0.00025174368,0.011876889],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997191,0.00007173959,0.000020717513,0.00006632234,0.000077094424,0.0000450028],"domain_scores_gemma":[0.99919385,0.00041240352,0.00006730518,0.00006549623,0.00022642415,0.000034555847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007239887,0.0010781982,0.00071566063,0.0008218913,0.00044712448,0.0011852934,0.0012242023,0.0011094422,0.004003697],"category_scores_gemma":[0.0028123427,0.0006347685,0.00068056275,0.0010231677,0.00031900464,0.0021971245,0.0010328862,0.0024900176,0.0019712013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023548845,0.00027073035,0.0018757175,0.00017730833,0.00014737263,0.00008218276,0.000112197224,0.298073,0.010345151,0.016293295,0.0111753205,0.6612123],"study_design_scores_gemma":[0.000003520629,0.000014155428,0.000099242374,0.000011752556,0.000013351063,0.0000060654447,0.0000055842497,0.99055266,0.000999528,0.007855182,0.0004350213,0.0000039393094],"about_ca_topic_score_codex":0.006155812,"about_ca_topic_score_gemma":0.0112914005,"teacher_disagreement_score":0.006155812,"about_ca_system_score_codex":0.0008426937,"about_ca_system_score_gemma":0.0006408835,"threshold_uncertainty_score":0.0133937},"labels":[],"label_agreement":null},{"id":"W2740514774","doi":"10.1145/3110394.3110396","title":"\"Interactive text analytics for user-generated content\" by Raheleh Makki with Prateek Jain as coordinator","year":2017,"lang":"en","type":"article","venue":"ACM SIGWEB Newsletter","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Social media; Analytics; Semantics (computer science); Content (measure theory); Volume (thermodynamics); World Wide Web; Social media analytics; Data science; Information retrieval","score_opus":0.04481424045470375,"score_gpt":0.29537613714107824,"score_spread":0.2505618966863745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2740514774","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0771575,0.015528917,0.7106742,0.10208897,0.011384789,0.0019517238,0.0049858983,0.03503678,0.04119129],"genre_scores_gemma":[0.25595897,0.017000072,0.4684379,0.0096955765,0.013172557,0.0017767301,0.014282124,0.0048616366,0.21481454],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972288,0.0010149038,0.0001291789,0.0004309098,0.0010544799,0.00014174098],"domain_scores_gemma":[0.9929843,0.0026576587,0.0001643234,0.00026043877,0.003178757,0.0007544948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004678289,0.0006806348,0.00064141455,0.001886006,0.0007234011,0.0029986026,0.0008656985,0.000679395,0.007752041],"category_scores_gemma":[0.0073666135,0.00028093698,0.00032296288,0.0016433195,0.00045654888,0.0033888815,0.0016092929,0.001224599,0.0063006976],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051471376,0.0004588738,0.0044684834,0.00029530562,0.000049212485,0.00034994393,0.0016597537,0.0021685164,0.011114214,0.0076538324,0.50141984,0.46984735],"study_design_scores_gemma":[0.00015795107,0.0007454548,0.0066105863,0.0003048692,0.000092023154,0.0011760702,0.0034484507,0.1419067,0.03930343,0.013443452,0.79266775,0.00014323204],"about_ca_topic_score_codex":0.0014014095,"about_ca_topic_score_gemma":0.0015164713,"teacher_disagreement_score":0.007752041,"about_ca_system_score_codex":0.00043187084,"about_ca_system_score_gemma":0.0011146856,"threshold_uncertainty_score":0.025933146},"labels":[],"label_agreement":null},{"id":"W2740540254","doi":"10.18653/v1/s17-1007","title":"Emotion Intensities in Tweets","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Sadness; Anger; Consistency (knowledge bases); Computer science; Benchmark (surveying); Task (project management); Annotation; Emotion classification; Natural language processing; Word (group theory); Emotion detection; Artificial intelligence; Scaling; Emotion recognition; Cognitive psychology; Psychology; Social psychology; Linguistics; Mathematics","score_opus":0.05343004542447813,"score_gpt":0.30766094854863646,"score_spread":0.2542309031241583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2740540254","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.911326,0.0010960483,0.032581992,0.00092143315,0.00041369695,0.00030402074,0.039352167,0.0014202937,0.01258433],"genre_scores_gemma":[0.9428962,0.00044449785,0.024298554,0.00016865942,0.00040901365,0.00033954764,0.027867245,0.00020440221,0.0033719745],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985147,0.00029906526,0.00014134537,0.0003861228,0.00053372805,0.00012511034],"domain_scores_gemma":[0.9957033,0.0020356996,0.0009790203,0.00028633137,0.0008364459,0.00015918241],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091388455,0.000773158,0.00044488537,0.0025235622,0.00047139,0.0014328293,0.00037399348,0.00056937145,0.0025178841],"category_scores_gemma":[0.008760129,0.00021566628,0.0005417557,0.00200068,0.00032112043,0.001540548,0.0008516711,0.00087774254,0.0016857007],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0030344345,0.00053766195,0.43084133,0.0020272243,0.0005861807,0.00076818763,0.0037586133,0.009379675,0.14803462,0.004450729,0.042158842,0.35442254],"study_design_scores_gemma":[0.00006842866,0.00048682842,0.80089927,0.00019897519,0.0002703344,0.001353092,0.002860933,0.06909406,0.060444735,0.008458052,0.055655643,0.00020975003],"about_ca_topic_score_codex":0.0012477825,"about_ca_topic_score_gemma":0.0014344328,"teacher_disagreement_score":0.0025235622,"about_ca_system_score_codex":0.0005131527,"about_ca_system_score_gemma":0.00016979208,"threshold_uncertainty_score":0.00842315},"labels":[],"label_agreement":null},{"id":"W2741386817","doi":"10.18653/v1/e17-2088","title":"A Dataset for Multi-Target Stance Detection","year":2017,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":136,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Focus (optics); Task (project management); Artificial intelligence; Machine learning; Joint (building); Position (finance); Product (mathematics); Artificial neural network; Position paper; Data mining; Engineering","score_opus":0.094901962189394,"score_gpt":0.36014213868087763,"score_spread":0.2652401764914836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2741386817","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019920982,0.0009306569,0.002715556,0.00066398684,0.0003206961,0.0003699577,0.9641514,0.0020268403,0.008899831],"genre_scores_gemma":[0.01632732,0.00022956255,0.0074098823,0.00020185173,0.0000646898,0.0003821936,0.97212744,0.00008509244,0.0031719157],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99892765,0.00015006379,0.00015405523,0.00026334552,0.0003770495,0.00012785752],"domain_scores_gemma":[0.9978543,0.00040691462,0.00031394965,0.00041093296,0.00074629945,0.0002676035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007317519,0.0017500276,0.00080505083,0.0027997433,0.0012128494,0.0009891589,0.0018828829,0.0026648564,0.01011557],"category_scores_gemma":[0.003384031,0.00029249393,0.0009582137,0.003210428,0.00036775993,0.0010350138,0.0011298378,0.001717732,0.0147222355],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046816427,0.0004926584,0.007746009,0.0009487395,0.000099201294,0.00039139576,0.00015277544,0.0014293401,0.00451234,0.001763333,0.94062114,0.041374914],"study_design_scores_gemma":[0.0005400124,0.00029916235,0.040311404,0.00029197455,0.00010101222,0.0011292465,0.00071707397,0.014791507,0.008370467,0.0035398449,0.9297762,0.00013209815],"about_ca_topic_score_codex":0.01148147,"about_ca_topic_score_gemma":0.038918402,"teacher_disagreement_score":0.01148147,"about_ca_system_score_codex":0.0011679862,"about_ca_system_score_gemma":0.0014824346,"threshold_uncertainty_score":0.03383994},"labels":[],"label_agreement":null},{"id":"W2741447225","doi":"10.18653/v1/p17-1067","title":"EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks","year":2017,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":383,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Task (project management); Emotion detection; Emotion recognition; Deep neural networks; Artificial intelligence; Ranging; Deep learning; Emotion classification; Artificial neural network; Task analysis; Machine learning; Natural language processing","score_opus":0.02179387651304163,"score_gpt":0.25572129582229813,"score_spread":0.2339274193092565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2741447225","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23494889,0.0026216838,0.71564704,0.0009944519,0.00084863324,0.00028223734,0.005961481,0.027688097,0.011007532],"genre_scores_gemma":[0.751632,0.0007114587,0.21467336,0.0006983256,0.00019044806,0.00036063846,0.014730959,0.00069440936,0.016308399],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974614,0.000053832355,0.000012349436,0.00009185837,0.000046746725,0.000049051294],"domain_scores_gemma":[0.9997216,0.00009390322,0.000033471246,0.000052653486,0.000074075084,0.000024187215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005769435,0.0013417188,0.0005258371,0.0005067598,0.00025322687,0.00061284046,0.0010380266,0.00066938874,0.002669726],"category_scores_gemma":[0.0013974647,0.00033980713,0.00056868594,0.00032816353,0.00020759887,0.0011286363,0.00077933667,0.0011324994,0.0017233915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008860066,0.0009573024,0.012287799,0.00035711512,0.0004998458,0.00036783874,0.000233997,0.13545305,0.08620799,0.007470899,0.08199541,0.67328286],"study_design_scores_gemma":[0.000034886474,0.00013187657,0.0022906817,0.000020259447,0.000044082513,0.00004808584,0.000026987605,0.97642267,0.011830673,0.0040599443,0.005067677,0.000022195165],"about_ca_topic_score_codex":0.0043789884,"about_ca_topic_score_gemma":0.009376587,"teacher_disagreement_score":0.0043789884,"about_ca_system_score_codex":0.00047355902,"about_ca_system_score_gemma":0.00044016883,"threshold_uncertainty_score":0.0089311},"labels":[],"label_agreement":null},{"id":"W2741691725","doi":"10.18653/v1/p17-2074","title":"Best-Worst Scaling More Reliable than Rating Scales: A Case Study on Sentiment Intensity Annotation","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Annotation; Consistency (knowledge bases); Rating scale; Computer science; Set (abstract data type); Scaling; Scale (ratio); Quality (philosophy); Data mining; Data set; Information retrieval; Artificial intelligence; Natural language processing; Machine learning; Statistics; Mathematics","score_opus":0.0678353615902395,"score_gpt":0.3486248772234626,"score_spread":0.2807895156332231,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2741691725","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5030389,0.0045033637,0.4420683,0.0054947603,0.001347344,0.0010011083,0.0030497594,0.008143158,0.03135328],"genre_scores_gemma":[0.6842792,0.00057300954,0.30469602,0.0006559998,0.00037414266,0.00050078327,0.002328824,0.001774907,0.0048171],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9673405,0.020723164,0.001825159,0.0035452982,0.005954995,0.00061079685],"domain_scores_gemma":[0.8967538,0.07572263,0.0040052193,0.012368268,0.009920527,0.0012295564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022338955,0.0013150678,0.0014493784,0.0022537229,0.0023938527,0.0026976357,0.0016287605,0.002300571,0.0025159374],"category_scores_gemma":[0.08800716,0.00046703877,0.0010101936,0.0035443536,0.002012017,0.0032433574,0.0023252082,0.0024091238,0.0021748068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0039276676,0.0011388637,0.041460164,0.004563628,0.00061668525,0.0043103024,0.020397907,0.026442861,0.10184781,0.02167257,0.066883214,0.7067383],"study_design_scores_gemma":[0.0007610424,0.0019913171,0.07430875,0.001021631,0.00055277586,0.0056665875,0.0147184525,0.52210075,0.12757298,0.06837029,0.1823769,0.0005586281],"about_ca_topic_score_codex":0.0032763707,"about_ca_topic_score_gemma":0.004440242,"teacher_disagreement_score":0.022338955,"about_ca_system_score_codex":0.0012660271,"about_ca_system_score_gemma":0.00082189287,"threshold_uncertainty_score":0.118141115},"labels":[],"label_agreement":null},{"id":"W2744881172","doi":"10.1016/j.ipm.2017.07.004","title":"Modeling Arabic subjectivity and sentiment in lexical space","year":2017,"lang":"en","type":"article","venue":"Information Processing & Management","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Subjectivity; Arabic; Natural language processing; Computer science; Space (punctuation); Artificial intelligence; Linguistics; Philosophy; Epistemology","score_opus":0.019284365507209814,"score_gpt":0.2747371884251029,"score_spread":0.2554528229178931,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2744881172","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73503673,0.00077991944,0.2547223,0.0006343918,0.00012239428,0.00005722495,0.0015929639,0.00091935194,0.0061346963],"genre_scores_gemma":[0.97735924,0.00016190793,0.019309867,0.000023393348,0.0000335784,0.000031615964,0.0008496793,0.000037105667,0.0021936917],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972695,0.000106847976,0.000015822921,0.0000687032,0.000045734258,0.00003587738],"domain_scores_gemma":[0.99937004,0.00038004908,0.0000535068,0.0000312576,0.00012635604,0.000038740556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004633609,0.0005236097,0.00035771754,0.0012032395,0.00048050922,0.0016303159,0.00037378402,0.00043016716,0.0023654138],"category_scores_gemma":[0.0019089316,0.00018618545,0.00072570425,0.0010845507,0.0002807458,0.0017316929,0.00068363495,0.0006638523,0.0007701915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019039167,0.0007363902,0.076023415,0.00032257926,0.00042055518,0.0010351721,0.0015402558,0.46468347,0.029525783,0.047829133,0.010103186,0.3658761],"study_design_scores_gemma":[0.0000066834455,0.00004050521,0.0029713893,0.000007396792,0.000022260567,0.000032584583,0.00015029257,0.9885217,0.00077449623,0.0067311483,0.0007339576,0.0000076056317],"about_ca_topic_score_codex":0.01246541,"about_ca_topic_score_gemma":0.012257163,"teacher_disagreement_score":0.01246541,"about_ca_system_score_codex":0.000658234,"about_ca_system_score_gemma":0.0005056081,"threshold_uncertainty_score":0.024785697},"labels":[],"label_agreement":null},{"id":"W2747541555","doi":"10.18653/v1/w17-5205","title":"WASSA-2017 Shared Task on Emotion Intensity","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Sadness; Task (project management); Computer science; Anger; Emotion classification; Artificial intelligence; Natural language processing; Psychology; Social psychology","score_opus":0.07464270248836234,"score_gpt":0.31192900408929586,"score_spread":0.2372863016009335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2747541555","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24485008,0.007808517,0.14520982,0.0068499967,0.018717887,0.005187457,0.4138905,0.100273125,0.057212625],"genre_scores_gemma":[0.2406163,0.0007679966,0.09580944,0.0016284162,0.002543863,0.0046120817,0.6223388,0.005516881,0.026166216],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9883663,0.003180701,0.001260293,0.0025459395,0.0031476284,0.0014990062],"domain_scores_gemma":[0.97912264,0.005161183,0.00075369375,0.006203989,0.006008713,0.0027498049],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0074664685,0.005910066,0.005298132,0.0041255434,0.0033267,0.004713076,0.0038965233,0.0055740597,0.015542679],"category_scores_gemma":[0.026525712,0.00088041706,0.003371621,0.002600917,0.001337945,0.005280292,0.012409816,0.00520817,0.024042618],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0063012694,0.0023462088,0.0082339775,0.003024139,0.0013120498,0.0010953113,0.000980329,0.005453585,0.045956574,0.0022655232,0.74400777,0.17902327],"study_design_scores_gemma":[0.003136545,0.0035456736,0.041343965,0.00065856247,0.0017012019,0.0022554249,0.003352403,0.19549441,0.116783515,0.027006393,0.60357565,0.0011462872],"about_ca_topic_score_codex":0.01255356,"about_ca_topic_score_gemma":0.015150403,"teacher_disagreement_score":0.015542679,"about_ca_system_score_codex":0.0017263406,"about_ca_system_score_gemma":0.0047528893,"threshold_uncertainty_score":0.051995456},"labels":[],"label_agreement":null},{"id":"W2751358090","doi":"10.18653/v1/s17-2149","title":"UW-FinSent at SemEval-2017 Task 5: Sentiment Analysis on Financial News Headlines using Training Dataset Augmentation","year":2017,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bigram; Computer science; SemEval; Task (project management); Sentiment analysis; Vectorization (mathematics); Paragraph; Artificial intelligence; Natural language processing; Machine learning; Baseline (sea); Regression; Training set; Simple (philosophy); World Wide Web; Statistics; Trigram","score_opus":0.13076153761679646,"score_gpt":0.37252394340278416,"score_spread":0.2417624057859877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2751358090","genre_codex":"software","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29690865,0.0037165247,0.1588461,0.00694021,0.0072358046,0.0047743698,0.1464341,0.31974137,0.05540288],"genre_scores_gemma":[0.27453488,0.0008238232,0.31341478,0.0016822221,0.00088268693,0.00293901,0.35618168,0.0056152414,0.043925676],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970951,0.00091570854,0.0002411172,0.00082067924,0.0006119719,0.00031528212],"domain_scores_gemma":[0.99364704,0.001580164,0.00028599362,0.0016334144,0.0024507642,0.00040268843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005271656,0.0023131985,0.0014481823,0.002149429,0.0014171252,0.0023147992,0.0016938838,0.001995778,0.010329332],"category_scores_gemma":[0.013257591,0.0006783618,0.0011488425,0.0015956949,0.0006201205,0.0040636794,0.0027468945,0.0028461057,0.016773604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011730343,0.0010345438,0.0054328046,0.0008086217,0.00020712522,0.0005915157,0.00089819485,0.0031377298,0.03333305,0.0014007466,0.5578932,0.3940894],"study_design_scores_gemma":[0.0009982258,0.0017788172,0.025800614,0.00048860756,0.00029684126,0.0010776115,0.0021545212,0.32945412,0.16068754,0.006156516,0.47077346,0.00033316595],"about_ca_topic_score_codex":0.017692838,"about_ca_topic_score_gemma":0.027205281,"teacher_disagreement_score":0.017692838,"about_ca_system_score_codex":0.0016217908,"about_ca_system_score_gemma":0.002202103,"threshold_uncertainty_score":0.035179675},"labels":[],"label_agreement":null},{"id":"W2751820697","doi":"10.1145/3091995","title":"Modeling and Mining Domain Shared Knowledge for Sentiment Analysis","year":2017,"lang":"en","type":"article","venue":"ACM Transactions on Information Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Fundamental Research Funds for the Central Universities","keywords":"Computer science; Sentiment analysis; Domain (mathematical analysis); Sentence; Artificial intelligence; Data mining; Natural language processing; Mathematics","score_opus":0.040456273623900925,"score_gpt":0.2987172456892563,"score_spread":0.25826097206535537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2751820697","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03186138,0.0009805394,0.96435124,0.00031924734,0.000052383475,0.00010054189,0.00042510347,0.0005068876,0.001402705],"genre_scores_gemma":[0.6041291,0.0014095258,0.38635027,0.0003443513,0.00032231337,0.0004324863,0.0041498276,0.00011529217,0.0027467518],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838996,0.00048730028,0.00010675547,0.00057940296,0.00032233918,0.00011430777],"domain_scores_gemma":[0.99775285,0.0010609577,0.00038149318,0.00031254243,0.00040965714,0.00008254277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018528644,0.0013820357,0.0010709118,0.0031273328,0.0006505458,0.0011329608,0.0012180512,0.0008375707,0.00090603856],"category_scores_gemma":[0.004774803,0.0005322219,0.0018556744,0.0023392076,0.0007570289,0.0022102809,0.001395002,0.0013635352,0.0006677178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031512105,0.00064767693,0.015547226,0.000734073,0.0006343056,0.00087996357,0.0011751639,0.24196595,0.029780377,0.021202393,0.014882837,0.67223495],"study_design_scores_gemma":[0.000014130193,0.00004829515,0.0016018916,0.000027096447,0.000053432937,0.00012747834,0.00013710331,0.9696083,0.0026114564,0.022318715,0.003429038,0.000023010869],"about_ca_topic_score_codex":0.004194541,"about_ca_topic_score_gemma":0.0066130054,"teacher_disagreement_score":0.004194541,"about_ca_system_score_codex":0.00088604377,"about_ca_system_score_gemma":0.0013492865,"threshold_uncertainty_score":0.009799004},"labels":[],"label_agreement":null},{"id":"W2751969445","doi":"","title":"Social emotional data analysis. The map of Europe","year":2017,"lang":"en","type":"article","venue":"STATISTICS AND DATA SCIENCE: NEW CHALLENGES, NEW GENERATIONS","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Meaning (existential); European union; Period (music); Content analysis; Psychology; Order (exchange); Linguistics; Natural language processing; Social psychology; Artificial intelligence; Computer science; Sociology; Social science; Art","score_opus":0.25640709151813884,"score_gpt":0.4060754723751443,"score_spread":0.14966838085700546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2751969445","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23044635,0.016818993,0.21372078,0.014548147,0.0021533147,0.00058319664,0.16872689,0.008680008,0.3443223],"genre_scores_gemma":[0.7973981,0.0065276036,0.1149214,0.00086057157,0.0006529512,0.0006105318,0.045297008,0.0012219377,0.032509968],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99953234,0.00013834369,0.000034607798,0.00011648918,0.00014382176,0.00003446528],"domain_scores_gemma":[0.9989322,0.00036155072,0.000203704,0.00016416507,0.00024562093,0.00009263384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055884384,0.0004060169,0.00020878452,0.0062979064,0.00073174597,0.0034455196,0.00025446288,0.0004082863,0.008487815],"category_scores_gemma":[0.004383119,0.00014053464,0.000348019,0.010263297,0.0005468258,0.0019247478,0.0013917716,0.0004983378,0.002104889],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026939556,0.00008008724,0.046494763,0.0010969014,0.00022575894,0.0007950645,0.0052660573,0.007347583,0.00303044,0.17479831,0.13542952,0.6251661],"study_design_scores_gemma":[0.00002185122,0.000045314966,0.07270425,0.00043738738,0.00006049078,0.0005106749,0.007057133,0.0118800495,0.0020950541,0.10598958,0.7991474,0.000050820825],"about_ca_topic_score_codex":0.0060168137,"about_ca_topic_score_gemma":0.0056575476,"teacher_disagreement_score":0.008487815,"about_ca_system_score_codex":0.0006601844,"about_ca_system_score_gemma":0.00076613546,"threshold_uncertainty_score":0.02839458},"labels":[],"label_agreement":null},{"id":"W2760392765","doi":"10.18653/v1/d17-1056","title":"Refining Word Embeddings for Sentiment Analysis","year":2017,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":190,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Science and Technology, Taiwan; Institute for Catastrophic Loss Reduction","keywords":"Computer science; Treebank; Word2vec; Sentiment analysis; Word (group theory); Natural language processing; Artificial intelligence; Context (archaeology); Parsing; Mathematics","score_opus":0.03981461137555894,"score_gpt":0.3329621635491202,"score_spread":0.2931475521735613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2760392765","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13834558,0.0011060438,0.84741086,0.0005014366,0.00045482075,0.00029768696,0.0020163716,0.0060877013,0.0037795522],"genre_scores_gemma":[0.59064996,0.00077063224,0.39346677,0.00023603249,0.00017066361,0.0003275265,0.008181259,0.000551394,0.0056457384],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994978,0.00012557197,0.00006272569,0.00016465639,0.00010308809,0.000046278197],"domain_scores_gemma":[0.99911875,0.00022270897,0.000109294524,0.00016684442,0.0003477436,0.00003457105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006764982,0.0013362654,0.0005435007,0.0014565359,0.0003098696,0.0007543931,0.0004688554,0.0005255713,0.003209107],"category_scores_gemma":[0.0038937165,0.0002850178,0.0008051597,0.001250166,0.00030202084,0.0031536645,0.0009376532,0.0011630069,0.0027316227],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000356187,0.00024956494,0.0074430513,0.0002742022,0.00012714358,0.00012528847,0.00042689318,0.015821734,0.04616515,0.008738087,0.019526672,0.900746],"study_design_scores_gemma":[0.00006387237,0.00031637607,0.005715633,0.00008420065,0.00013462234,0.00027351268,0.0005649499,0.9067731,0.028717095,0.03460322,0.022683792,0.00006965417],"about_ca_topic_score_codex":0.0014521955,"about_ca_topic_score_gemma":0.0025505775,"teacher_disagreement_score":0.003209107,"about_ca_system_score_codex":0.0003805072,"about_ca_system_score_gemma":0.00048795962,"threshold_uncertainty_score":0.010735571},"labels":[],"label_agreement":null},{"id":"W2765183831","doi":"10.1177/0047287517729757","title":"Automated Sentiment Analysis in Tourism: Comparison of Approaches","year":2017,"lang":"en","type":"article","venue":"Journal of Travel Research","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":196,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mount Saint Vincent University","funders":"","keywords":"Sentiment analysis; Tourism; Computer science; Artificial intelligence; Machine learning; Hospitality; Data science; Selection (genetic algorithm); Natural language processing; Software; Data mining","score_opus":0.2825954453852928,"score_gpt":0.4573567636676105,"score_spread":0.17476131828231772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765183831","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5432182,0.026935553,0.29941592,0.0059096096,0.0021768473,0.0025082233,0.0029014184,0.0040996005,0.11283459],"genre_scores_gemma":[0.74111855,0.011437734,0.2343694,0.000705339,0.0006850601,0.0007961158,0.0033699505,0.00041976885,0.007098092],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99482983,0.0021547202,0.00039425792,0.00041681627,0.0019788675,0.00022553932],"domain_scores_gemma":[0.99337584,0.0032704878,0.00043678714,0.0002941611,0.0023850522,0.00023772052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0064918427,0.00080884626,0.00083574024,0.0054840366,0.0007777702,0.002397097,0.0009777239,0.00080687826,0.0019061764],"category_scores_gemma":[0.009626948,0.0003403737,0.001384556,0.0026584943,0.0004796672,0.0021489463,0.0013755673,0.00077430706,0.0010193891],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011413852,0.00041708944,0.026891205,0.0022918908,0.00076805596,0.0001801603,0.002492904,0.0075962674,0.0076196664,0.005728784,0.015740965,0.9291317],"study_design_scores_gemma":[0.0005632327,0.0027245025,0.2853138,0.0023030026,0.0013340188,0.0016218548,0.016695594,0.47771552,0.026072145,0.03755054,0.14747313,0.0006326143],"about_ca_topic_score_codex":0.0028070402,"about_ca_topic_score_gemma":0.0038863383,"teacher_disagreement_score":0.0064918427,"about_ca_system_score_codex":0.0012761696,"about_ca_system_score_gemma":0.0012262465,"threshold_uncertainty_score":0.034332514},"labels":[],"label_agreement":null},{"id":"W2767720103","doi":"10.1007/s11266-017-9916-3","title":"Emotions and Pan-Asian Organizing in the U.S. Southwest: Analyzing Interview Discourses via Sentiment Analysis","year":2017,"lang":"en","type":"article","venue":"VOLUNTAS International Journal of Voluntary and Nonprofit Organizations","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sentiment analysis; Psychology; Sociology; Natural language processing; Computer science","score_opus":0.015621790692445651,"score_gpt":0.2878055178839526,"score_spread":0.27218372719150696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2767720103","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.998298,0.000035678975,0.00015345607,0.00022990242,0.000005139754,0.000019969097,0.00008161868,0.0000017228093,0.0011746014],"genre_scores_gemma":[0.99872094,0.00012242803,0.00034331062,0.00013617646,0.000010196524,0.000049904924,0.00008692764,0.0000036505528,0.000526393],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99931693,0.00039741088,0.00005411007,0.000053553988,0.0000841095,0.00009396633],"domain_scores_gemma":[0.9970009,0.001373807,0.0007816355,0.00007671803,0.0005304262,0.00023651376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025887818,0.00017833948,0.000176323,0.0010610173,0.0021628756,0.0016482861,0.00016954794,0.0002887267,0.0011103696],"category_scores_gemma":[0.004153018,0.0000987808,0.00012620051,0.001576038,0.001000332,0.0008470826,0.0011853403,0.0004841667,0.00014020136],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014120212,0.00011090188,0.2707218,0.00016920872,0.000019453335,0.00039847457,0.6947626,0.00011510874,0.006343488,0.0010040791,0.0020668213,0.024146967],"study_design_scores_gemma":[0.000002580331,0.000052788888,0.19169006,0.00007215379,0.000007869471,0.000050645493,0.80217963,0.0005751213,0.0006693489,0.0003032624,0.004384556,0.000011954461],"about_ca_topic_score_codex":0.012366144,"about_ca_topic_score_gemma":0.028535075,"teacher_disagreement_score":0.012366144,"about_ca_system_score_codex":0.0013092777,"about_ca_system_score_gemma":0.0011833294,"threshold_uncertainty_score":0.024588346},"labels":[],"label_agreement":null},{"id":"W2767912391","doi":"10.1109/intech.2017.8102442","title":"An empirical study on detecting fake reviews using machine learning techniques","year":2017,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Reputation; Computer science; Support vector machine; C4.5 algorithm; Naive Bayes classifier; Machine learning; Sentiment analysis; Decision tree; Artificial intelligence; Product (mathematics); Order (exchange)","score_opus":0.1546971287591058,"score_gpt":0.43738383714009493,"score_spread":0.28268670838098914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2767912391","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98889285,0.0015794858,0.0066565904,0.00017865475,0.000035656773,0.00012810409,0.00044992875,0.00005869856,0.0020201055],"genre_scores_gemma":[0.9885616,0.0004988082,0.009631607,0.00004284704,0.000041203635,0.00005931812,0.0007097368,0.0000123959735,0.00044253658],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9901736,0.004970721,0.00106162,0.0009117226,0.0026092583,0.00027316212],"domain_scores_gemma":[0.89506394,0.07776242,0.009324769,0.003890984,0.013392318,0.0005655807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0067815054,0.0005568895,0.000590243,0.0022443112,0.0005976386,0.0012114801,0.0006883922,0.0008026896,0.0006798319],"category_scores_gemma":[0.045427315,0.00023865385,0.00045293392,0.0020157623,0.00056077354,0.0017425988,0.00036494443,0.0006197229,0.0004331531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016151774,0.003443005,0.62806255,0.0028837798,0.00057040877,0.0010625282,0.0025473782,0.014014015,0.010365324,0.0017201345,0.00605311,0.32766262],"study_design_scores_gemma":[0.00012915638,0.0041640536,0.59036607,0.00065815914,0.00058422127,0.0034773194,0.0045920997,0.35673946,0.025809659,0.0010710964,0.012268398,0.00014031028],"about_ca_topic_score_codex":0.0026897,"about_ca_topic_score_gemma":0.002619564,"teacher_disagreement_score":0.0067815054,"about_ca_system_score_codex":0.0006518451,"about_ca_system_score_gemma":0.00037879802,"threshold_uncertainty_score":0.035864472},"labels":[],"label_agreement":null},{"id":"W2773532513","doi":"10.26615/978-954-452-049-6_015","title":"Inter-Annotator Agreement in Sentiment Analysis: Machine Learning Perspective","year":2017,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Dalhousie University","funders":"","keywords":"Annotation; Computer science; Perspective (graphical); Artificial intelligence; Sentiment analysis; Reliability (semiconductor); Agreement; Natural language processing; Machine learning; Linguistics","score_opus":0.022541868398487746,"score_gpt":0.3058372293485844,"score_spread":0.28329536095009666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2773532513","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16074564,0.0048356038,0.80614084,0.002824821,0.0009447214,0.0016260366,0.0012476414,0.00071365945,0.020921078],"genre_scores_gemma":[0.77709526,0.0007852949,0.21329895,0.0011384429,0.0006803366,0.0020591423,0.001630644,0.0006969118,0.002614939],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.6444675,0.26902834,0.021131989,0.028313188,0.034324355,0.0027345596],"domain_scores_gemma":[0.30521357,0.54799974,0.046205916,0.03428536,0.0645433,0.00175203],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.23034833,0.0017618765,0.0023985181,0.0062583527,0.0046542305,0.006826664,0.003177781,0.002645461,0.0019206465],"category_scores_gemma":[0.44534755,0.0012452435,0.0014296392,0.006345012,0.00556569,0.008171546,0.007946665,0.003908028,0.001081757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0036264875,0.001028106,0.25300595,0.0072977296,0.0073128347,0.0012656024,0.07610304,0.031212462,0.03929511,0.07133155,0.019846246,0.4886749],"study_design_scores_gemma":[0.00046667526,0.0012155512,0.20615138,0.0033473787,0.0022604603,0.0015294668,0.020262713,0.25319803,0.062477414,0.38843927,0.059604906,0.0010467299],"about_ca_topic_score_codex":0.0028968002,"about_ca_topic_score_gemma":0.0044826763,"teacher_disagreement_score":0.23034833,"about_ca_system_score_codex":0.0033035935,"about_ca_system_score_gemma":0.003085321,"threshold_uncertainty_score":0.94911754},"labels":[],"label_agreement":null},{"id":"W2773853552","doi":"10.2200/s00809ed2v01y201710hlt038","title":"Natural Language Processing for Social Media, Second Edition","year":2017,"lang":"en","type":"article","venue":"Synthesis lectures on human language technologies","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"University of Ottawa; University of Toronto; University of Southern California","keywords":"Social media; Linguistics; Computer science; Natural language processing; World Wide Web; Philosophy","score_opus":0.035857188233221336,"score_gpt":0.3292590273087216,"score_spread":0.29340183907550027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2773853552","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035651382,0.07997166,0.76655143,0.009151047,0.027496792,0.00041922048,0.026981087,0.036618173,0.04924539],"genre_scores_gemma":[0.024990194,0.049338154,0.5595976,0.002496795,0.012827799,0.0010761935,0.066999346,0.0098424,0.27283144],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99885094,0.00014576416,0.00017320177,0.00025234645,0.00051626214,0.00006139895],"domain_scores_gemma":[0.99579334,0.0017617213,0.00016181548,0.0006689836,0.0014757022,0.0001384682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001801881,0.0017303753,0.0014209565,0.0037309423,0.0006611103,0.0050102724,0.0016797371,0.001133074,0.055470873],"category_scores_gemma":[0.0072248047,0.001337678,0.0013893513,0.0025730245,0.0009570117,0.0058567477,0.001427835,0.002264292,0.0400041],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006291395,0.000062337494,0.00020541565,0.0010127347,0.000057029298,0.00010944744,0.000118077114,0.0013206372,0.0044025923,0.008462861,0.60358304,0.380603],"study_design_scores_gemma":[0.00003366005,0.00006054159,0.002236017,0.0004788428,0.000072491144,0.00068219804,0.00019123752,0.020067243,0.0062474683,0.040923122,0.928932,0.00007510934],"about_ca_topic_score_codex":0.0071428875,"about_ca_topic_score_gemma":0.010796467,"teacher_disagreement_score":0.055470873,"about_ca_system_score_codex":0.0011783958,"about_ca_system_score_gemma":0.0017989016,"threshold_uncertainty_score":0.18556857},"labels":[],"label_agreement":null},{"id":"W2773991511","doi":"10.26615/978-954-452-049-6_094","title":"Multi-entity sentiment analysis using entity-level feature extraction and word embeddings approach","year":2017,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Sentiment analysis; Natural language processing; Lexicon; Artificial intelligence; Word (group theory); Task (project management); String (physics); Feature (linguistics); Polarity (international relations); Relation (database); Binary classification; Linguistics; Data mining; Mathematics","score_opus":0.08222425526620293,"score_gpt":0.3446523552865046,"score_spread":0.2624281000203017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2773991511","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06313579,0.0004588165,0.93054104,0.00043330685,0.00014120978,0.00017176731,0.0013517531,0.0014497713,0.0023164896],"genre_scores_gemma":[0.5083052,0.0005098225,0.4827005,0.00011841383,0.000121655656,0.00019889658,0.0050185286,0.00008952737,0.002937528],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993794,0.00017461143,0.000095768446,0.00013345499,0.0001495355,0.000067191315],"domain_scores_gemma":[0.99911064,0.00026225284,0.00016018706,0.00010345443,0.00032915463,0.000034217184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008348218,0.00072419917,0.0006316253,0.0032275089,0.0003171527,0.0012920494,0.0005019014,0.0006032694,0.0015371382],"category_scores_gemma":[0.0020729366,0.00020248283,0.0010831238,0.002711877,0.00020145217,0.0026757151,0.00083342125,0.00070496404,0.0010402224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003270872,0.00047604935,0.021774722,0.00037949442,0.00046765537,0.00090129185,0.00048976456,0.020944515,0.065610446,0.019472793,0.01123393,0.85792214],"study_design_scores_gemma":[0.000022702694,0.00018485812,0.015824597,0.00005578631,0.00015856691,0.0004999077,0.000631438,0.9240964,0.021690661,0.022810431,0.013949898,0.00007474644],"about_ca_topic_score_codex":0.0013377061,"about_ca_topic_score_gemma":0.0017891821,"teacher_disagreement_score":0.0032275089,"about_ca_system_score_codex":0.00032718104,"about_ca_system_score_gemma":0.00039423705,"threshold_uncertainty_score":0.0051422715},"labels":[],"label_agreement":null},{"id":"W2783474735","doi":"10.5539/cis.v11n1p52","title":"Stock Market Classification Model Using Sentiment Analysis on Twitter Based on Hybrid Naive Bayes Classifiers","year":2018,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Naive Bayes classifier; Sentiment analysis; Artificial intelligence; Stock market; Machine learning; Stock market prediction; Stock (firearms); Bayes' theorem; Data mining; Support vector machine; Bayesian probability","score_opus":0.047147997772483526,"score_gpt":0.29793768438122425,"score_spread":0.25078968660874074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2783474735","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3640127,0.0015216162,0.6169863,0.0016729354,0.00057211606,0.00061692175,0.0011018048,0.0012587059,0.012256838],"genre_scores_gemma":[0.9117752,0.0006352853,0.08031672,0.00023980778,0.00027232646,0.0003272624,0.00095037045,0.000022770322,0.0054602125],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993894,0.00011251339,0.000083141815,0.00012581977,0.00020375896,0.000085332584],"domain_scores_gemma":[0.9993839,0.0001729239,0.00005113434,0.000018772616,0.00034850332,0.000024802835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009998513,0.00068947976,0.00097313855,0.0011898397,0.00056776736,0.0010247305,0.00083441933,0.0007498629,0.0017075171],"category_scores_gemma":[0.0016262587,0.0002605511,0.00088449166,0.0005599388,0.00021003383,0.0010300188,0.000304149,0.00054765464,0.0007449154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012694648,0.00080982887,0.04888144,0.00034336583,0.00038569816,0.00055227376,0.0004282628,0.21119098,0.01944604,0.00561536,0.009712827,0.70136446],"study_design_scores_gemma":[0.000013093246,0.0000584522,0.001936453,0.000013429947,0.000033760563,0.00003916268,0.000037664176,0.9951566,0.0012572837,0.00088454585,0.00055920886,0.000010257863],"about_ca_topic_score_codex":0.010844034,"about_ca_topic_score_gemma":0.0077871834,"teacher_disagreement_score":0.010844034,"about_ca_system_score_codex":0.00081809604,"about_ca_system_score_gemma":0.00074911834,"threshold_uncertainty_score":0.021561801},"labels":[],"label_agreement":null},{"id":"W2783550300","doi":"10.4018/ijepr.2018040102","title":"From Citizens to Decision-Makers","year":2018,"lang":"en","type":"article","venue":"International Journal of E-Planning Research","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Social media; Order (exchange); Democracy; Computer science; Process (computing); Digital era; Data science; Public relations; Internet privacy; Political science; World Wide Web; Business; The Internet","score_opus":0.0999224005149314,"score_gpt":0.48249226722349087,"score_spread":0.38256986670855947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2783550300","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30967855,0.0070954445,0.12149503,0.21551818,0.003215629,0.0006303607,0.0024631978,0.000837977,0.33906564],"genre_scores_gemma":[0.92804456,0.0024493972,0.023773149,0.006874282,0.00051599514,0.00022629219,0.00082010106,0.0001550049,0.03714124],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9949586,0.0030148497,0.00017950853,0.000529588,0.001001712,0.0003157487],"domain_scores_gemma":[0.99195874,0.0049113845,0.0005754431,0.0004335983,0.0014956209,0.00062523666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041607814,0.00038504382,0.00028238923,0.0012182362,0.0028584101,0.007228393,0.00069766433,0.0016362228,0.010455644],"category_scores_gemma":[0.017672148,0.00030633304,0.00024439004,0.0013853526,0.0042580166,0.0070033115,0.0039337757,0.0024763166,0.002641021],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022990938,0.00018051457,0.0133274,0.00080404483,0.000052735377,0.0016861734,0.12806818,0.001395162,0.0041175615,0.48694876,0.1089514,0.25423798],"study_design_scores_gemma":[0.00002437005,0.00005012422,0.0037936177,0.00057006977,0.000023385943,0.00019793061,0.1021165,0.0031123003,0.0019061549,0.27264646,0.6155199,0.000039222585],"about_ca_topic_score_codex":0.0022631579,"about_ca_topic_score_gemma":0.0023226554,"teacher_disagreement_score":0.010455644,"about_ca_system_score_codex":0.0022595762,"about_ca_system_score_gemma":0.003833864,"threshold_uncertainty_score":0.034977615},"labels":[],"label_agreement":null},{"id":"W2789753827","doi":"10.4018/ijeis.2018040105","title":"Sentiment Recognition in Customer Reviews Using Deep Learning","year":2018,"lang":"en","type":"article","venue":"International Journal of Enterprise Information Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Deep learning; Artificial intelligence; Sentiment analysis; Computer science; Machine learning; Artificial neural network; Support vector machine; Convolutional neural network; Naive Bayes classifier; Natural language processing","score_opus":0.03266996214159804,"score_gpt":0.30870979366001144,"score_spread":0.2760398315184134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789753827","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54797685,0.002604059,0.4284682,0.0013636824,0.00050113426,0.0002470449,0.002208297,0.0032127786,0.013417944],"genre_scores_gemma":[0.9153611,0.00090800156,0.075692624,0.00025153242,0.00013878995,0.00007212919,0.0019093624,0.00005327397,0.0056132628],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997291,0.00006029099,0.000020341158,0.000048119997,0.00009322551,0.000048922124],"domain_scores_gemma":[0.9994116,0.00012351446,0.0000914767,0.000026175816,0.0003249421,0.000022387117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004274179,0.00051645,0.00042631757,0.00070312695,0.00014509907,0.0005901018,0.0002804876,0.00035737807,0.0012838335],"category_scores_gemma":[0.0012816845,0.00018134892,0.00042662653,0.0005897976,0.0001163124,0.00056703703,0.0003032309,0.00060460105,0.0010189795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007403334,0.00040532835,0.022290474,0.00046639127,0.00024285677,0.00042454337,0.00033982954,0.042451076,0.097548135,0.00225485,0.020807745,0.81202835],"study_design_scores_gemma":[0.000012982026,0.000113710375,0.009717022,0.000031001746,0.0000415893,0.00008734855,0.0001267717,0.96281284,0.022107447,0.0017185252,0.003210787,0.000019914101],"about_ca_topic_score_codex":0.0026349528,"about_ca_topic_score_gemma":0.00444222,"teacher_disagreement_score":0.0026349528,"about_ca_system_score_codex":0.00039162528,"about_ca_system_score_gemma":0.00026319415,"threshold_uncertainty_score":0.0052391887},"labels":[],"label_agreement":null},{"id":"W2792962410","doi":"10.1109/csicsse.2017.8320114","title":"Translation is not enough: Comparing Lexicon-based methods for sentiment analysis in Persian","year":2017,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Council Canada","keywords":"Lexicon; Sentiment analysis; Computer science; Persian; Natural language processing; Artificial intelligence; Machine translation; Linguistics","score_opus":0.14444073071171698,"score_gpt":0.4261051158294514,"score_spread":0.2816643851177344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792962410","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8945944,0.008194037,0.05569192,0.0012180499,0.00072439504,0.00075226993,0.0060691596,0.0056169713,0.02713887],"genre_scores_gemma":[0.8719842,0.0021334933,0.09689608,0.00050750526,0.00022676266,0.00037246203,0.023278683,0.00042074986,0.0041800924],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99769443,0.0010632961,0.000305651,0.00030693188,0.0005039223,0.00012576701],"domain_scores_gemma":[0.99652594,0.0016008283,0.00023960372,0.00033459766,0.0012011051,0.000097878736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034231418,0.0011420855,0.00064592436,0.0041508772,0.00070172054,0.001890107,0.0007851671,0.00058697176,0.0017829923],"category_scores_gemma":[0.0083066095,0.000201222,0.0007074913,0.002983433,0.0006179708,0.002025342,0.0011075379,0.00053707464,0.001400161],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0042169187,0.00081891177,0.027554918,0.002031854,0.00068227615,0.0008022305,0.0030882896,0.017004717,0.019910838,0.0054146717,0.041446764,0.87702763],"study_design_scores_gemma":[0.001260526,0.0026256987,0.14380671,0.00070575037,0.00096345384,0.0023104004,0.0155262565,0.6412821,0.062330235,0.018168036,0.11063775,0.00038318872],"about_ca_topic_score_codex":0.010608588,"about_ca_topic_score_gemma":0.010932295,"teacher_disagreement_score":0.010608588,"about_ca_system_score_codex":0.0012379887,"about_ca_system_score_gemma":0.0010959755,"threshold_uncertainty_score":0.021093667},"labels":[],"label_agreement":null},{"id":"W2796214464","doi":"10.1007/978-3-319-89656-4_33","title":"A Sentence-Level Sparse Gamma Topic Model for Sentiment Analysis","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Sentence; Artificial intelligence; Natural language processing; Sentiment analysis; Inference; Identification (biology); Product (mathematics); Contrast (vision); Task (project management); Topic model","score_opus":0.06075668038258512,"score_gpt":0.28478767400337285,"score_spread":0.22403099362078774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2796214464","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01570284,0.0012357354,0.97779113,0.000392859,0.00026412267,0.00012338883,0.0010568983,0.0022427465,0.0011903574],"genre_scores_gemma":[0.4148133,0.0028777134,0.549932,0.0006901623,0.0014106405,0.00095103204,0.011641581,0.00083214696,0.016851364],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993961,0.00019485842,0.00004682466,0.00015803821,0.00013022342,0.00007395216],"domain_scores_gemma":[0.9990392,0.0004725571,0.000051570314,0.00010052597,0.0002842826,0.000051892468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015406008,0.0010559679,0.0014041682,0.0013476523,0.0005559357,0.0011004457,0.001656414,0.0011787096,0.004328858],"category_scores_gemma":[0.003220419,0.0005747837,0.0014312746,0.0020541784,0.00031214734,0.0018341831,0.001289652,0.0021350028,0.0038088034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00072097685,0.00040347152,0.0020523262,0.000305575,0.0003910821,0.00018208665,0.000306814,0.13262509,0.021986047,0.013277984,0.029220581,0.798528],"study_design_scores_gemma":[0.000017429513,0.000038314236,0.00037877273,0.000011158248,0.00005318954,0.000030203451,0.000016679516,0.9917074,0.0010005778,0.0048909597,0.001842947,0.000012357936],"about_ca_topic_score_codex":0.007515196,"about_ca_topic_score_gemma":0.010984036,"teacher_disagreement_score":0.007515196,"about_ca_system_score_codex":0.0006454364,"about_ca_system_score_gemma":0.0012042657,"threshold_uncertainty_score":0.014942884},"labels":[],"label_agreement":null},{"id":"W2798854499","doi":"10.1145/3209978.3210148","title":"Affective Representations for Sarcasm Detection","year":2018,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Sarcasm; Computer science; Natural language processing; Artificial intelligence; Sentiment analysis; Emotion detection; Word (group theory); Psychology; Linguistics; Irony; Emotion recognition; Philosophy","score_opus":0.02757253692911713,"score_gpt":0.32335980456414243,"score_spread":0.2957872676350253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2798854499","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5274651,0.0066734445,0.43309897,0.0016828973,0.001002921,0.00056547165,0.0053467294,0.003978376,0.020186028],"genre_scores_gemma":[0.93171245,0.0009330243,0.06022558,0.00020437794,0.0002856142,0.00027117025,0.0030005064,0.00008652685,0.0032806061],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933213,0.0002213405,0.00007395279,0.00014229375,0.00016118828,0.000069026704],"domain_scores_gemma":[0.9976802,0.0010415436,0.0005105158,0.00015787705,0.00052612234,0.00008379167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074139284,0.0008845402,0.000341861,0.001963379,0.0003052101,0.00089348096,0.0002849094,0.0006795498,0.0022867161],"category_scores_gemma":[0.006687452,0.00014852473,0.0003616847,0.0011746914,0.00025492767,0.0014886722,0.0006381171,0.00072004437,0.0014952545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009392239,0.00042604786,0.036595974,0.0008435982,0.00027719236,0.0003765486,0.0016861147,0.0076763737,0.059424035,0.004208396,0.019526346,0.86802],"study_design_scores_gemma":[0.0001293349,0.0017109647,0.2004093,0.00059947005,0.00053125556,0.0018569009,0.004437395,0.64147913,0.050162558,0.040329166,0.058045562,0.00030895826],"about_ca_topic_score_codex":0.0004739368,"about_ca_topic_score_gemma":0.0009926487,"teacher_disagreement_score":0.0022867161,"about_ca_system_score_codex":0.0002655018,"about_ca_system_score_gemma":0.00020889644,"threshold_uncertainty_score":0.0076497793},"labels":[],"label_agreement":null},{"id":"W2799696170","doi":"10.5539/cis.v11n2p76","title":"Corpus Analysis and Annotation for Helpful Sentences in Product Reviews","year":2018,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"King Abdulaziz University","keywords":"Computer science; Helpfulness; Annotation; Natural language processing; Product (mathematics); Quality (philosophy); Information retrieval; Artificial intelligence; Scheme (mathematics); Resource (disambiguation); Task (project management); Rank (graph theory)","score_opus":0.024982395979917,"score_gpt":0.2967413276958779,"score_spread":0.2717589317159609,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799696170","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37781835,0.00734981,0.5089813,0.002110464,0.0022410757,0.007822298,0.046449017,0.0065430626,0.040684648],"genre_scores_gemma":[0.24720217,0.0016320755,0.6613898,0.0003712162,0.00056855177,0.012183866,0.065142095,0.0011031427,0.010407027],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.98671526,0.006277068,0.0018129983,0.0019836626,0.0029190353,0.0002920575],"domain_scores_gemma":[0.93924487,0.02940109,0.0043696812,0.0036965269,0.022620648,0.00066721044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007953393,0.0008602848,0.00068207993,0.009224132,0.001905313,0.0014363936,0.0008882026,0.00075026107,0.0036768892],"category_scores_gemma":[0.04125257,0.0003803132,0.0006148917,0.0054197544,0.00075815007,0.001273027,0.002153868,0.0012597361,0.002017774],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010912704,0.0004032796,0.019959515,0.008399609,0.00018811456,0.0023386413,0.02193738,0.0034262913,0.15287526,0.011820925,0.124099635,0.65346014],"study_design_scores_gemma":[0.00024051595,0.00064731116,0.10643722,0.0022660955,0.0005405759,0.0029539873,0.012943168,0.064185664,0.11061602,0.011869205,0.68686956,0.00043067697],"about_ca_topic_score_codex":0.003006792,"about_ca_topic_score_gemma":0.0065771826,"teacher_disagreement_score":0.009224132,"about_ca_system_score_codex":0.0009593118,"about_ca_system_score_gemma":0.0024536883,"threshold_uncertainty_score":0.042062044},"labels":[],"label_agreement":null},{"id":"W2804405055","doi":"10.1007/978-3-319-92058-0_49","title":"Auto-detection of Safety Issues in Baby Products","year":2018,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Random forest; Naive Bayes classifier; Computer science; Classifier (UML); Support vector machine; Logistic regression; Dimensionality reduction; Machine learning; Artificial intelligence; Product (mathematics); Commission; Data mining; Business; Mathematics","score_opus":0.01921199339360233,"score_gpt":0.28257097660981384,"score_spread":0.2633589832162115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804405055","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.965331,0.0015547695,0.020345585,0.00044500985,0.00029369796,0.00010787916,0.0037702306,0.0006731346,0.0074786847],"genre_scores_gemma":[0.97825205,0.00044530453,0.013401904,0.00012585559,0.00020025294,0.000044503176,0.0036658463,0.000057109264,0.0038071864],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992237,0.000093130235,0.000051673138,0.0001582678,0.00038677393,0.00008638318],"domain_scores_gemma":[0.9958003,0.0014107081,0.00087000104,0.00020144357,0.0015614668,0.0001559991],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081516744,0.00050014444,0.0003768541,0.0017756645,0.0002426827,0.0005778306,0.00031915095,0.0006452694,0.002059994],"category_scores_gemma":[0.003688212,0.00013685222,0.00042284673,0.00081261015,0.00012029248,0.0007586599,0.0004199014,0.00049960957,0.001346136],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027516626,0.00089619396,0.31348678,0.0012212874,0.00032159244,0.0014995891,0.00068632,0.0036789845,0.13441403,0.0012871275,0.029562708,0.51019377],"study_design_scores_gemma":[0.000081187995,0.0017228222,0.6567248,0.00028666094,0.00066344236,0.0024020914,0.0016342635,0.22217666,0.080752105,0.0029722836,0.030476192,0.0001075239],"about_ca_topic_score_codex":0.0012315313,"about_ca_topic_score_gemma":0.0019349027,"teacher_disagreement_score":0.002059994,"about_ca_system_score_codex":0.00023564078,"about_ca_system_score_gemma":0.00030586115,"threshold_uncertainty_score":0.00689137},"labels":[],"label_agreement":null},{"id":"W2805192223","doi":"","title":"Target-focused Sentiment and Belief Extraction and Classification using CUBISM.","year":2016,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Extraction (chemistry); Computer science; Artificial intelligence; Pattern recognition (psychology); Natural language processing; Chemistry; Chromatography","score_opus":0.021265883267216,"score_gpt":0.2796288558048587,"score_spread":0.2583629725376427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2805192223","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13237116,0.00074859033,0.84182346,0.0011844578,0.00018657529,0.000536789,0.0032797174,0.0024500818,0.017419156],"genre_scores_gemma":[0.64266473,0.00025728904,0.34538534,0.00022918989,0.00008264183,0.0005903879,0.004461602,0.00013499822,0.0061937068],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933594,0.00023813793,0.000046927522,0.00014518619,0.0001446861,0.00008903089],"domain_scores_gemma":[0.99859005,0.00053441006,0.000120557044,0.00017313358,0.00049928547,0.000082528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000984467,0.0004382661,0.0005507174,0.0016796731,0.00086382806,0.0018332013,0.0007886314,0.00047401988,0.0030123133],"category_scores_gemma":[0.004970266,0.00026078025,0.00070755533,0.0015161234,0.00043805936,0.0015438764,0.0015571853,0.0008023316,0.0009891216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014021655,0.00041216283,0.020322027,0.00072721404,0.00036725003,0.00032387025,0.0021812604,0.020703675,0.02604541,0.074230075,0.03452417,0.8187607],"study_design_scores_gemma":[0.00006735956,0.00014736074,0.013264492,0.0001429883,0.00018021488,0.00023607361,0.0015941324,0.81845474,0.01572152,0.1187856,0.03133819,0.000067265704],"about_ca_topic_score_codex":0.00819831,"about_ca_topic_score_gemma":0.012376969,"teacher_disagreement_score":0.00819831,"about_ca_system_score_codex":0.0011317806,"about_ca_system_score_gemma":0.0011822247,"threshold_uncertainty_score":0.016301215},"labels":[],"label_agreement":null},{"id":"W2805744755","doi":"10.18653/v1/s18-1001","title":"SemEval-2018 Task 1: Affect in Tweets","year":2018,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":745,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"SemEval; Valence (chemistry); Computer science; Affect (linguistics); Task (project management); Natural language processing; Sentiment analysis; Emotion detection; Artificial intelligence; Emotion classification; Focus (optics); Emotion recognition; Linguistics","score_opus":0.02371668830788393,"score_gpt":0.2892998895742772,"score_spread":0.2655832012663933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2805744755","genre_codex":"dataset","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3836173,0.0031473031,0.055097,0.003134677,0.0033709134,0.0045441543,0.4549326,0.04828475,0.04387127],"genre_scores_gemma":[0.2869075,0.00052229624,0.08760968,0.00097214227,0.0009413367,0.007057306,0.58634347,0.0031768042,0.026469456],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99717975,0.0010374372,0.00021482355,0.00066130445,0.00061979645,0.00028693816],"domain_scores_gemma":[0.9945962,0.0024147993,0.0003562726,0.0010484156,0.001142792,0.0004414514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028783989,0.0027983056,0.0013520998,0.0016207387,0.001627718,0.002428146,0.0016753307,0.0025091388,0.015802676],"category_scores_gemma":[0.011032009,0.00046965852,0.0011338466,0.001313416,0.00061953184,0.0026182486,0.0033309022,0.0022192302,0.018598637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0031283484,0.0017887655,0.023729302,0.0025071674,0.00025778162,0.00066646095,0.0016725681,0.0069936994,0.019456465,0.002447283,0.74873906,0.1886131],"study_design_scores_gemma":[0.0013715907,0.0021745367,0.11742272,0.0006429255,0.00031743274,0.0021012342,0.003739432,0.15277797,0.08198225,0.012052589,0.6248644,0.0005530199],"about_ca_topic_score_codex":0.0040575834,"about_ca_topic_score_gemma":0.0068695564,"teacher_disagreement_score":0.015802676,"about_ca_system_score_codex":0.00095845666,"about_ca_system_score_gemma":0.0010763617,"threshold_uncertainty_score":0.052865207},"labels":[],"label_agreement":null},{"id":"W2806731175","doi":"10.18653/v1/s18-1052","title":"Mutux at SemEval-2018 Task 1: Exploring Impacts of Context Information On Emotion Detection","year":2018,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"SemEval; Task (project management); Computer science; Context (archaeology); Emotion detection; Natural language processing; Artificial intelligence; Emotion recognition; Engineering; History","score_opus":0.041984766843436695,"score_gpt":0.2560218584355574,"score_spread":0.21403709159212073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2806731175","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7075103,0.006932374,0.08247252,0.0023449545,0.002964402,0.0019093949,0.060473908,0.10333266,0.03205949],"genre_scores_gemma":[0.7202471,0.00066704035,0.148891,0.0009928233,0.00052508456,0.0016026848,0.10223101,0.0021810674,0.022662107],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982317,0.0006433715,0.00009954944,0.0005069469,0.0003534343,0.00016496805],"domain_scores_gemma":[0.99851805,0.0007026224,0.00009108688,0.00024393368,0.00032915146,0.00011525446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022353926,0.002323303,0.0011841288,0.0016976164,0.0009842804,0.0019561043,0.0014532461,0.001821398,0.007066615],"category_scores_gemma":[0.0051301643,0.00034170022,0.0008679947,0.00070387986,0.000352383,0.0024520212,0.001910915,0.0012814099,0.0046371357],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0063257995,0.0023259267,0.024838472,0.002201632,0.0007597723,0.0012527053,0.0010693163,0.011743047,0.06641559,0.0023558838,0.32097065,0.55974126],"study_design_scores_gemma":[0.0009487862,0.0033315455,0.06441918,0.00027082523,0.00049254595,0.0023424127,0.0019706385,0.5839517,0.15398873,0.006671755,0.18121885,0.00039298856],"about_ca_topic_score_codex":0.004182012,"about_ca_topic_score_gemma":0.0070076366,"teacher_disagreement_score":0.007066615,"about_ca_system_score_codex":0.0008777007,"about_ca_system_score_gemma":0.00067083945,"threshold_uncertainty_score":0.023640156},"labels":[],"label_agreement":null},{"id":"W2807049895","doi":"10.1007/978-3-319-89932-9_11","title":"Text-Based Analysis of Emotion by Considering Tweets","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in social networks","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Sentiment analysis; Natural language processing; Computer science; Psychology; Information retrieval; Artificial intelligence","score_opus":0.019135501476201047,"score_gpt":0.2611615066462348,"score_spread":0.24202600517003373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2807049895","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5174563,0.0028729371,0.44342574,0.0011608372,0.001669341,0.00054894446,0.012906736,0.0025551298,0.017404106],"genre_scores_gemma":[0.84131795,0.0013814892,0.1351623,0.00016435531,0.0008937535,0.00034859084,0.010638774,0.0002409746,0.009851895],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961984,0.000067093366,0.000034507964,0.000090053705,0.00013166897,0.00005679752],"domain_scores_gemma":[0.9993414,0.00027272975,0.000074106814,0.00003259514,0.00024286691,0.000036275498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042169128,0.0007778906,0.0005401547,0.0019014975,0.00031053557,0.0011589244,0.00030563408,0.0004152002,0.0026726348],"category_scores_gemma":[0.0014917452,0.00012325405,0.00067729305,0.001853841,0.0001358458,0.001000974,0.0004480469,0.0005747355,0.0023360504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015820926,0.00058387785,0.023250055,0.0007605147,0.00034986675,0.0005934053,0.00060262985,0.009438767,0.26757166,0.0025298987,0.020037659,0.6726995],"study_design_scores_gemma":[0.00006664303,0.0008380156,0.066234276,0.0001692373,0.0006410052,0.00088506134,0.0017821786,0.7623703,0.13124973,0.009062687,0.026560154,0.00014072705],"about_ca_topic_score_codex":0.00091944373,"about_ca_topic_score_gemma":0.001343126,"teacher_disagreement_score":0.0026726348,"about_ca_system_score_codex":0.00022672406,"about_ca_system_score_gemma":0.00022254299,"threshold_uncertainty_score":0.0089408755},"labels":[],"label_agreement":null},{"id":"W2807470759","doi":"10.63317/2ragacqgsct6","title":"An Attribution Relations Corpus for Political News","year":2018,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Attribution; Politics; Natural language processing; Political science; Linguistics; Artificial intelligence; Psychology; Social psychology; Law; Philosophy","score_opus":0.041711196712918716,"score_gpt":0.33278045195293443,"score_spread":0.2910692552400157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2807470759","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18799457,0.005055649,0.04086291,0.0038634923,0.002727984,0.0016402429,0.666205,0.011322225,0.080327936],"genre_scores_gemma":[0.17891958,0.0016705322,0.053180754,0.00036983375,0.0007483797,0.0012287251,0.74649906,0.0012678318,0.016115298],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99772555,0.0006839863,0.00032423495,0.0004516267,0.0006702696,0.00014443354],"domain_scores_gemma":[0.98750234,0.0075335493,0.0009700068,0.0014474693,0.0019338697,0.0006127331],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001774628,0.0008230041,0.00045563545,0.009174177,0.0022559885,0.0019131263,0.0009452281,0.0011872867,0.0149131715],"category_scores_gemma":[0.012901154,0.0005108358,0.00044127522,0.009367587,0.00071781396,0.001992468,0.001957314,0.0016096276,0.008489292],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00083942927,0.0010228589,0.014197128,0.003912781,0.00017808405,0.002419669,0.0041057025,0.003352807,0.027784217,0.022405902,0.6616428,0.25813857],"study_design_scores_gemma":[0.00023048739,0.00013123835,0.0529611,0.0004525528,0.00018868322,0.0012941724,0.0020500198,0.016461153,0.014419736,0.0067201825,0.90499276,0.00009789979],"about_ca_topic_score_codex":0.0061746743,"about_ca_topic_score_gemma":0.01282227,"teacher_disagreement_score":0.0149131715,"about_ca_system_score_codex":0.00093111774,"about_ca_system_score_gemma":0.0019850829,"threshold_uncertainty_score":0.049889505},"labels":[],"label_agreement":null},{"id":"W2807476308","doi":"10.18653/v1/s18-1027","title":"uOttawa at SemEval-2018 Task 1: Self-Attentive Hybrid GRU-Based Network","year":2018,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"TD Bank Group; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"SemEval; Computer science; Artificial intelligence; Encoder; Convolutional neural network; Task (project management); Representation (politics); Valence (chemistry); Feature (linguistics); Character (mathematics); Natural language processing; Machine learning","score_opus":0.012543692547026768,"score_gpt":0.2373444790603731,"score_spread":0.2248007865133463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2807476308","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6339622,0.003684172,0.27969816,0.0035712575,0.002782382,0.0011749273,0.009893348,0.026423272,0.03881024],"genre_scores_gemma":[0.8165736,0.00031971638,0.13569732,0.000916875,0.00026722503,0.00065983465,0.013144717,0.00071569136,0.031704955],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994992,0.00012769237,0.000015684629,0.00020253845,0.000061270104,0.00009356074],"domain_scores_gemma":[0.9994199,0.00016492361,0.00003411977,0.00013483889,0.00017191654,0.000074290125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010078206,0.0018709082,0.00094869133,0.0004079531,0.00073355366,0.001001574,0.0018421244,0.0022874898,0.005814566],"category_scores_gemma":[0.0021685606,0.0005018579,0.00078486896,0.00036211422,0.00045188706,0.0022125472,0.0014820279,0.0019920678,0.0026655889],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003129164,0.001440174,0.007483744,0.0007444554,0.00047919876,0.0010300699,0.0005368845,0.19303286,0.05626342,0.007688312,0.1411645,0.58700716],"study_design_scores_gemma":[0.00016042253,0.00051089964,0.0018292834,0.000039897306,0.000086273314,0.00014134911,0.000107364074,0.94659156,0.029055204,0.0059596957,0.015466215,0.00005195839],"about_ca_topic_score_codex":0.007879006,"about_ca_topic_score_gemma":0.013244472,"teacher_disagreement_score":0.007879006,"about_ca_system_score_codex":0.0009108663,"about_ca_system_score_gemma":0.0007842477,"threshold_uncertainty_score":0.019451618},"labels":[],"label_agreement":null},{"id":"W2807551980","doi":"10.1186/s13673-018-0135-8","title":"QER: a new feature selection method for sentiment analysis","year":2018,"lang":"en","type":"article","venue":"Human-centric Computing and Information Sciences","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Mustafa Kemal Üniversitesi; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu; University of Waterloo; Çukurova Üniversitesi","keywords":"Computer science; Feature selection; Weighting; Artificial intelligence; Naive Bayes classifier; Ranking (information retrieval); Pattern recognition (psychology); Support vector machine; Feature (linguistics); Information gain ratio; Data mining; Machine learning","score_opus":0.031063118666117627,"score_gpt":0.34828127166867096,"score_spread":0.31721815300255335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2807551980","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03595169,0.00056588073,0.95465,0.00019068002,0.00015344968,0.0004291301,0.0010940579,0.0061411113,0.0008240115],"genre_scores_gemma":[0.30110794,0.0003127081,0.68908376,0.00023590853,0.0002439048,0.0010496614,0.0044431943,0.0005327662,0.0029901925],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983423,0.00041605602,0.00018135278,0.00030201997,0.00064157636,0.00011668507],"domain_scores_gemma":[0.99837154,0.00061553705,0.0001383146,0.0000902621,0.0007436188,0.000040765026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022747146,0.001273457,0.0014798541,0.0033170008,0.00040856216,0.00067351747,0.0010605585,0.0005869111,0.0039249593],"category_scores_gemma":[0.004494877,0.000283075,0.0013863266,0.0023757368,0.00022300944,0.0010484548,0.0005289163,0.00067320524,0.0016817092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059854845,0.0002808247,0.0037115188,0.00031598523,0.00025083337,0.0002465107,0.000096940166,0.011750228,0.053992826,0.001111831,0.01589251,0.9117515],"study_design_scores_gemma":[0.0002873445,0.0004992777,0.014089358,0.000036917987,0.00019464442,0.00048734556,0.00009630481,0.9366875,0.034153614,0.0023095978,0.011050392,0.00010763658],"about_ca_topic_score_codex":0.001880081,"about_ca_topic_score_gemma":0.0017066963,"teacher_disagreement_score":0.0039249593,"about_ca_system_score_codex":0.0003624171,"about_ca_system_score_gemma":0.00053442945,"threshold_uncertainty_score":0.013130307},"labels":[],"label_agreement":null},{"id":"W2807860467","doi":"10.1007/978-3-319-93372-6_6","title":"Detecting Agreement and Disagreement in Political Debates","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Task (project management); Computer science; Agreement; Presidential system; Baseline (sea); Field (mathematics); Presidential campaign; Politics; Artificial intelligence; State (computer science); Training set; Natural language processing; Algorithm; Political science; Law; Linguistics; Mathematics","score_opus":0.021868784169364077,"score_gpt":0.2668814719523952,"score_spread":0.24501268778303112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2807860467","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9227676,0.0009218874,0.056058392,0.001113105,0.0002177891,0.00019985203,0.0027836822,0.00060121744,0.015336412],"genre_scores_gemma":[0.980893,0.00011747209,0.014058013,0.000082889215,0.00018667192,0.0000955402,0.0029888991,0.00005249945,0.0015250547],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99592483,0.0017586526,0.00027524962,0.0006971734,0.00092944346,0.0004147241],"domain_scores_gemma":[0.97388387,0.020697122,0.0016728446,0.0006786764,0.002395871,0.0006716991],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004616274,0.0005354487,0.00072354625,0.00360893,0.001257579,0.003015838,0.00086778007,0.0014162024,0.002951862],"category_scores_gemma":[0.02857567,0.0003124269,0.00053410075,0.0025250572,0.0006698495,0.004057905,0.0018158054,0.0020662458,0.0017581033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00318829,0.0009928377,0.3630566,0.0007295851,0.000492796,0.00056919776,0.007134769,0.009737353,0.021570928,0.022016477,0.028878992,0.5416321],"study_design_scores_gemma":[0.00020107659,0.00060868496,0.21911551,0.000297328,0.00044307637,0.0007432718,0.01280352,0.6001445,0.026954647,0.111695535,0.026839385,0.0001534905],"about_ca_topic_score_codex":0.00108878,"about_ca_topic_score_gemma":0.0014711196,"teacher_disagreement_score":0.004616274,"about_ca_system_score_codex":0.0006484647,"about_ca_system_score_gemma":0.000507049,"threshold_uncertainty_score":0.024413466},"labels":[],"label_agreement":null},{"id":"W2809595247","doi":"10.5539/mas.v12n7p49","title":"Sentiment Analysis Algorithms through Azure Machine Learning: Analysis and Comparison","year":2018,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Sentiment analysis; Support vector machine; Cloud computing; Machine learning; Social media; Analytics; Artificial intelligence; Microblogging; Big data; Data analysis; Algorithm; Social media analytics; Data mining; Data science; World Wide Web","score_opus":0.025390984638482873,"score_gpt":0.29690718585056525,"score_spread":0.27151620121208236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809595247","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4833693,0.003385485,0.47510603,0.0010411998,0.0005238973,0.0005404191,0.004061675,0.018279186,0.013692882],"genre_scores_gemma":[0.57331413,0.00100787,0.41441587,0.00018033203,0.00009905317,0.00048750284,0.006536294,0.00048844144,0.0034704956],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978612,0.00056139834,0.00029281617,0.00033223451,0.00083369523,0.00011860814],"domain_scores_gemma":[0.9971794,0.0013965126,0.00025276403,0.0004018843,0.00071493397,0.000054647997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029516402,0.00078512257,0.00054419955,0.0029801626,0.00039878118,0.001620168,0.0011999564,0.00077917305,0.0032527742],"category_scores_gemma":[0.0074331667,0.00024014025,0.0007789269,0.0019572098,0.0002870234,0.0022236048,0.000919026,0.0011248238,0.0014674558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015752326,0.0006770044,0.019743642,0.00047902323,0.00056169595,0.0001576532,0.00046564898,0.0671791,0.018483268,0.0106292125,0.021960689,0.8580879],"study_design_scores_gemma":[0.000078908066,0.00057331525,0.014953678,0.00006392602,0.00008516445,0.00014158768,0.00022912073,0.94389105,0.023424542,0.003994504,0.012518095,0.000046108296],"about_ca_topic_score_codex":0.0024210508,"about_ca_topic_score_gemma":0.002267253,"teacher_disagreement_score":0.0032527742,"about_ca_system_score_codex":0.00085100025,"about_ca_system_score_gemma":0.00041737902,"threshold_uncertainty_score":0.01560992},"labels":[],"label_agreement":null},{"id":"W2810141665","doi":"10.1109/mipr.2018.00058","title":"Soccer Fans Sentiment through the Eye of Big Data: The UEFA Champions League as a Case Study","year":2018,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Lexicon; League; Leverage (statistics); Sentiment analysis; Computer science; Artificial intelligence; Domain (mathematical analysis); Big data; Natural language processing; Machine learning; Data science; Data mining; Mathematics","score_opus":0.13278687479122103,"score_gpt":0.36998819157791313,"score_spread":0.2372013167866921,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810141665","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97734636,0.0006721075,0.00610075,0.00272662,0.00021345777,0.00014712616,0.00609064,0.000218952,0.0064840126],"genre_scores_gemma":[0.9768992,0.00045097532,0.009934722,0.00036279936,0.00017842927,0.00009410474,0.009689641,0.000056315574,0.0023337372],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922955,0.00031542144,0.00004535803,0.00012495813,0.0001893927,0.00009526805],"domain_scores_gemma":[0.9984492,0.00063359115,0.00017401655,0.00013279196,0.00037802925,0.00023231732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00110407,0.00043273935,0.00026107827,0.0012673053,0.0007295141,0.0010402546,0.00046338144,0.00071346416,0.00066372135],"category_scores_gemma":[0.0022229278,0.00010623065,0.00036459806,0.0012774233,0.0003951408,0.0009581941,0.0006951718,0.00060914847,0.0003204247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014221682,0.0017883105,0.6116381,0.0010111571,0.00043686107,0.0085058315,0.007857903,0.015069331,0.026608935,0.0043969536,0.09666161,0.22460276],"study_design_scores_gemma":[0.00014737133,0.0006933588,0.6875099,0.00033625585,0.00020044162,0.002776522,0.033935975,0.17214039,0.018542435,0.0057226024,0.07783825,0.00015651312],"about_ca_topic_score_codex":0.018397909,"about_ca_topic_score_gemma":0.048117187,"teacher_disagreement_score":0.018397909,"about_ca_system_score_codex":0.0006356194,"about_ca_system_score_gemma":0.0005031686,"threshold_uncertainty_score":0.036581635},"labels":[],"label_agreement":null},{"id":"W2814750830","doi":"","title":"Learning Emotion-enriched Word Representations","year":2018,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Similarity (geometry); Word (group theory); Natural language processing; Computer science; Meaning (existential); Affect (linguistics); Representation (politics); Artificial intelligence; Emotion classification; Contrast (vision); Psychology; Cognitive psychology; Linguistics; Communication","score_opus":0.057740884668665775,"score_gpt":0.35367466296883426,"score_spread":0.2959337783001685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2814750830","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1988266,0.0010424444,0.7910687,0.00051232777,0.00032395968,0.00015372675,0.0014158697,0.0029792937,0.0036770692],"genre_scores_gemma":[0.7955788,0.00076080946,0.19350953,0.00023943171,0.00019207201,0.00027809513,0.0047874725,0.00015257849,0.004501216],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996631,0.000073016614,0.000025431631,0.000149539,0.000045999906,0.000043008047],"domain_scores_gemma":[0.9995515,0.00017043932,0.000056139303,0.0000774562,0.00012329634,0.000021116906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035591467,0.0010874553,0.0004566773,0.0009804579,0.00020217376,0.00073376513,0.0008016798,0.0008409607,0.0022518751],"category_scores_gemma":[0.0023432192,0.00019130777,0.00068014715,0.0009462062,0.00027738413,0.00197978,0.000851773,0.0011106118,0.001029467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049729645,0.00040992268,0.003740117,0.00034051618,0.00018450874,0.0002501267,0.0004701553,0.056836113,0.052562557,0.01055502,0.013619407,0.8605343],"study_design_scores_gemma":[0.000058471094,0.00020425396,0.002228771,0.000045660316,0.00012294974,0.00014265101,0.00022507312,0.94537604,0.014279632,0.03312405,0.004159208,0.00003319436],"about_ca_topic_score_codex":0.0007891998,"about_ca_topic_score_gemma":0.0012719877,"teacher_disagreement_score":0.0022518751,"about_ca_system_score_codex":0.00040506065,"about_ca_system_score_gemma":0.00032870428,"threshold_uncertainty_score":0.0075332522},"labels":[],"label_agreement":null},{"id":"W2887804579","doi":"10.1111/coin.12189","title":"Exploring deep neural networks for multitarget stance detection","year":2018,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Subjectivity; Computer science; Artificial intelligence; Independence (probability theory); Dependency (UML); Machine learning; Epistemology; Mathematics","score_opus":0.13752941104644828,"score_gpt":0.31971774538462483,"score_spread":0.18218833433817655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2887804579","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2750774,0.0014813547,0.713342,0.0017143651,0.00011606752,0.00005449867,0.00029725526,0.0008627631,0.0070542367],"genre_scores_gemma":[0.965407,0.00018914533,0.031247115,0.00015683465,0.000053525742,0.000021950334,0.00021198005,0.00003286041,0.0026795396],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997501,0.000084986554,0.000009409563,0.00005933476,0.00004195536,0.000054226948],"domain_scores_gemma":[0.999218,0.00047267883,0.00011590543,0.00004003236,0.000108021835,0.000045310033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010385752,0.0007902992,0.00050763175,0.00075397803,0.000265458,0.0007983841,0.0009946802,0.0009405078,0.0014279153],"category_scores_gemma":[0.002092907,0.0003975548,0.00039448065,0.0005940195,0.0003867281,0.0011280649,0.0009675781,0.0015226434,0.00039187458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040074307,0.00035358756,0.010362964,0.00016964403,0.00022144307,0.0002882091,0.00021918798,0.6200373,0.021579668,0.017691279,0.0053302976,0.32334566],"study_design_scores_gemma":[0.0000019441543,0.000008127124,0.0002083545,0.000003922898,0.000004458455,0.00000463187,0.0000068143777,0.99559337,0.0005534619,0.0035076493,0.000105796746,0.0000014539619],"about_ca_topic_score_codex":0.00359984,"about_ca_topic_score_gemma":0.0043042814,"teacher_disagreement_score":0.00359984,"about_ca_system_score_codex":0.00079065404,"about_ca_system_score_gemma":0.0004284367,"threshold_uncertainty_score":0.0071578026},"labels":[],"label_agreement":null},{"id":"W2890462856","doi":"","title":"A Comparison of Sentiment Analysis Tools","year":2018,"lang":"en","type":"article","venue":"Journal of the Association for Information Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Sentiment analysis; Computer science; Natural language processing; Artificial intelligence","score_opus":0.03632656804051622,"score_gpt":0.32354047096286315,"score_spread":0.28721390292234694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890462856","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5724688,0.017855402,0.27702492,0.0033423603,0.0022921362,0.0035135497,0.013865812,0.02587781,0.08375917],"genre_scores_gemma":[0.6109984,0.006658971,0.35190374,0.0011466914,0.0006823733,0.0024912686,0.015876416,0.0015998464,0.008642187],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9898934,0.0035035957,0.0012187872,0.00074118655,0.0042521763,0.00039092757],"domain_scores_gemma":[0.9719151,0.017337127,0.001593622,0.0010959097,0.0075657056,0.00049247843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0088713765,0.0014682952,0.0009822489,0.009453813,0.0006159431,0.0029166942,0.0012100212,0.00091283204,0.0035287447],"category_scores_gemma":[0.030384876,0.00044129856,0.0015421867,0.0036959366,0.00041505793,0.004090713,0.0015512708,0.0009096006,0.0024037494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0032930935,0.0006819506,0.031905137,0.0048370296,0.0010642987,0.00035512418,0.0028231286,0.0035382453,0.030766025,0.00621312,0.036984455,0.8775385],"study_design_scores_gemma":[0.0014862618,0.0051546013,0.21569252,0.004606423,0.0019803552,0.0029254523,0.012041182,0.34277213,0.09133353,0.020214196,0.30076408,0.0010292786],"about_ca_topic_score_codex":0.0009157848,"about_ca_topic_score_gemma":0.0013013472,"teacher_disagreement_score":0.009453813,"about_ca_system_score_codex":0.00078614336,"about_ca_system_score_gemma":0.00094601687,"threshold_uncertainty_score":0.046916902},"labels":[],"label_agreement":null},{"id":"W2898870554","doi":"10.18653/v1/w18-5214","title":"Using context to identify the language of face-saving","year":2018,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Face (sociological concept); Computer science; Context (archaeology); Natural language processing; Artificial intelligence; Linguistics; History","score_opus":0.06698015611158933,"score_gpt":0.36867882590602125,"score_spread":0.30169866979443194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898870554","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8797962,0.0021684156,0.08738707,0.001313189,0.0005245392,0.00017762354,0.0072802803,0.0014459386,0.019906837],"genre_scores_gemma":[0.9623613,0.00034202257,0.029192956,0.00014448901,0.00014133668,0.00012718319,0.004441448,0.00017039746,0.003078792],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893445,0.00043816882,0.000067862806,0.0002692548,0.00019791596,0.00009236526],"domain_scores_gemma":[0.9964005,0.0021989713,0.00042157646,0.0001852669,0.00066278584,0.00013095861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084051205,0.00059590413,0.0002856903,0.001739188,0.0006807532,0.0011395974,0.00038817304,0.0006463428,0.0020919591],"category_scores_gemma":[0.006528135,0.00026178616,0.00036809855,0.00088351674,0.00051768165,0.0019222284,0.00072384626,0.0012065426,0.001310081],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002329794,0.00057382823,0.14905618,0.0025747207,0.00031136998,0.00514938,0.03521076,0.016143853,0.27168265,0.021569887,0.045741808,0.44965577],"study_design_scores_gemma":[0.00014424362,0.0007210022,0.23798434,0.00083739794,0.0004904206,0.0053578806,0.02107675,0.42373273,0.11341505,0.016663369,0.17911404,0.00046270122],"about_ca_topic_score_codex":0.005905759,"about_ca_topic_score_gemma":0.009123064,"teacher_disagreement_score":0.005905759,"about_ca_system_score_codex":0.000650385,"about_ca_system_score_gemma":0.0006817319,"threshold_uncertainty_score":0.011742771},"labels":[],"label_agreement":null},{"id":"W2900120976","doi":"10.18653/v1/w18-6219","title":"Self-Attention: A Better Building Block for Sentiment Analysis Neural Network Classifiers","year":2018,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Recurrent neural network; Sentiment analysis; Block (permutation group theory); Artificial intelligence; Artificial neural network; Task (project management); Sequence (biology); Machine learning","score_opus":0.02036846082033298,"score_gpt":0.27747934012521497,"score_spread":0.25711087930488197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900120976","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044428457,0.0012449784,0.9409097,0.0008507631,0.00026399232,0.00018612179,0.0003808086,0.0049681347,0.006767036],"genre_scores_gemma":[0.52593726,0.0009400698,0.45923483,0.0009648253,0.0005213267,0.0002799636,0.0014971658,0.00039164236,0.010232905],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999471,0.00013229628,0.000039585047,0.00015890457,0.00013122507,0.00006709835],"domain_scores_gemma":[0.99881685,0.00041238163,0.000084001455,0.00019549819,0.00044423802,0.000047099114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017536951,0.0009795182,0.00081921247,0.0010508727,0.00043530637,0.001024998,0.0011140173,0.0009308649,0.006259658],"category_scores_gemma":[0.003168451,0.00041872545,0.00068485114,0.00076585147,0.00036078555,0.0027317111,0.000940166,0.0016579381,0.002351419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034162827,0.00027640056,0.0035214282,0.00018348302,0.00021730838,0.00011607886,0.00013046803,0.08454472,0.038576234,0.014307905,0.011275084,0.8465093],"study_design_scores_gemma":[0.000018881014,0.00012057803,0.0011885762,0.000020494952,0.00004890595,0.000049797014,0.000017854818,0.9758016,0.012083982,0.0055169333,0.0051178294,0.000014565258],"about_ca_topic_score_codex":0.005273637,"about_ca_topic_score_gemma":0.007642488,"teacher_disagreement_score":0.006259658,"about_ca_system_score_codex":0.00079090724,"about_ca_system_score_gemma":0.00068151753,"threshold_uncertainty_score":0.020940602},"labels":[],"label_agreement":null},{"id":"W2904008005","doi":"10.1007/978-3-030-02592-2_6","title":"Detecting Canadian Internet Satisfaction by Analyzing Twitter Accounts of Shaw Communications","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in social networks","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Customer satisfaction; Order (exchange); Advertising; Social media; The Internet; Business; Computer science; Telecommunications; Marketing; Data science; World Wide Web","score_opus":0.028449798637786714,"score_gpt":0.2766354510552305,"score_spread":0.2481856524174438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904008005","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9595588,0.00023956629,0.0010717986,0.00046573125,0.000053773307,0.000054226177,0.01326287,0.00015722305,0.025136039],"genre_scores_gemma":[0.9832275,0.00020676055,0.001148029,0.00006203509,0.000037928738,0.000032185926,0.008927028,0.000034044755,0.0063245357],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922454,0.000060380335,0.000026795655,0.00006631886,0.000417483,0.00020444469],"domain_scores_gemma":[0.99690044,0.00041964583,0.00030453433,0.00008064525,0.0019099731,0.00038465223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051552954,0.00033219578,0.00031126,0.0039993455,0.0017305393,0.001780571,0.00048082674,0.00040014944,0.0042852527],"category_scores_gemma":[0.003913191,0.00013947586,0.0002316197,0.008067664,0.0003310491,0.0007489927,0.00057512155,0.000503349,0.0012665138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030534665,0.00013521571,0.8837088,0.000098081044,0.00010898865,0.0001848268,0.0021551785,0.001781941,0.0020283,0.0018456586,0.040209454,0.06743825],"study_design_scores_gemma":[0.000008390131,0.000054031636,0.95483875,0.000029683286,0.000045331497,0.000070413436,0.008263408,0.013977901,0.0011162241,0.0002878121,0.021255877,0.000052161806],"about_ca_topic_score_codex":0.8406355,"about_ca_topic_score_gemma":0.92197967,"teacher_disagreement_score":0.15936452,"about_ca_system_score_codex":0.0045673037,"about_ca_system_score_gemma":0.004249199,"threshold_uncertainty_score":0.32060605},"labels":[],"label_agreement":null},{"id":"W2904335795","doi":"10.1007/978-3-030-04375-9_2","title":"Affectional Ontology and Multimedia Dataset for Sentiment Analysis","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Ontology; Computer science; Domain (mathematical analysis); Sentiment analysis; Upper ontology; Information retrieval; Ontology-based data integration; Annotation; Natural language processing; Suggested Upper Merged Ontology; Ontology alignment; Artificial intelligence; Domain knowledge","score_opus":0.022809683507426484,"score_gpt":0.28833563940035917,"score_spread":0.2655259558929327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904335795","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.069213465,0.00103343,0.011295757,0.0008414584,0.0007237962,0.0010787434,0.87968147,0.0088054165,0.027326437],"genre_scores_gemma":[0.034787938,0.00035369032,0.017228428,0.0001906423,0.000102861064,0.0008453979,0.93878037,0.0002555142,0.0074551725],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994449,0.00007180738,0.00006732388,0.000101105805,0.00023036046,0.0000845425],"domain_scores_gemma":[0.9993881,0.000106804226,0.000056930592,0.000115679555,0.0002475923,0.00008490767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056999456,0.0010511411,0.0004023926,0.004130095,0.0007576358,0.0008640465,0.00075792137,0.0007025395,0.0085678315],"category_scores_gemma":[0.0021658742,0.0001618321,0.0007476605,0.002895265,0.00020397002,0.0010223653,0.00088851963,0.00088273635,0.006845029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005212579,0.000679951,0.0064073615,0.0010976809,0.00012456486,0.00036223867,0.00028045237,0.0014613102,0.0167286,0.003680607,0.8251701,0.14348583],"study_design_scores_gemma":[0.00022334605,0.00021160404,0.043652497,0.00023950315,0.00017596158,0.00055715267,0.00093254185,0.021740312,0.017476875,0.004088113,0.9105788,0.00012328778],"about_ca_topic_score_codex":0.012065573,"about_ca_topic_score_gemma":0.02376826,"teacher_disagreement_score":0.012065573,"about_ca_system_score_codex":0.00083808484,"about_ca_system_score_gemma":0.0012064334,"threshold_uncertainty_score":0.028662264},"labels":[],"label_agreement":null},{"id":"W2904391139","doi":"10.1109/access.2018.2885117","title":"Sentiment Identification in Football-Specific Tweets","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Football; Sentiment analysis; Computer science; Popularity; Lexicon; Classifier (UML); Social media; Artificial intelligence; Identification (biology); Feeling; Natural language processing; Data science; World Wide Web; Psychology; History","score_opus":0.053076314941448446,"score_gpt":0.3389338855961466,"score_spread":0.2858575706546982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904391139","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8392037,0.001069882,0.054172624,0.0010285379,0.00090702425,0.0011463395,0.07604499,0.002099378,0.02432741],"genre_scores_gemma":[0.83682567,0.0009639618,0.062272064,0.00032161997,0.0006292079,0.00085001375,0.08843669,0.00018585201,0.009514983],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999424,0.00007989253,0.000094059236,0.00013242007,0.00017433468,0.00009545604],"domain_scores_gemma":[0.9984211,0.00043504097,0.00031496372,0.0000854397,0.00065642316,0.00008706272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005213743,0.000585864,0.00044070414,0.003399088,0.0006420185,0.00095633994,0.00026414945,0.0004094978,0.0019638985],"category_scores_gemma":[0.0025108806,0.00013919127,0.00043643342,0.0020757734,0.00016785525,0.0010203084,0.00054277,0.00043402685,0.0014703815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001649008,0.00061227044,0.2397825,0.001866577,0.0002663121,0.002095717,0.0039252015,0.0040565846,0.22026733,0.0041298373,0.0696404,0.4517083],"study_design_scores_gemma":[0.00009960012,0.00053781964,0.55663306,0.0005430364,0.00031301676,0.001774221,0.01001002,0.16330336,0.10761127,0.0070983223,0.15191317,0.00016304107],"about_ca_topic_score_codex":0.003206512,"about_ca_topic_score_gemma":0.0061536743,"teacher_disagreement_score":0.003399088,"about_ca_system_score_codex":0.000531045,"about_ca_system_score_gemma":0.00046369672,"threshold_uncertainty_score":0.0065698624},"labels":[],"label_agreement":null},{"id":"W2906928036","doi":"10.4000/books.aaccademia.4545","title":"Aspect-based Sentiment Analysis: X2Check at ABSITA 2018","year":2018,"lang":"en","type":"book-chapter","venue":"Accademia University Press eBooks","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Council Canada; Università degli Studi di Napoli Federico II","keywords":"Task (project management); Set (abstract data type); Polarity (international relations); Computer science; Sentiment analysis; Artificial intelligence; Training set; Natural language processing; Information retrieval; Engineering; Programming language; Chemistry; Systems engineering","score_opus":0.03266538007875537,"score_gpt":0.23089461969215377,"score_spread":0.1982292396133984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906928036","genre_codex":"software","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20448828,0.007342575,0.13943598,0.0034047589,0.0057128817,0.002083909,0.09333145,0.44765046,0.09654985],"genre_scores_gemma":[0.29702947,0.001680424,0.25479943,0.0013124595,0.0010749776,0.001390255,0.31872278,0.03308833,0.09090197],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973143,0.0005536421,0.00016913771,0.0005214053,0.0012708516,0.00017062073],"domain_scores_gemma":[0.996307,0.0008392085,0.00018497836,0.00074553315,0.0016630922,0.00026027384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036848248,0.0017318347,0.00083493476,0.0029491659,0.0008454242,0.0024738642,0.0013841267,0.0010326372,0.02722303],"category_scores_gemma":[0.008749193,0.00071032875,0.0008720846,0.0021379823,0.00032105923,0.0035481348,0.0028099834,0.0014122993,0.023247115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013883085,0.00040026492,0.0065596816,0.0006795622,0.00015535436,0.00025059227,0.0005718415,0.0015266668,0.024641633,0.0023543164,0.5575876,0.40388408],"study_design_scores_gemma":[0.0010592502,0.0012984587,0.039664276,0.00036664883,0.00024140712,0.0009894106,0.00076926954,0.14224035,0.091683194,0.00966767,0.7117306,0.00028942787],"about_ca_topic_score_codex":0.004260083,"about_ca_topic_score_gemma":0.004276602,"teacher_disagreement_score":0.02722303,"about_ca_system_score_codex":0.001137889,"about_ca_system_score_gemma":0.0011020624,"threshold_uncertainty_score":0.091070056},"labels":[],"label_agreement":null},{"id":"W2907310141","doi":"10.4000/books.aaccademia.4539","title":"ItVENSES - A Symbolic System for Aspect-Based Sentiment Analysis","year":2018,"lang":"en","type":"book-chapter","venue":"Accademia University Press eBooks","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Council Canada; Università degli Studi di Napoli Federico II","keywords":"Computer science; Sentence; Natural language processing; Parsing; Negation; Artificial intelligence; Predicate (mathematical logic); Polarity (international relations); Programming language","score_opus":0.03234764403113062,"score_gpt":0.2364144771928378,"score_spread":0.2040668331617072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907310141","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029040445,0.0007385823,0.75283325,0.00050970796,0.00029627891,0.0005417029,0.011982835,0.1729305,0.03112673],"genre_scores_gemma":[0.23862858,0.0008297039,0.6798691,0.0003419411,0.00027153592,0.0008768719,0.044033714,0.012377186,0.0227714],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951184,0.00010919528,0.000060740575,0.00012096283,0.00015980833,0.00003749985],"domain_scores_gemma":[0.99950075,0.00017501327,0.00006219264,0.00008322065,0.00015161006,0.000027301641],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007079708,0.00096419157,0.0005957843,0.0019494764,0.00035990454,0.0015030698,0.00084724155,0.00043480334,0.02129854],"category_scores_gemma":[0.002437768,0.00040092683,0.0009687477,0.0011495075,0.00032173024,0.0019805555,0.0012883001,0.0008480096,0.013337599],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063335715,0.0001509032,0.0043973215,0.0012103283,0.00018383449,0.00042141095,0.0011409358,0.0032087474,0.08511405,0.018157143,0.10969497,0.775687],"study_design_scores_gemma":[0.0002539013,0.00036250768,0.015244043,0.0003513126,0.0003536721,0.0018226457,0.0007816721,0.3556918,0.120363116,0.061588243,0.44299826,0.00018876638],"about_ca_topic_score_codex":0.0010273609,"about_ca_topic_score_gemma":0.0014481444,"teacher_disagreement_score":0.02129854,"about_ca_system_score_codex":0.0005240004,"about_ca_system_score_gemma":0.00061534694,"threshold_uncertainty_score":0.07125068},"labels":[],"label_agreement":null},{"id":"W2911517153","doi":"10.3169/mta.9.262","title":"[Paper] Measuring Similarity between Brands using Social Media Content","year":2021,"lang":"en","type":"article","venue":"ITE Transactions on Media Technology and Applications","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre de Géomatique du Québec","funders":"","keywords":"Content (measure theory); Similarity (geometry); Advertising; Social media; User-generated content; Computer science; Information retrieval; Mathematics; Business; Artificial intelligence; World Wide Web","score_opus":0.08837451358766958,"score_gpt":0.27808160880018823,"score_spread":0.18970709521251866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911517153","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77967685,0.0019059082,0.15876418,0.0014266155,0.00083043944,0.00071222056,0.0060636853,0.0014094942,0.049210515],"genre_scores_gemma":[0.9579433,0.0003709513,0.034715228,0.0002109684,0.0002889509,0.000097756005,0.0017830328,0.00004502344,0.0045448258],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990614,0.0001705863,0.00009150972,0.00018315535,0.00043841844,0.000055013254],"domain_scores_gemma":[0.99715096,0.0008021751,0.0005320037,0.000288162,0.0011358085,0.00009091534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007700004,0.00046410575,0.0003494376,0.0046315403,0.00041110674,0.0011613999,0.0004981136,0.00084991317,0.004340022],"category_scores_gemma":[0.0059287054,0.00014684994,0.00039860164,0.003078226,0.00032315755,0.0018978908,0.0004926883,0.0003001564,0.0021400738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00083472324,0.0005702875,0.266811,0.0006694868,0.0007550487,0.00027381984,0.0009564418,0.004508819,0.0215436,0.0078544775,0.019013556,0.6762088],"study_design_scores_gemma":[0.0001261102,0.0012062044,0.6333781,0.00021020202,0.0008182153,0.0021369793,0.0021162198,0.23733678,0.051164035,0.01956612,0.05169456,0.00024642743],"about_ca_topic_score_codex":0.0016942555,"about_ca_topic_score_gemma":0.0017675086,"teacher_disagreement_score":0.0046315403,"about_ca_system_score_codex":0.00032908996,"about_ca_system_score_gemma":0.00020487049,"threshold_uncertainty_score":0.014518797},"labels":[],"label_agreement":null},{"id":"W2912276085","doi":"10.3166/ria.32.s1.103-114","title":"A novel semantic matching method for chatbots based on convolutional neural network and attention mechanism","year":2018,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mechanism (biology); Computer science; Convolutional neural network; Matching (statistics); Artificial intelligence; Semantic matching; Natural language processing; Mathematics; Philosophy","score_opus":0.055346438796595336,"score_gpt":0.31220202724798624,"score_spread":0.2568555884513909,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912276085","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028891442,0.00054355635,0.95957595,0.00019365449,0.0002978832,0.0002540108,0.00042366076,0.0059287096,0.0038911256],"genre_scores_gemma":[0.45727545,0.00041726007,0.5171761,0.00034562266,0.00022528887,0.00029432427,0.0027349514,0.00045047866,0.021080531],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988207,0.0001314615,0.00006823015,0.0004425648,0.0003707278,0.00016628341],"domain_scores_gemma":[0.9994611,0.00009364162,0.000050393504,0.00008883677,0.0002428418,0.00006325066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079153606,0.0010102873,0.0012140885,0.003198499,0.0011446461,0.00095143454,0.0018881578,0.0015879725,0.0050979266],"category_scores_gemma":[0.0015786911,0.00040878207,0.0010947619,0.002032592,0.0005055216,0.0026140756,0.0016079757,0.0010694147,0.0020536643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004658144,0.0005509098,0.0026794102,0.00028211475,0.00016375641,0.00021792967,0.00020842685,0.010515708,0.048603192,0.014321874,0.017776472,0.90421444],"study_design_scores_gemma":[0.000052484953,0.00017327776,0.0029405109,0.000029208015,0.00012672377,0.00032817645,0.00015487138,0.943557,0.029319592,0.0129271485,0.010338676,0.000052213614],"about_ca_topic_score_codex":0.011285956,"about_ca_topic_score_gemma":0.01432181,"teacher_disagreement_score":0.011285956,"about_ca_system_score_codex":0.00092542457,"about_ca_system_score_gemma":0.0017802861,"threshold_uncertainty_score":0.022440553},"labels":[],"label_agreement":null},{"id":"W2913898221","doi":"10.1109/access.2019.2892852","title":"A Hybrid Framework for Sentiment Analysis Using Genetic Algorithm Based Feature Reduction","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":155,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University; McGill University","funders":"Zayed University","keywords":"Computer science; Sentiment analysis; Lexicon; Artificial intelligence; Scalability; Machine learning; Feature (linguistics); Rough set; Data mining; Algorithm","score_opus":0.030521814475669047,"score_gpt":0.3267984758927722,"score_spread":0.2962766614171032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913898221","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0066854567,0.00008664388,0.9913935,0.00006258019,0.000028610018,0.00005715931,0.000042541484,0.0008014082,0.00084208336],"genre_scores_gemma":[0.15642993,0.00016535679,0.8406226,0.00011330014,0.00004311788,0.00032060538,0.00030934167,0.00014184916,0.0018538971],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996252,0.00008503536,0.000020467623,0.000074591924,0.00015722559,0.000037470425],"domain_scores_gemma":[0.99973947,0.000087423214,0.000027801656,0.00002217298,0.00011428067,0.000008922661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057734834,0.00087269256,0.0008119042,0.0012186703,0.00035735505,0.0008380525,0.00090349204,0.0006140551,0.0012016967],"category_scores_gemma":[0.0011102246,0.00031105473,0.0011301872,0.0010200931,0.00035977404,0.00062263093,0.00048908795,0.00066270947,0.00049725705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001373942,0.0002045626,0.0017833823,0.00015964979,0.00019645934,0.00023035484,0.00021153632,0.43993542,0.043784637,0.017311148,0.0035884075,0.492457],"study_design_scores_gemma":[0.000011546522,0.000040908733,0.00025734675,0.0000052797723,0.000017175838,0.000033020424,0.000015810032,0.9924896,0.0024924057,0.0031915177,0.00143453,0.000010888119],"about_ca_topic_score_codex":0.0051537524,"about_ca_topic_score_gemma":0.0034618522,"teacher_disagreement_score":0.0051537524,"about_ca_system_score_codex":0.00060295145,"about_ca_system_score_gemma":0.00065596536,"threshold_uncertainty_score":0.010247469},"labels":[],"label_agreement":null},{"id":"W2921300811","doi":"10.1007/978-3-030-15035-8_32","title":"Sentiment Analysis of Arabic and English Tweets","year":2019,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Arabic; Sentiment analysis; Natural language processing; Computer science; Artificial intelligence; Linguistics; Philosophy","score_opus":0.015369254999277276,"score_gpt":0.26370467529168207,"score_spread":0.2483354202924048,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921300811","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8487086,0.0028479733,0.046464108,0.0019046388,0.0017012523,0.00033208224,0.01606072,0.0012665706,0.08071406],"genre_scores_gemma":[0.9156864,0.0017017443,0.03969602,0.00022913812,0.0005646926,0.00023474451,0.013360195,0.00028374902,0.028243313],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977046,0.000052372852,0.000025223824,0.000026475109,0.00008958532,0.000035784153],"domain_scores_gemma":[0.99936396,0.00021320023,0.000061069375,0.000021003632,0.0003140773,0.000026657184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041994505,0.00032569937,0.00017088467,0.0014460215,0.00060746307,0.0009812597,0.00013022254,0.00016530043,0.0055835936],"category_scores_gemma":[0.0017504485,0.00007290312,0.0003372038,0.0014444842,0.00012492371,0.0005542625,0.0003398089,0.00035041588,0.002229239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019054138,0.0003089468,0.038605686,0.0011792082,0.00016347501,0.0010933265,0.00445492,0.002548308,0.1563284,0.008831735,0.06729643,0.71728414],"study_design_scores_gemma":[0.000102262406,0.00079932914,0.26157942,0.0006322375,0.0005810874,0.0030528111,0.01757973,0.12091887,0.17534561,0.0126300445,0.4065721,0.00020654796],"about_ca_topic_score_codex":0.001287072,"about_ca_topic_score_gemma":0.0018343689,"teacher_disagreement_score":0.0055835936,"about_ca_system_score_codex":0.00036096477,"about_ca_system_score_gemma":0.00025953137,"threshold_uncertainty_score":0.018678963},"labels":[],"label_agreement":null},{"id":"W2922318995","doi":"10.5220/0007313503680376","title":"Multimodal Sentiment Analysis: A Multitask Learning Approach","year":2019,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Sentiment analysis; Multi-task learning; Artificial intelligence; Machine learning; Natural language processing; Task (project management); Engineering; Systems engineering","score_opus":0.011601793995757798,"score_gpt":0.24427986551091332,"score_spread":0.23267807151515552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2922318995","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045155205,0.0008818791,0.94855,0.0010358345,0.00021851283,0.00016586808,0.00046886344,0.00090716104,0.0026167892],"genre_scores_gemma":[0.7682393,0.00063740247,0.22212039,0.0005314071,0.0009683629,0.00037084363,0.0014416421,0.00021292713,0.005477628],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855417,0.00060057145,0.00008641549,0.00032406818,0.00025398735,0.00018071634],"domain_scores_gemma":[0.9979972,0.0010212689,0.00020173192,0.00017502435,0.00046235105,0.00014237958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032725702,0.0014895606,0.0012848397,0.0016719669,0.00080225477,0.0014598018,0.0015157119,0.0015995846,0.0023761583],"category_scores_gemma":[0.0046121655,0.00052160444,0.00187127,0.0011506343,0.0004811328,0.001429386,0.0015160317,0.002117085,0.0011183535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00097074575,0.0012616032,0.010951334,0.00045722403,0.00088441826,0.00047838315,0.00061763433,0.17902987,0.0322042,0.012544055,0.021816958,0.7387835],"study_design_scores_gemma":[0.000016236312,0.0000736591,0.0013391958,0.000012586934,0.000054224936,0.00004001577,0.00005240976,0.98616624,0.0016129438,0.009110756,0.0015018699,0.000019844903],"about_ca_topic_score_codex":0.003044882,"about_ca_topic_score_gemma":0.0036262257,"teacher_disagreement_score":0.0032725702,"about_ca_system_score_codex":0.00087826245,"about_ca_system_score_gemma":0.0006851641,"threshold_uncertainty_score":0.017307162},"labels":[],"label_agreement":null},{"id":"W2924078677","doi":"","title":"Detection of Twitter Users' Attitudes about Flu Vaccine based on the Content and Sentiment Analysis of the Sent Tweets","year":2019,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sentiment analysis; Advertising; Internet privacy; Content analysis; World Wide Web; Computer science; Business; Natural language processing; Sociology","score_opus":0.2000175815167489,"score_gpt":0.4763263278967882,"score_spread":0.2763087463800393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2924078677","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9697972,0.0017451461,0.0025660011,0.0006297856,0.00015531726,0.00027985786,0.017005112,0.0001943661,0.00762735],"genre_scores_gemma":[0.9797044,0.0016547013,0.0042541074,0.0001418526,0.00019713129,0.0003189073,0.010397943,0.000027016817,0.003303895],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996101,0.00007435516,0.00006633383,0.00006967602,0.0001380708,0.00004139798],"domain_scores_gemma":[0.9987533,0.0004780004,0.00035306226,0.000038498463,0.00031624341,0.00006097482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005309886,0.000311287,0.00032509692,0.0029942393,0.00030476117,0.0007748071,0.00017514786,0.00029151427,0.0014046076],"category_scores_gemma":[0.0026360957,0.00012726031,0.00040724903,0.0018561232,0.00014197611,0.00084091374,0.00041735734,0.00022565892,0.0007463572],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013448261,0.00018812608,0.6872897,0.00496401,0.00041727803,0.0018163203,0.009574464,0.0011536216,0.039791275,0.0011957982,0.029018998,0.22324558],"study_design_scores_gemma":[0.000025361931,0.0003883126,0.93494594,0.00040246564,0.00029382066,0.00091376563,0.009462076,0.011700751,0.011800926,0.00059952564,0.029385116,0.00008186908],"about_ca_topic_score_codex":0.001976501,"about_ca_topic_score_gemma":0.0031745124,"teacher_disagreement_score":0.0029942393,"about_ca_system_score_codex":0.0002491541,"about_ca_system_score_gemma":0.00021243822,"threshold_uncertainty_score":0.0046988726},"labels":[],"label_agreement":null},{"id":"W2926337278","doi":"10.48550/arxiv.1904.00132","title":"ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"SemEval; Computer science; Task (project management); Utterance; Context (archaeology); Artificial intelligence; Emotion detection; Natural language processing; Emotion recognition","score_opus":0.053210681853575095,"score_gpt":0.20301890264243402,"score_spread":0.14980822078885891,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2926337278","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34528098,0.0060128076,0.33219326,0.0044326303,0.006270113,0.0017399625,0.057253137,0.18423887,0.06257831],"genre_scores_gemma":[0.571648,0.0006859457,0.27379945,0.00149057,0.00084660517,0.0014441743,0.09479006,0.0039374535,0.051357783],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987477,0.00040276058,0.00005150622,0.00044937147,0.00019284051,0.00015570511],"domain_scores_gemma":[0.9989868,0.00031024477,0.000054753626,0.00023272078,0.0002944426,0.00012108818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018680203,0.002113871,0.0011167784,0.0006504488,0.00085821666,0.0017826563,0.0014011025,0.0021775684,0.012152481],"category_scores_gemma":[0.0037392105,0.0005223324,0.00094851834,0.00042976672,0.00030673575,0.0023723182,0.002016553,0.0020933293,0.011169891],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027376292,0.0008112437,0.0040673185,0.0009464431,0.00042357642,0.0006617782,0.0006081639,0.0143893035,0.08919392,0.004295967,0.3810181,0.5008466],"study_design_scores_gemma":[0.0007229388,0.0014653829,0.014422064,0.00020337902,0.0003330716,0.0009414416,0.00086788664,0.6752412,0.091474675,0.013279804,0.20076913,0.0002790867],"about_ca_topic_score_codex":0.0047640335,"about_ca_topic_score_gemma":0.009163625,"teacher_disagreement_score":0.012152481,"about_ca_system_score_codex":0.00072228553,"about_ca_system_score_gemma":0.00085857365,"threshold_uncertainty_score":0.040654123},"labels":[],"label_agreement":null},{"id":"W2930446584","doi":"10.5539/ijel.v9n3p13","title":"A Corpus-Based Study of Hillary Clinton’s and Donald Trump’s Linguistic Styles","year":2019,"lang":"en","type":"article","venue":"International Journal of English Linguistics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Rhetorical question; Style (visual arts); Rhetoric; Politics; Sociology; Presidential system; Political science; Media studies; Law; Linguistics; Art; Literature; Philosophy","score_opus":0.01569477986101624,"score_gpt":0.28482491598417925,"score_spread":0.269130136123163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2930446584","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9598547,0.000936636,0.00064562797,0.0012426797,0.00016187625,0.00013129831,0.0015386539,0.000015190435,0.035473425],"genre_scores_gemma":[0.983977,0.0011709752,0.0017186959,0.00048264372,0.000116601455,0.0003132576,0.0019438581,0.000090997426,0.010185943],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99798656,0.0009493685,0.000135968,0.00026495464,0.00051384675,0.00014927883],"domain_scores_gemma":[0.99017906,0.0067042448,0.0009861384,0.00042315794,0.0012725982,0.00043480453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027150463,0.00021516718,0.00027552096,0.0039311796,0.0034318815,0.0022874072,0.00038963283,0.0005663408,0.0031881053],"category_scores_gemma":[0.009192252,0.00026153546,0.0000951987,0.006250126,0.002202912,0.0011159707,0.0017028571,0.0011167268,0.0005712904],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015999484,0.00014200046,0.03657922,0.00050924387,0.000020367033,0.0014323714,0.85476553,0.00011077552,0.008079996,0.008894258,0.0265037,0.06280253],"study_design_scores_gemma":[0.000022295013,0.00007162026,0.22141083,0.00043151682,0.000019324814,0.00089424854,0.47015885,0.0004892919,0.0021572527,0.0007662858,0.30349982,0.000078726414],"about_ca_topic_score_codex":0.02626227,"about_ca_topic_score_gemma":0.093207575,"teacher_disagreement_score":0.02626227,"about_ca_system_score_codex":0.0024211407,"about_ca_system_score_gemma":0.0019354755,"threshold_uncertainty_score":0.052218854},"labels":[],"label_agreement":null},{"id":"W2934857089","doi":"10.1007/978-3-030-15712-8_55","title":"On Interpretability and Feature Representations: An Analysis of the Sentiment Neuron","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cisco Systems (Canada)","funders":"","keywords":"Interpretability; Computer science; Sentiment analysis; Artificial intelligence; Classifier (UML); Neuron; Feature (linguistics); Representation (politics); Pattern recognition (psychology); Machine learning; Neuroscience","score_opus":0.015165132403360366,"score_gpt":0.275229253428267,"score_spread":0.2600641210249066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2934857089","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30641198,0.0025537587,0.65797806,0.0023608801,0.00016497183,0.000084552856,0.00053117325,0.00040983027,0.029504793],"genre_scores_gemma":[0.9344971,0.0010042886,0.05825973,0.00013943284,0.00015745584,0.000043784203,0.00035782537,0.0001456891,0.0053946683],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997298,0.00007438533,0.000015744965,0.000051621122,0.00009362866,0.000034778193],"domain_scores_gemma":[0.9987949,0.0007744162,0.000082371225,0.00011055333,0.00020500856,0.000032644333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085142965,0.00040585225,0.0004684859,0.00073939643,0.0004069056,0.0018597895,0.00060687255,0.00054121605,0.0037264042],"category_scores_gemma":[0.0054597794,0.00018446153,0.00051841664,0.0008877402,0.00073814957,0.0025135335,0.0007119417,0.0009585744,0.00027976598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003734451,0.00010936431,0.0085573625,0.00031639,0.00013998503,0.0006084948,0.0009937048,0.04845133,0.041484736,0.426885,0.00853723,0.46354297],"study_design_scores_gemma":[0.00001138312,0.00007796434,0.008110079,0.000047848782,0.000052736912,0.00023298994,0.00023741163,0.63864404,0.0042534787,0.3447354,0.0035771746,0.00001947271],"about_ca_topic_score_codex":0.0013907108,"about_ca_topic_score_gemma":0.0010906684,"teacher_disagreement_score":0.0037264042,"about_ca_system_score_codex":0.0006067661,"about_ca_system_score_gemma":0.00024995976,"threshold_uncertainty_score":0.012466013},"labels":[],"label_agreement":null},{"id":"W2937170667","doi":"10.1145/3306500.3306514","title":"Feature-based Facebook reviews process model for e-management using data mining","year":2019,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Sentiment analysis; Punctuation; Information retrieval; Analytics; Social media; Process (computing); Feature (linguistics); Data science; World Wide Web; Natural language processing; Artificial intelligence","score_opus":0.1633281797723798,"score_gpt":0.37190094418645425,"score_spread":0.20857276441407446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2937170667","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09764718,0.00047383783,0.88384336,0.0018465542,0.0001171666,0.0009868592,0.003416588,0.0031299526,0.008538494],"genre_scores_gemma":[0.7983265,0.00039935086,0.18418594,0.00020753626,0.000102021164,0.0014204526,0.0028531232,0.00013181922,0.012373231],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981476,0.0005457436,0.00018253678,0.0005798653,0.00038308318,0.00016130549],"domain_scores_gemma":[0.9965389,0.0019800668,0.00033126088,0.00023720271,0.0007740572,0.0001386045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002519524,0.0008144951,0.0010622,0.0017967647,0.0008506127,0.002287974,0.00249745,0.0015871698,0.0053964783],"category_scores_gemma":[0.005979735,0.00056437234,0.0017614454,0.0013302896,0.00047899122,0.0022898233,0.0010317345,0.0013631281,0.0015441328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047378524,0.0005812104,0.01915367,0.00023918271,0.0002579671,0.00075290824,0.0007348589,0.8163388,0.0028674442,0.047026914,0.006561339,0.10501197],"study_design_scores_gemma":[0.0000056241875,0.000013933439,0.00038018863,0.0000041713183,0.000010265793,0.000021703523,0.000013532095,0.9958062,0.00022496503,0.002771088,0.0007429598,0.00000528138],"about_ca_topic_score_codex":0.01770626,"about_ca_topic_score_gemma":0.015645705,"teacher_disagreement_score":0.01770626,"about_ca_system_score_codex":0.0022907231,"about_ca_system_score_gemma":0.0014195775,"threshold_uncertainty_score":0.035206437},"labels":[],"label_agreement":null},{"id":"W2945204774","doi":"","title":"Context-specific sentiment lexicon expansion via minimal user interaction","year":2015,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Lexicon; Computer science; Sentiment analysis; Context (archaeology); Natural language processing; Process (computing); Polarity (international relations); Artificial intelligence; Domain (mathematical analysis); Task (project management); Quality (philosophy); Visualization; Information retrieval; Human–computer interaction","score_opus":0.060096619389286954,"score_gpt":0.2901881105373826,"score_spread":0.23009149114809563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2945204774","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07612012,0.0002779526,0.836575,0.0003905741,0.00012437475,0.0013761497,0.0013106815,0.07032673,0.013498447],"genre_scores_gemma":[0.28068078,0.00019696797,0.703511,0.00035017493,0.000058176498,0.002431199,0.0025967187,0.0035182373,0.006656729],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986099,0.0007066766,0.000104816805,0.0002681417,0.00024945408,0.000061051476],"domain_scores_gemma":[0.9946361,0.0037956994,0.0001641575,0.0006462514,0.00063389324,0.00012397204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019013417,0.0017553678,0.0007467919,0.00110898,0.0004091558,0.0013281779,0.0011075964,0.00066557137,0.02087269],"category_scores_gemma":[0.0114194,0.0004740924,0.0006855165,0.0005979104,0.0003189268,0.0021465467,0.0023713063,0.00066662766,0.007386357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016951723,0.00072992773,0.0026041195,0.0015261025,0.00009349269,0.00091992057,0.0051015536,0.002705761,0.22021912,0.0046593146,0.03886321,0.7208823],"study_design_scores_gemma":[0.001012552,0.001451522,0.013368033,0.00062573195,0.0002971929,0.0030666278,0.0036558015,0.47058305,0.20785324,0.03352365,0.2641541,0.00040842048],"about_ca_topic_score_codex":0.000305021,"about_ca_topic_score_gemma":0.00086723786,"teacher_disagreement_score":0.02087269,"about_ca_system_score_codex":0.0001859917,"about_ca_system_score_gemma":0.00034830594,"threshold_uncertainty_score":0.06982607},"labels":[],"label_agreement":null},{"id":"W2945223861","doi":"10.1007/978-3-030-18305-9_3","title":"Measuring Human Emotion in Short Documents to Improve Social Robot and Agent Interactions","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Exploit; Granularity; Robot; Emotion detection; State (computer science); Human–computer interaction; Tone (literature); Empirical research; Social robot; Artificial intelligence; Emotion recognition; Computer security; Mobile robot; Linguistics; Programming language; Robot control","score_opus":0.04286971534234519,"score_gpt":0.29846208032827354,"score_spread":0.25559236498592836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2945223861","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5550006,0.0056140455,0.40029505,0.0011069254,0.001123473,0.0005581459,0.013279033,0.0047697634,0.018252933],"genre_scores_gemma":[0.7855444,0.0014600515,0.18608639,0.00024093445,0.000753928,0.00032308832,0.013113011,0.00035926973,0.012118841],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99923885,0.00020090802,0.000060537222,0.0001572206,0.00027562198,0.00006692385],"domain_scores_gemma":[0.99708647,0.0014368686,0.00026656096,0.00015334513,0.0009507846,0.00010598634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008644838,0.0008349709,0.0006121414,0.0014622152,0.00044245546,0.00115737,0.00034530292,0.00066889386,0.0040970915],"category_scores_gemma":[0.003912948,0.00015988952,0.0003841086,0.0014397646,0.0001913959,0.0013515892,0.00046254953,0.00062085147,0.0033535408],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015050112,0.00062887266,0.02580026,0.0007074723,0.00023480908,0.00021533876,0.00057000713,0.009078838,0.14428009,0.0011107024,0.028153328,0.7877154],"study_design_scores_gemma":[0.00014729191,0.0021700647,0.14902788,0.00021485363,0.0006747262,0.00061924924,0.0022588095,0.6280699,0.15572469,0.010937426,0.049952682,0.00020246129],"about_ca_topic_score_codex":0.0017040133,"about_ca_topic_score_gemma":0.0037454206,"teacher_disagreement_score":0.0040970915,"about_ca_system_score_codex":0.0003778792,"about_ca_system_score_gemma":0.00031174437,"threshold_uncertainty_score":0.013706207},"labels":[],"label_agreement":null},{"id":"W2946742686","doi":"10.18653/v1/n19-1137","title":"Multi-Channel Convolutional Neural Network for Twitter Emotion and Sentiment Recognition","year":2019,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Sentiment analysis; Convolutional neural network; Computer science; Artificial intelligence; Computational linguistics; Natural language processing; Emotion recognition; Volume (thermodynamics); Artificial neural network; Speech recognition","score_opus":0.050450802938799076,"score_gpt":0.269680381761986,"score_spread":0.21922957882318692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946742686","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2746293,0.009926152,0.663725,0.002923108,0.0019873765,0.00030111667,0.009330779,0.014690997,0.02248616],"genre_scores_gemma":[0.8412988,0.0022916603,0.11754855,0.00042154166,0.00047555385,0.0002558621,0.011780342,0.0003358625,0.025591904],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985826,0.0000224505,0.000008826085,0.000035349654,0.000031027404,0.00004416655],"domain_scores_gemma":[0.99980825,0.000043063752,0.00001752872,0.000022885692,0.00009286897,0.000015483978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003818903,0.0010317105,0.0004133689,0.00073421246,0.000371762,0.00045495795,0.00073822064,0.00061474985,0.0032849042],"category_scores_gemma":[0.00079968077,0.00032025154,0.00056855206,0.00060130784,0.00014984899,0.0009442186,0.0006129135,0.0009019021,0.002365477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010107551,0.0005579652,0.0055265143,0.000204743,0.0003185249,0.00025309162,0.00010213362,0.06285096,0.0574176,0.0033333022,0.081068106,0.7873564],"study_design_scores_gemma":[0.000014407295,0.000050836672,0.0016818898,0.000011962534,0.0000474198,0.000022495195,0.000024898416,0.98508227,0.0087637,0.0014875835,0.0027996537,0.000012984514],"about_ca_topic_score_codex":0.012172084,"about_ca_topic_score_gemma":0.024200283,"teacher_disagreement_score":0.012172084,"about_ca_system_score_codex":0.00059046893,"about_ca_system_score_gemma":0.00047816001,"threshold_uncertainty_score":0.024202466},"labels":[],"label_agreement":null},{"id":"W2948595979","doi":"","title":"OutdoorSent","year":2020,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Sentiment analysis; Generalization; Class (philosophy); Artificial intelligence; Context (archaeology); Information retrieval; Machine learning; Data science; Geography","score_opus":0.11348021432568527,"score_gpt":0.1753705201339679,"score_spread":0.06189030580828263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2948595979","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061750215,0.002259046,0.027450128,0.0013142487,0.0016111127,0.0009087426,0.67154366,0.10622315,0.12693971],"genre_scores_gemma":[0.07551117,0.00060224923,0.027316498,0.00055307255,0.00025432275,0.00044150796,0.85062045,0.0038258478,0.040874805],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945253,0.000064810105,0.00003258285,0.00020150906,0.00016053327,0.000088171415],"domain_scores_gemma":[0.99935526,0.00009284274,0.00006710831,0.00024158909,0.00016433,0.00007896733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061981473,0.0015078239,0.0005955209,0.0015696342,0.0005847486,0.0015532766,0.0012091392,0.00092199567,0.048405856],"category_scores_gemma":[0.0020046264,0.00037154814,0.0008201083,0.0014839465,0.000284509,0.0016796435,0.0017952987,0.0007616444,0.04038443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005083358,0.00018042645,0.006636968,0.0009262799,0.00009240662,0.00022874198,0.00024263385,0.0017078302,0.0063941577,0.0024656376,0.8554339,0.12518273],"study_design_scores_gemma":[0.0002114945,0.00030612916,0.021376722,0.00020519797,0.000075532465,0.00075263006,0.00042575726,0.032098815,0.013216586,0.00521303,0.92602956,0.00008854125],"about_ca_topic_score_codex":0.009308935,"about_ca_topic_score_gemma":0.027539626,"teacher_disagreement_score":0.048405856,"about_ca_system_score_codex":0.00060112396,"about_ca_system_score_gemma":0.00057014724,"threshold_uncertainty_score":0.16193372},"labels":[],"label_agreement":null},{"id":"W294906217","doi":"","title":"Helpful or Unhelpful: A Linear Approach for Ranking Product Reviews","year":2010,"lang":"en","type":"article","venue":"Journal of electronic commerce research","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Helpfulness; Computer science; Product (mathematics); Ranking (information retrieval); Purchasing; The Internet; World Wide Web; Information retrieval; Data science; Marketing; Psychology; Business; Mathematics","score_opus":0.16438599543261662,"score_gpt":0.4424971498965129,"score_spread":0.2781111544638963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W294906217","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14598325,0.0030049898,0.83882767,0.00257966,0.00022916605,0.00050518295,0.0010901395,0.0028509935,0.0049288925],"genre_scores_gemma":[0.83800805,0.00055911514,0.15321709,0.00048195472,0.00036469699,0.00037512055,0.0009324821,0.00008390482,0.0059776483],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99555486,0.0021109837,0.0003186109,0.0008864004,0.0008747369,0.0002543111],"domain_scores_gemma":[0.9854107,0.010370919,0.0010597102,0.0003457365,0.0025038656,0.00030908664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004620948,0.0013449263,0.001183477,0.0048562284,0.0008198696,0.0018432023,0.002084465,0.0015727459,0.003282242],"category_scores_gemma":[0.011864028,0.0006334136,0.0010917411,0.0028232906,0.0007771964,0.0017661125,0.00069787743,0.0016402527,0.002007434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010024774,0.0012152335,0.05716355,0.0006788899,0.00078477047,0.0004972955,0.0007006329,0.34945738,0.0053650793,0.008439318,0.014800685,0.55989474],"study_design_scores_gemma":[0.000011813402,0.0001291495,0.0018198371,0.000017853687,0.000036020236,0.0000676634,0.000030509951,0.9954627,0.00033954642,0.0016093262,0.0004526651,0.000022806767],"about_ca_topic_score_codex":0.017719062,"about_ca_topic_score_gemma":0.02127812,"teacher_disagreement_score":0.017719062,"about_ca_system_score_codex":0.0018195248,"about_ca_system_score_gemma":0.001480592,"threshold_uncertainty_score":0.03523183},"labels":[],"label_agreement":null},{"id":"W2949453867","doi":"10.48550/arxiv.1311.1194","title":"Identifying Purpose Behind Electoral Tweets","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Automatic summarization; Computer science; Popularity; Task (project management); Class (philosophy); Baseline (sea); Key (lock); Event (particle physics); Artificial intelligence; Natural language processing; Information retrieval; Data science; Computer security; Political science","score_opus":0.12092842093604589,"score_gpt":0.21265656460099291,"score_spread":0.09172814366494703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949453867","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.953065,0.0006595485,0.015164415,0.0010337753,0.00031452294,0.00017489985,0.014278178,0.0009040211,0.014405706],"genre_scores_gemma":[0.9728707,0.00026970654,0.009498212,0.00014894099,0.00021769528,0.00010097698,0.013209529,0.00007449361,0.0036097227],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992999,0.00019069576,0.00008227999,0.00013905375,0.00016668254,0.000121356345],"domain_scores_gemma":[0.99695694,0.0013757655,0.0006223212,0.00028153608,0.000576912,0.00018655042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000853704,0.00035422438,0.00032726777,0.0028749781,0.00083719485,0.0012104628,0.00024531453,0.0004736082,0.001417323],"category_scores_gemma":[0.0046256897,0.000176963,0.00035643045,0.0015252227,0.00021413935,0.0011910814,0.00079821027,0.00046979083,0.0011597847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010564167,0.0002258406,0.69902015,0.00061587105,0.00015772336,0.0011176622,0.0056323824,0.0017616998,0.042556,0.0046537914,0.045296393,0.19790609],"study_design_scores_gemma":[0.000040122963,0.00015070934,0.8464857,0.00015134885,0.00012031179,0.0010485933,0.0070121256,0.04986611,0.024967253,0.005583372,0.0645027,0.00007162603],"about_ca_topic_score_codex":0.0024772072,"about_ca_topic_score_gemma":0.005333657,"teacher_disagreement_score":0.0028749781,"about_ca_system_score_codex":0.00037139663,"about_ca_system_score_gemma":0.00031615497,"threshold_uncertainty_score":0.0049256086},"labels":[],"label_agreement":null},{"id":"W2949709688","doi":"10.48550/arxiv.1308.6242","title":"NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of\\n Tweets","year":2013,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":419,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Lexicon; Sentiment analysis; Task (project management); Computer science; Variety (cybernetics); Word (group theory); Natural language processing; Artificial intelligence; Term (time); State (computer science); Information retrieval; Linguistics; Engineering","score_opus":0.04688768530605174,"score_gpt":0.18878538298413178,"score_spread":0.14189769767808003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949709688","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0840175,0.009291767,0.61609477,0.005566546,0.0026461135,0.0030845169,0.051448368,0.16363205,0.064218454],"genre_scores_gemma":[0.16314608,0.0031714474,0.6821747,0.0015609198,0.00044693984,0.0012822662,0.09977223,0.007935128,0.040510383],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99509627,0.00085087284,0.00023547106,0.0011261285,0.002215538,0.00047564376],"domain_scores_gemma":[0.9908857,0.0011509275,0.00020351377,0.0010207968,0.006210127,0.00052901503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055430075,0.002712264,0.0015334637,0.0056332853,0.002948772,0.0032708582,0.003218183,0.0015002397,0.012797264],"category_scores_gemma":[0.012350114,0.0010845647,0.0019804442,0.0033106932,0.000995883,0.004178568,0.0030782234,0.0029275184,0.01437589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00071798504,0.00039264237,0.010301736,0.00079672755,0.00034537804,0.0002267117,0.00068178365,0.0065555456,0.033324115,0.0052191205,0.23240776,0.7090305],"study_design_scores_gemma":[0.00034732756,0.00039941902,0.018872058,0.00037658584,0.00034081432,0.0002656811,0.0012538929,0.6107902,0.050606154,0.008643638,0.30781916,0.00028518145],"about_ca_topic_score_codex":0.5547163,"about_ca_topic_score_gemma":0.58175373,"teacher_disagreement_score":0.5547163,"about_ca_system_score_codex":0.006142495,"about_ca_system_score_gemma":0.01342696,"threshold_uncertainty_score":0.89581215},"labels":[],"label_agreement":null},{"id":"W2949998441","doi":"10.48550/arxiv.cs/0212032","title":"Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews","year":2002,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1585,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Phrase; Orientation (vector space); Natural language processing; Word (group theory); Computer science; Artificial intelligence; Linguistics; Mathematics","score_opus":0.15401219006017933,"score_gpt":0.3315217737850789,"score_spread":0.17750958372489958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949998441","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24315341,0.0015092527,0.7381621,0.0009997576,0.00034472856,0.00065758725,0.0014006477,0.003879878,0.009892613],"genre_scores_gemma":[0.7380538,0.00037373556,0.25684786,0.00024247366,0.00032286978,0.00028512103,0.001362897,0.00010970747,0.0024015498],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99837947,0.00044886617,0.0001829009,0.00040624238,0.00045706454,0.0001254079],"domain_scores_gemma":[0.9969236,0.0015921055,0.0004820354,0.00019343731,0.0007345355,0.00007423096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018479467,0.0009526332,0.0009915725,0.0037825906,0.00051459495,0.0012371087,0.0007267687,0.0009406521,0.0012461405],"category_scores_gemma":[0.007626206,0.00025593347,0.00072435103,0.002289648,0.00057256594,0.0010651529,0.0005754678,0.00071300974,0.0010554398],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004887125,0.00024803702,0.037915595,0.000293472,0.00031831852,0.0001654629,0.0004898616,0.016549898,0.014343548,0.003833491,0.008973823,0.91637987],"study_design_scores_gemma":[0.00008389108,0.00022501258,0.038474586,0.00010527598,0.00014901445,0.00052211754,0.0004053469,0.91838956,0.013267498,0.021321885,0.0069669043,0.000088957],"about_ca_topic_score_codex":0.0027512568,"about_ca_topic_score_gemma":0.0031428835,"teacher_disagreement_score":0.0037825906,"about_ca_system_score_codex":0.00069079094,"about_ca_system_score_gemma":0.0008064743,"threshold_uncertainty_score":0.009773016},"labels":[],"label_agreement":null},{"id":"W2950426098","doi":"10.48550/arxiv.1308.6297","title":"Crowdsourcing a Word-Emotion Association Lexicon","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Lexicon; Crowdsourcing; Annotation; Computer science; Polarity (international relations); Word (group theory); Term (time); Natural language processing; Sentiment analysis; Association (psychology); Artificial intelligence; Agreement; Quality (philosophy); Crowds; Linguistics; Psychology; World Wide Web","score_opus":0.06494115368169828,"score_gpt":0.18769835696521403,"score_spread":0.12275720328351575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950426098","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1171333,0.0010872014,0.77822816,0.0044643404,0.0014466125,0.0041120257,0.027029425,0.00884539,0.057653535],"genre_scores_gemma":[0.46656415,0.0006527235,0.46800512,0.0014810839,0.0006007837,0.004737223,0.036265787,0.0017021392,0.019990908],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9921335,0.0031877933,0.00066489016,0.0013883321,0.0023314822,0.00029393163],"domain_scores_gemma":[0.98181915,0.009046316,0.0012329287,0.00269086,0.0046266345,0.00058412255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062701046,0.0014859303,0.0010326181,0.006649706,0.0024598995,0.0030563371,0.001301206,0.0012923817,0.0059871143],"category_scores_gemma":[0.030443989,0.00060503336,0.0013268585,0.00491254,0.001164566,0.0033254453,0.0050926306,0.001836678,0.004599401],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015520026,0.0006762293,0.022255318,0.0028233933,0.0004078,0.0018779008,0.011781807,0.016818829,0.1019956,0.048763297,0.16945247,0.62159526],"study_design_scores_gemma":[0.00043110058,0.00034328605,0.028235987,0.0006876051,0.00039180723,0.0010629202,0.011092956,0.2845552,0.04987663,0.21024147,0.41263434,0.00044673687],"about_ca_topic_score_codex":0.0041129244,"about_ca_topic_score_gemma":0.007562918,"teacher_disagreement_score":0.006649706,"about_ca_system_score_codex":0.0016534722,"about_ca_system_score_gemma":0.0028029946,"threshold_uncertainty_score":0.03315991},"labels":[],"label_agreement":null},{"id":"W2950494993","doi":"","title":"From Once Upon a Time to Happily Ever After: Tracking Emotions in Novels and Fairy Tales","year":2013,"lang":"en","type":"preprint","venue":"NPARC","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Word (group theory); Key (lock); Literature; Tracking (education); Computer science; Range (aeronautics); Art; History; Psychology; Linguistics; Philosophy","score_opus":0.02223789673763302,"score_gpt":0.2581035923446005,"score_spread":0.2358656956069675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950494993","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9797442,0.0010180456,0.007647932,0.00035505422,0.00006746782,0.000058690617,0.0037500306,0.00050795474,0.006850582],"genre_scores_gemma":[0.98021245,0.00038092464,0.012434797,0.00006350104,0.00007487398,0.000048611193,0.0033879173,0.00009106328,0.0033058655],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99980146,0.00004533143,0.000011382401,0.00006438467,0.00005017406,0.000027241522],"domain_scores_gemma":[0.99911016,0.00035931153,0.00019776379,0.00007290346,0.00016082836,0.00009911505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035136574,0.00020716504,0.00019957378,0.0021441805,0.00062250573,0.0013559937,0.00021112004,0.00030610646,0.0017431115],"category_scores_gemma":[0.0025304398,0.000114999144,0.00015679807,0.002013971,0.0003365481,0.0014745046,0.00082334527,0.0004229465,0.0008749211],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012836431,0.00039531576,0.41857162,0.0009529436,0.00030282204,0.002131288,0.024523722,0.0034209557,0.05756908,0.0049903137,0.05653393,0.42932436],"study_design_scores_gemma":[0.000019976082,0.00014417249,0.89243084,0.000106403895,0.00008181956,0.0010658274,0.0151455095,0.029427601,0.0113190785,0.0049920734,0.045187633,0.00007898418],"about_ca_topic_score_codex":0.0032035352,"about_ca_topic_score_gemma":0.0077267145,"teacher_disagreement_score":0.0032035352,"about_ca_system_score_codex":0.00032378233,"about_ca_system_score_gemma":0.00013168827,"threshold_uncertainty_score":0.0063697696},"labels":[],"label_agreement":null},{"id":"W2950676070","doi":"10.48550/arxiv.1906.03677","title":"Happy Together: Learning and Understanding Appraisal From Natural\\n Language","year":2019,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Embedding; Computer science; Sociality; Artificial intelligence; Agency (philosophy); Task (project management); Machine learning; Natural language processing; Focus (optics); Artificial neural network; Machine translation; Cognitive psychology; Psychology; Sociology; Engineering","score_opus":0.0777231234466074,"score_gpt":0.2242216538109689,"score_spread":0.1464985303643615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950676070","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5875684,0.0041510435,0.354645,0.0030612282,0.0009925779,0.0004249567,0.005946801,0.004286624,0.038923286],"genre_scores_gemma":[0.91612214,0.00067194994,0.0657807,0.0004485048,0.00031516826,0.00019081734,0.00651357,0.00013929879,0.009817883],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995963,0.00014788142,0.000020633182,0.00011654253,0.000071198156,0.000047522917],"domain_scores_gemma":[0.999241,0.0003312973,0.00015488386,0.000084570005,0.0001255888,0.00006275486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008447092,0.0010435744,0.0002672032,0.000780835,0.00027223583,0.0012003937,0.00070285867,0.0006269971,0.0033969756],"category_scores_gemma":[0.0036782117,0.0002020884,0.00057730847,0.0005695908,0.00028444742,0.0023594315,0.00081298494,0.0011886231,0.0017388089],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001087409,0.00065502775,0.06391911,0.0005212122,0.0003819018,0.00071334094,0.0029046882,0.013285346,0.0271375,0.013790185,0.058378786,0.8172254],"study_design_scores_gemma":[0.00011266786,0.0007508544,0.09723998,0.00021901682,0.00023633195,0.0005490195,0.0043797977,0.72467303,0.018791284,0.07136055,0.08151239,0.00017512258],"about_ca_topic_score_codex":0.001761193,"about_ca_topic_score_gemma":0.0045916075,"teacher_disagreement_score":0.0033969756,"about_ca_system_score_codex":0.000504044,"about_ca_system_score_gemma":0.00024464625,"threshold_uncertainty_score":0.011364043},"labels":[],"label_agreement":null},{"id":"W2950680175","doi":"10.1109/ms.2019.2923408","title":"Can a Machine Learn Through Customer Sentiment?: A Cost-Aware Approach to Predict Support Ticket Escalations","year":2019,"lang":"en","type":"article","venue":"IEEE Software","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Ticket; Computer science; Happiness; Sentiment analysis; Mechanism (biology); Customer retention; Artificial intelligence; Machine learning; Knowledge management; Marketing; Business; Computer security; Psychology; Service (business)","score_opus":0.023024918169823754,"score_gpt":0.2671272912724126,"score_spread":0.24410237310258884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950680175","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5727232,0.0012140065,0.40417987,0.007772783,0.00035865518,0.00030416224,0.0014924781,0.0018858794,0.010068883],"genre_scores_gemma":[0.94935036,0.0001264265,0.04848126,0.0003162241,0.00015781369,0.000079364836,0.00042179643,0.000036697733,0.001030105],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993255,0.00022731864,0.000051173796,0.00012621192,0.00018018762,0.00008952753],"domain_scores_gemma":[0.9982659,0.00072828564,0.00021744531,0.00018648778,0.0005250664,0.0000769337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015954671,0.0008059711,0.0008434895,0.0009584799,0.0003517688,0.001174132,0.0010421745,0.001254742,0.0019457947],"category_scores_gemma":[0.006354378,0.0002492765,0.0004946598,0.0007580106,0.00025810828,0.0018693766,0.0005320746,0.0009862798,0.0005980452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001399992,0.0015667748,0.07563852,0.00018437146,0.0005743643,0.0002541829,0.00015536342,0.25772002,0.011126129,0.00750442,0.015378624,0.6284972],"study_design_scores_gemma":[0.000020634869,0.00009568065,0.0037289204,0.000009532966,0.000034733333,0.000025101675,0.00002878786,0.9903482,0.0011957737,0.003938416,0.00056234474,0.000011860416],"about_ca_topic_score_codex":0.0028214965,"about_ca_topic_score_gemma":0.0029965988,"teacher_disagreement_score":0.0028214965,"about_ca_system_score_codex":0.00069121044,"about_ca_system_score_gemma":0.00042880012,"threshold_uncertainty_score":0.008437753},"labels":[],"label_agreement":null},{"id":"W2951934610","doi":"10.48550/arxiv.1309.6352","title":"Using Nuances of Emotion to Identify Personality","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Affect (linguistics); Admiration; Psychology; Personality; Big Five personality traits; Emotion recognition; Cognitive psychology; Social psychology; Communication","score_opus":0.20456984687117996,"score_gpt":0.26791430755361517,"score_spread":0.06334446068243521,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951934610","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.934681,0.0010076499,0.05224396,0.0003308762,0.00010066111,0.000085973326,0.0011876059,0.00032779123,0.010034463],"genre_scores_gemma":[0.986668,0.00020345871,0.01133274,0.00003788997,0.000052541644,0.00002648039,0.00055213907,0.000016017633,0.0011106684],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995154,0.0001525178,0.000042145195,0.000119840755,0.00012236685,0.000047729718],"domain_scores_gemma":[0.99679774,0.0015285898,0.0006857511,0.00022933198,0.00056623534,0.00019235098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093566085,0.000511607,0.00036902368,0.0020779606,0.00032845334,0.0011045787,0.00016348356,0.00037291492,0.0015654169],"category_scores_gemma":[0.0047290022,0.0001182277,0.00033057822,0.0008845255,0.00023385827,0.00094190356,0.00051133294,0.00053878926,0.0008957825],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005273474,0.00024192999,0.55528927,0.00030464196,0.00025826448,0.00025608524,0.0016737974,0.0024779052,0.053732287,0.0014008787,0.004349388,0.37948832],"study_design_scores_gemma":[0.00001770491,0.00035508527,0.87807685,0.00009301948,0.00013968286,0.0007366117,0.0017349995,0.09420485,0.011582756,0.0060949293,0.0068908874,0.000072671784],"about_ca_topic_score_codex":0.00084117986,"about_ca_topic_score_gemma":0.0013870371,"teacher_disagreement_score":0.0020779606,"about_ca_system_score_codex":0.00018362884,"about_ca_system_score_gemma":0.00011275318,"threshold_uncertainty_score":0.005236864},"labels":[],"label_agreement":null},{"id":"W2953739332","doi":"10.18653/v1/s19-2006","title":"ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"SemEval; Computer science; Utterance; Task (project management); Artificial intelligence; Emotion detection; Natural language processing; Emotion recognition","score_opus":0.02290316999301337,"score_gpt":0.27094575396750453,"score_spread":0.24804258397449117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953739332","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37170294,0.005486789,0.31587392,0.0036460352,0.005545479,0.0018721487,0.054931473,0.17740904,0.06353222],"genre_scores_gemma":[0.591531,0.00060752034,0.25573072,0.0012642313,0.0007626083,0.0015350365,0.08978315,0.0036345182,0.05515122],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988034,0.00037843414,0.00004774619,0.00041710451,0.00018984472,0.00016351847],"domain_scores_gemma":[0.999049,0.0002740728,0.000053505326,0.00022163196,0.00027467543,0.0001272195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001730704,0.0020612502,0.0011512641,0.0006053699,0.00082766026,0.0017023238,0.001349183,0.0021078726,0.0130541995],"category_scores_gemma":[0.0032882362,0.00051849644,0.0009072587,0.0003946817,0.0003036984,0.0021847612,0.0019968618,0.0019636163,0.01155973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033827552,0.0008936753,0.004216646,0.0010271553,0.000440994,0.0007388056,0.00061392825,0.014682141,0.11259662,0.0043429243,0.35254896,0.50451535],"study_design_scores_gemma":[0.0008220412,0.0019470827,0.018526228,0.00020273226,0.0003720512,0.0011651793,0.0009099114,0.63593376,0.11101319,0.012423909,0.2163672,0.00031671659],"about_ca_topic_score_codex":0.004603572,"about_ca_topic_score_gemma":0.0087336935,"teacher_disagreement_score":0.0130541995,"about_ca_system_score_codex":0.00066772767,"about_ca_system_score_gemma":0.00086204446,"threshold_uncertainty_score":0.043670595},"labels":[],"label_agreement":null},{"id":"W2954935485","doi":"10.1002/spy2.69","title":"Discerning cyber threatening incidents from ordinary events using sentiment analysis and logistic regression","year":2019,"lang":"en","type":"article","venue":"Security and Privacy","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Sentence; Set (abstract data type); Logistic regression; Event (particle physics); Precision and recall; Sentiment analysis; Natural language processing; Artificial intelligence; Recall; Data mining; Machine learning; Psychology","score_opus":0.032267403223365076,"score_gpt":0.3019247693715464,"score_spread":0.2696573661481813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954935485","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8387799,0.00034885455,0.1489192,0.0008709474,0.00014367557,0.00040168923,0.0032472273,0.0017251782,0.005563224],"genre_scores_gemma":[0.9281519,0.00017478257,0.06510677,0.000090285204,0.00008397476,0.00012750858,0.005054459,0.00006162303,0.0011486552],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983278,0.0005656283,0.00026504576,0.00030719233,0.00037177966,0.0001626003],"domain_scores_gemma":[0.9944563,0.0032243447,0.0007974068,0.0002535498,0.0011416663,0.00012675766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002624049,0.0011517577,0.00055765756,0.002743794,0.00045705488,0.0014778951,0.0005871749,0.0005808693,0.0013014562],"category_scores_gemma":[0.008467626,0.0002843783,0.0010747028,0.0011823052,0.00026067384,0.0013676155,0.000659257,0.0011749235,0.0011134227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010764734,0.0014122683,0.3432322,0.00061943626,0.00063505175,0.001026219,0.0012590309,0.0785199,0.033694427,0.0021077702,0.01903911,0.51737815],"study_design_scores_gemma":[0.00002930693,0.0001985026,0.06099537,0.000056150275,0.00007909182,0.00016378738,0.0006201583,0.9250689,0.008631561,0.0017249997,0.0023958983,0.000036302124],"about_ca_topic_score_codex":0.004068192,"about_ca_topic_score_gemma":0.0045424015,"teacher_disagreement_score":0.004068192,"about_ca_system_score_codex":0.00064917054,"about_ca_system_score_gemma":0.00054718007,"threshold_uncertainty_score":0.013877451},"labels":[],"label_agreement":null},{"id":"W2959013365","doi":"10.18280/isi.240119","title":"Sentiment Analysis from Movie Reviews Using LSTMs","year":2019,"lang":"fr","type":"article","venue":"Ingénierie des systèmes d information","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sentiment analysis; Computer science; Natural language processing; Artificial intelligence; Information retrieval","score_opus":0.040268203608810775,"score_gpt":0.27616554890659123,"score_spread":0.23589734529778045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2959013365","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5011044,0.0031851584,0.47482616,0.0012119159,0.0009166155,0.00025141408,0.0033495065,0.005262322,0.009892534],"genre_scores_gemma":[0.91361576,0.0007863674,0.07570034,0.00020646215,0.000322181,0.00012195938,0.0034915365,0.00013612547,0.0056192204],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997496,0.000052604268,0.000020774374,0.00005430486,0.00008355785,0.000039193223],"domain_scores_gemma":[0.99945813,0.00014271385,0.000080621794,0.000024925212,0.00027245094,0.000021110141],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046215905,0.0007636451,0.00044885097,0.00078390964,0.00016159187,0.00042531354,0.00034819654,0.00046968646,0.001738813],"category_scores_gemma":[0.0020014485,0.00022022451,0.0005251657,0.00066331634,0.000113352995,0.0004927365,0.00026773897,0.00060689624,0.0011986061],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00094923377,0.00028449786,0.010158446,0.000434685,0.00031837405,0.00085695216,0.0003342914,0.11419754,0.12988614,0.001553204,0.018516634,0.72251004],"study_design_scores_gemma":[0.000016302361,0.00012343001,0.0055752876,0.000020672422,0.000036561185,0.000077745586,0.00006445594,0.97934693,0.011036481,0.0010970021,0.002587592,0.000017493434],"about_ca_topic_score_codex":0.003279543,"about_ca_topic_score_gemma":0.0041321055,"teacher_disagreement_score":0.003279543,"about_ca_system_score_codex":0.00036992237,"about_ca_system_score_gemma":0.0002896763,"threshold_uncertainty_score":0.0065208673},"labels":[],"label_agreement":null},{"id":"W2960505694","doi":"10.5120/ijca2019919167","title":"Twitter Texts’ Quality Classification using Data Mining and Neural Networks","year":2019,"lang":"en","type":"article","venue":"International Journal of Computer Applications","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Quality (philosophy); Artificial neural network; Artificial intelligence; Data mining; Data science; Information retrieval","score_opus":0.11067498705919068,"score_gpt":0.3798573457806176,"score_spread":0.26918235872142693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2960505694","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85095215,0.0011464496,0.13218214,0.002384403,0.00023241434,0.0007146424,0.0047483454,0.0012132582,0.0064262175],"genre_scores_gemma":[0.9553284,0.000300987,0.03804277,0.00011296307,0.00015034268,0.00022084679,0.0042593093,0.00002773252,0.001556535],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984944,0.0003031927,0.00024339669,0.0002665583,0.0005665758,0.00012586701],"domain_scores_gemma":[0.995033,0.0018448276,0.0011439474,0.00026375518,0.0015689238,0.0001454558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017555108,0.00075251266,0.00057434366,0.0047763353,0.00054460403,0.0016678657,0.00079529063,0.0009880498,0.0008127057],"category_scores_gemma":[0.008617682,0.00019785154,0.0007652624,0.0022898058,0.00036186198,0.0018454513,0.00084611773,0.00081667653,0.0006052381],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016259984,0.0016573058,0.37447208,0.0007924231,0.00053644826,0.00049666944,0.0006611342,0.075406194,0.011869508,0.002456906,0.009596058,0.52042925],"study_design_scores_gemma":[0.000031540014,0.00023489578,0.0652232,0.000107721324,0.00010539379,0.000112699076,0.0006463116,0.91863275,0.009245048,0.0029205836,0.002701035,0.000038755385],"about_ca_topic_score_codex":0.0044209505,"about_ca_topic_score_gemma":0.0044289664,"teacher_disagreement_score":0.0047763353,"about_ca_system_score_codex":0.001336935,"about_ca_system_score_gemma":0.0005059097,"threshold_uncertainty_score":0.009700179},"labels":[],"label_agreement":null},{"id":"W2961279248","doi":"10.1088/1742-6596/1267/1/012013","title":"Data Mining Algorithms for a Feature-Based Customer Review Process Model with Engineering Informatics Approach","year":2019,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Sentiment analysis; C4.5 algorithm; Cluster analysis; Analytics; Data mining; Social media; Process (computing); Statistical classification; Feature engineering; Information retrieval; Data science; Artificial intelligence; Machine learning; World Wide Web; Support vector machine; Deep learning","score_opus":0.05926809707881587,"score_gpt":0.2911194216394228,"score_spread":0.23185132456060692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2961279248","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012626686,0.00031174812,0.98381203,0.0005337119,0.000027896314,0.00026487108,0.00037806883,0.00044399415,0.0016009213],"genre_scores_gemma":[0.45042837,0.00068998453,0.5400261,0.00022824081,0.00011597421,0.0017708883,0.0014168139,0.00008706187,0.0052366136],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983853,0.0005090693,0.00017383225,0.00043817254,0.00036086957,0.00013271726],"domain_scores_gemma":[0.99583805,0.0028113686,0.00034374316,0.00013448522,0.0008042111,0.00006819677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029298686,0.00081332825,0.0016070725,0.0018723613,0.0007500016,0.001997586,0.002421758,0.0015211658,0.003758021],"category_scores_gemma":[0.008574793,0.0005669581,0.0016936492,0.0020678618,0.00047973965,0.0017753104,0.0010041451,0.0016212156,0.0011447585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019352176,0.00021104944,0.005184708,0.00024958912,0.00019917775,0.0002995898,0.0003545868,0.75330204,0.0011103143,0.052567337,0.004071832,0.18225624],"study_design_scores_gemma":[0.0000061095793,0.000015450963,0.000213447,0.0000074421923,0.000009415963,0.000021289128,0.000013697636,0.99232095,0.00010238946,0.006693734,0.00059121393,0.0000048603647],"about_ca_topic_score_codex":0.011450993,"about_ca_topic_score_gemma":0.0069414545,"teacher_disagreement_score":0.011450993,"about_ca_system_score_codex":0.002114676,"about_ca_system_score_gemma":0.0018445547,"threshold_uncertainty_score":0.022768736},"labels":[],"label_agreement":null},{"id":"W2963223838","doi":"","title":"WASSA-2017 shared task on emotion intensity","year":2019,"lang":"en","type":"article","venue":"Research Commons (University of Waikato)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":264,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Sadness; Computer science; Task (project management); Anger; Emotion classification; Artificial intelligence; Natural language processing; Psychology; Social psychology","score_opus":0.08052660557195573,"score_gpt":0.30632753208597147,"score_spread":0.22580092651401573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963223838","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22268477,0.0042934506,0.10851821,0.007406153,0.011034717,0.005498421,0.48618338,0.08375158,0.07062936],"genre_scores_gemma":[0.17883891,0.0004714559,0.08698969,0.001310112,0.0011077002,0.004299963,0.6988781,0.004957399,0.023146683],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.989509,0.0030934466,0.0012013667,0.0022801175,0.0027103648,0.001205687],"domain_scores_gemma":[0.98387724,0.0038933768,0.00074575463,0.0042680935,0.004996216,0.002219355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00806587,0.0044460697,0.0028591934,0.0035195197,0.0028940635,0.003927169,0.002971551,0.00321731,0.015239888],"category_scores_gemma":[0.020076666,0.000792961,0.002471271,0.0026778649,0.0012431601,0.005179639,0.008976122,0.004054253,0.02124421],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023457478,0.0010957511,0.0078095677,0.0018647647,0.00036261362,0.0004636546,0.0014123078,0.0035684397,0.024842868,0.0021767549,0.8283376,0.12571995],"study_design_scores_gemma":[0.0014411223,0.001959324,0.047513697,0.0006646079,0.00044863793,0.0012006455,0.004381308,0.08970591,0.065031625,0.016901916,0.77012146,0.0006297081],"about_ca_topic_score_codex":0.009749731,"about_ca_topic_score_gemma":0.013924131,"teacher_disagreement_score":0.015239888,"about_ca_system_score_codex":0.0027492754,"about_ca_system_score_gemma":0.0032800308,"threshold_uncertainty_score":0.050982535},"labels":[],"label_agreement":null},{"id":"W2963881255","doi":"","title":"Cross-Lingual Sentiment Analysis Without (Good) Translation","year":2017,"lang":"en","type":"article","venue":"International Joint Conference on Natural Language Processing","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Leverage (statistics); Natural language processing; Sentiment analysis; Artificial intelligence; Machine translation; Word (group theory); Translation (biology); Set (abstract data type); Context (archaeology); Linguistics","score_opus":0.04957342895840701,"score_gpt":0.3699171184644701,"score_spread":0.3203436895060631,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963881255","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18099836,0.000747651,0.77986866,0.0007913908,0.00063475734,0.0003283457,0.005655038,0.0061704563,0.024805298],"genre_scores_gemma":[0.72007424,0.00049929257,0.2536284,0.00046107054,0.0002077219,0.0005860943,0.011376349,0.0012152259,0.011951522],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847835,0.00043365004,0.00016341679,0.00041298597,0.00035054077,0.00016109357],"domain_scores_gemma":[0.9976634,0.00041873843,0.0001923145,0.0007033627,0.00096773915,0.00005452487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018647522,0.0011397488,0.0008205719,0.0016382508,0.0008775954,0.0016872124,0.0005685781,0.0004754127,0.0061437986],"category_scores_gemma":[0.0053933416,0.00036613064,0.0010884652,0.0019123617,0.00045104226,0.0020757301,0.002089122,0.0009883973,0.0062434273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058344664,0.00040987125,0.021754589,0.0005310194,0.00053780206,0.00060224195,0.0016936586,0.008713324,0.106213465,0.011512832,0.03438369,0.81306404],"study_design_scores_gemma":[0.00014391597,0.0006152017,0.070653014,0.00019168777,0.0006793252,0.001210071,0.0056291698,0.52270615,0.16488086,0.07429024,0.15873803,0.0002623136],"about_ca_topic_score_codex":0.0022019176,"about_ca_topic_score_gemma":0.003603062,"teacher_disagreement_score":0.0061437986,"about_ca_system_score_codex":0.0004742036,"about_ca_system_score_gemma":0.0009861069,"threshold_uncertainty_score":0.020553112},"labels":[],"label_agreement":null},{"id":"W2964321678","doi":"10.3115/v1/w14-2614","title":"Credibility Adjusted Term Frequency: A Supervised Term Weighting Scheme for Sentiment Analysis and Text Classification","year":2014,"lang":"en","type":"preprint","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Weighting; Term (time); Computer science; Credibility; Scheme (mathematics); Baseline (sea); Sentiment analysis; Artificial intelligence; Data mining; Pattern recognition (psychology); Machine learning; Information retrieval; Mathematics","score_opus":0.053711215892052085,"score_gpt":0.3002124657119668,"score_spread":0.24650124981991473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964321678","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011492778,0.0004684724,0.9835791,0.00017439632,0.00020172492,0.00018258355,0.000444833,0.0017759998,0.0016800889],"genre_scores_gemma":[0.15174732,0.00048522055,0.839024,0.00018721554,0.00055351865,0.0005298169,0.0017025487,0.0007258273,0.0050446037],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969531,0.00067125465,0.00027548394,0.00048250868,0.0014546246,0.00016289072],"domain_scores_gemma":[0.99256706,0.0021809833,0.0008775087,0.00124241,0.0028892688,0.00024281914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003675132,0.0009838627,0.00095236953,0.0052206973,0.0009330586,0.0014908406,0.0015198484,0.0014311218,0.0035628139],"category_scores_gemma":[0.021131944,0.00031325128,0.00092179765,0.004176076,0.0006291572,0.0028668805,0.0014121487,0.0019802256,0.0036345168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000391485,0.00023994526,0.0032320193,0.00033321127,0.0001456918,0.00010840946,0.00033133043,0.010825996,0.05818568,0.0091131395,0.014633183,0.90246],"study_design_scores_gemma":[0.00013965888,0.0003491009,0.006800825,0.000170229,0.00019235318,0.00064960326,0.0002345017,0.835426,0.06842414,0.044547416,0.042842284,0.00022396636],"about_ca_topic_score_codex":0.002645427,"about_ca_topic_score_gemma":0.0041306773,"teacher_disagreement_score":0.0052206973,"about_ca_system_score_codex":0.0007493429,"about_ca_system_score_gemma":0.0013884023,"threshold_uncertainty_score":0.01943618},"labels":[],"label_agreement":null},{"id":"W2964325543","doi":"10.18653/v1/d16-1057","title":"Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora","year":2016,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":359,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Army Research Office; National Institute of Biomedical Imaging and Bioengineering; Multidisciplinary University Research Initiative; Natural Sciences and Engineering Research Council of Canada; Defense Advanced Research Projects Agency; National Institutes of Health; National Science Foundation","keywords":"WordNet; Computer science; Sentiment analysis; Domain (mathematical analysis); Artificial intelligence; Natural language processing; Social media; Word (group theory); Linguistics; World Wide Web; Mathematics","score_opus":0.028154327802067593,"score_gpt":0.23625221304698463,"score_spread":0.20809788524491704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964325543","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4129568,0.00089849334,0.5284621,0.00086067844,0.00041068776,0.0012616111,0.019307682,0.0076047527,0.028237132],"genre_scores_gemma":[0.5671452,0.00071853213,0.35703123,0.00043070788,0.00017505171,0.0017794368,0.0657922,0.0009348186,0.005992766],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988721,0.00042932626,0.000100835874,0.00032267728,0.00020507447,0.000069958835],"domain_scores_gemma":[0.995593,0.0021715953,0.0004097875,0.00063592364,0.001051184,0.00013849263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018884705,0.0011811575,0.00050970766,0.0029477165,0.0007523775,0.0011096711,0.000711307,0.0007095165,0.0019909523],"category_scores_gemma":[0.0078118145,0.00062429084,0.00083792914,0.0018714464,0.0007296243,0.0020273053,0.0015675253,0.001006239,0.001897544],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006597337,0.0013287506,0.04461469,0.0019650203,0.00045202344,0.0012194709,0.0022650652,0.063061915,0.14767635,0.020394783,0.08751326,0.6288489],"study_design_scores_gemma":[0.00030268525,0.0005824793,0.030971514,0.00029852174,0.0002580094,0.0009298065,0.0017402021,0.7542147,0.0761065,0.039123178,0.09528698,0.00018550485],"about_ca_topic_score_codex":0.0021714196,"about_ca_topic_score_gemma":0.0077149007,"teacher_disagreement_score":0.0029477165,"about_ca_system_score_codex":0.0007167247,"about_ca_system_score_gemma":0.001132959,"threshold_uncertainty_score":0.009987295},"labels":[],"label_agreement":null},{"id":"W2966411251","doi":"10.48550/arxiv.1908.00648","title":"Contrastive Reasons Detection and Clustering from Online Polarized Debate","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Cluster analysis; Computer science; Psychology; Artificial intelligence","score_opus":0.053931743089563326,"score_gpt":0.19452697342739583,"score_spread":0.1405952303378325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966411251","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26718193,0.0024961845,0.7080767,0.0009678227,0.00026574076,0.00051986764,0.0057088374,0.0044012293,0.010381698],"genre_scores_gemma":[0.6433682,0.00071526796,0.33287498,0.00015612783,0.00041059626,0.00039201378,0.017050192,0.00040563347,0.004626994],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99819356,0.00050820474,0.00014396853,0.00040577358,0.0005578621,0.00019063476],"domain_scores_gemma":[0.9945766,0.0027652532,0.00062052876,0.00057986495,0.001285275,0.0001724726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017194476,0.0008699276,0.00074153626,0.0057449467,0.0009542151,0.002290149,0.00094847573,0.0011890325,0.002134302],"category_scores_gemma":[0.009539979,0.00028588294,0.0010386964,0.0031558778,0.0005369075,0.0023617877,0.0019194941,0.0015894776,0.0023350047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001273002,0.00031639088,0.020822683,0.0012854652,0.00031531224,0.00072314864,0.006100057,0.0069322144,0.11896685,0.015979651,0.018890588,0.8083947],"study_design_scores_gemma":[0.00016873174,0.0006717554,0.08806604,0.0004211406,0.00064178987,0.0014943358,0.01162455,0.5351518,0.16073799,0.09077576,0.109944455,0.00030173376],"about_ca_topic_score_codex":0.0010347398,"about_ca_topic_score_gemma":0.0020598148,"teacher_disagreement_score":0.0057449467,"about_ca_system_score_codex":0.0006105076,"about_ca_system_score_gemma":0.0009315077,"threshold_uncertainty_score":0.009093404},"labels":[],"label_agreement":null},{"id":"W2968820502","doi":"10.1109/cec.2019.8790020","title":"Evaluation and Validation of Semi-Supervised Ant-inspired Sentence-Level Sentiment Prediction Clustering","year":2019,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Research Manitoba","keywords":"Computer science; Cluster analysis; Artificial intelligence; Machine learning; Sentiment analysis; Sentence; Lexicon; Supervised learning; Class (philosophy); Data mining; Artificial neural network","score_opus":0.05207019802997932,"score_gpt":0.28157601066787025,"score_spread":0.22950581263789094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968820502","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75432783,0.001328468,0.22936848,0.0005154117,0.00032496676,0.00081992813,0.001144828,0.0059671807,0.006202942],"genre_scores_gemma":[0.85043454,0.00018812371,0.14319402,0.00015451631,0.000055498218,0.0002666385,0.0037495776,0.00020720823,0.0017499276],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9953365,0.0024071063,0.0002831979,0.00060916896,0.0012066298,0.00015750978],"domain_scores_gemma":[0.9874472,0.005104111,0.0006180072,0.0012256228,0.0052632387,0.00034177137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055849045,0.001182807,0.0011046613,0.0015254718,0.0007646326,0.0008863659,0.0021581221,0.0016232182,0.0009774044],"category_scores_gemma":[0.013030445,0.00028950485,0.0005856496,0.0010559391,0.00075307785,0.0010202188,0.0010534278,0.0007588233,0.00090355397],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019228085,0.0019215528,0.017931039,0.0009437702,0.00082317967,0.00023915502,0.00042068833,0.5395372,0.03986879,0.0019363771,0.011540785,0.38291466],"study_design_scores_gemma":[0.000037097463,0.00018850288,0.0021606572,0.000009667389,0.000018559464,0.00003591857,0.00004098432,0.99063206,0.006299374,0.00020146159,0.00036555168,0.000010151433],"about_ca_topic_score_codex":0.005936447,"about_ca_topic_score_gemma":0.006291485,"teacher_disagreement_score":0.005936447,"about_ca_system_score_codex":0.001163439,"about_ca_system_score_gemma":0.0009904815,"threshold_uncertainty_score":0.029536188},"labels":[],"label_agreement":null},{"id":"W2969181490","doi":"10.1109/jcdl.2019.00096","title":"A Sentiment Augmented Deep Architecture to Predict Peer Review Outcomes","year":2019,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Electronics and Information technology; Institute for Catastrophic Loss Reduction","keywords":"Computer science; Sentiment analysis; Artifact (error); Architecture; Data science; Inclusion (mineral); Polarity (international relations); Artificial intelligence; Information retrieval; World Wide Web; Psychology","score_opus":0.015143298510374686,"score_gpt":0.28238271140203935,"score_spread":0.26723941289166464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969181490","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69006056,0.0068525407,0.27279028,0.0037623062,0.0013054515,0.00029414173,0.0029949415,0.006156014,0.015783714],"genre_scores_gemma":[0.9365165,0.0007957998,0.043364424,0.0004453869,0.00041692532,0.00013035802,0.0029333676,0.00007915878,0.01531796],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966764,0.000082220206,0.00002134334,0.000080711776,0.000094689036,0.000053389573],"domain_scores_gemma":[0.9984939,0.0003286147,0.00017128713,0.00008895163,0.00081330584,0.00010395076],"candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0009016168,0.00078289176,0.00042509465,0.0007171326,0.0002445229,0.0006835001,0.0007743864,0.00085517153,0.0015418328],"category_scores_gemma":[0.0030236233,0.00023054254,0.00031947423,0.00042534416,0.00019800325,0.0006885257,0.0005179091,0.0010410611,0.0011691176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010893942,0.0011041292,0.029066177,0.0004247544,0.00054056366,0.00036441063,0.00032441536,0.1353704,0.04486549,0.0026928703,0.048413888,0.7357435],"study_design_scores_gemma":[0.000030540472,0.0001634557,0.0035541,0.000025655849,0.00007463683,0.000037643997,0.000026706093,0.984934,0.006675082,0.0014111788,0.0030516656,0.000015302621],"about_ca_topic_score_codex":0.005205959,"about_ca_topic_score_gemma":0.010665792,"teacher_disagreement_score":0.9992829,"about_ca_system_score_codex":0.00073270197,"about_ca_system_score_gemma":0.0011405102,"threshold_uncertainty_score":0.0103513},"labels":[],"label_agreement":null},{"id":"W2970431814","doi":"10.18653/v1/d19-1016","title":"Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations","year":2019,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":294,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Research (Canada)","funders":"Nanyang Technological University; National Research Foundation","keywords":"Transformer; Computer science; Emotion detection; Natural language processing; Artificial intelligence; Emotion recognition; Electrical engineering; Engineering; Voltage","score_opus":0.01972994354918485,"score_gpt":0.27372412148492614,"score_spread":0.2539941779357413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2970431814","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07070296,0.001136553,0.8934638,0.00042799755,0.0002440732,0.0003372086,0.005669303,0.019926384,0.008091799],"genre_scores_gemma":[0.70850194,0.0006064005,0.270462,0.00023754449,0.00013693402,0.00033889574,0.0122208,0.00048646875,0.007009086],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999302,0.0001806133,0.000049359598,0.00024670293,0.00012651925,0.00009484353],"domain_scores_gemma":[0.999178,0.00034567976,0.00006338369,0.00012823105,0.00023005395,0.000054666318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006905859,0.00071551156,0.0005842981,0.0020844818,0.0004754281,0.00090382824,0.00086223916,0.00057584257,0.0057073953],"category_scores_gemma":[0.002359912,0.00023692513,0.00069071754,0.001469075,0.00028533154,0.0021833377,0.0014477824,0.0006201007,0.0036593443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015408193,0.00045032473,0.004777148,0.00050508225,0.00016267785,0.0006983275,0.00072143786,0.0059160325,0.079617,0.008966409,0.022979625,0.873665],"study_design_scores_gemma":[0.00007790632,0.00032394865,0.007826638,0.00008023405,0.00028142502,0.0008278265,0.0010148984,0.86154884,0.07168033,0.03231062,0.023946041,0.00008127643],"about_ca_topic_score_codex":0.0029180741,"about_ca_topic_score_gemma":0.00388013,"teacher_disagreement_score":0.0057073953,"about_ca_system_score_codex":0.0004955723,"about_ca_system_score_gemma":0.00062151015,"threshold_uncertainty_score":0.019093096},"labels":[],"label_agreement":null},{"id":"W2970748008","doi":"10.18653/v1/d19-1569","title":"Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree","year":2019,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":427,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Key Research and Development Program of China; Beijing Advanced Innovation Center for Big Data and Brain Computing; Fundamental Research Funds for the Central Universities; State Key Laboratory of Software Development Environment; National Natural Science Foundation of China","keywords":"Dependency (UML); Computer science; Convolution (computer science); Natural language processing; Tree (set theory); Zhàng; Artificial intelligence; Joint (building); Sentiment analysis; China; Mathematics; Engineering; Geography; Archaeology; Artificial neural network; Combinatorics; Architectural engineering","score_opus":0.0186012261346731,"score_gpt":0.25387710188286405,"score_spread":0.23527587574819095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2970748008","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056999806,0.0006325947,0.93278956,0.00022152658,0.00012613011,0.00007458921,0.0008375972,0.0044374284,0.0038806726],"genre_scores_gemma":[0.6314942,0.0007021733,0.35787246,0.00016244338,0.00013804455,0.00013114794,0.0036834376,0.0005356771,0.0052804532],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996977,0.0000657165,0.000021014273,0.000064348096,0.000095464966,0.000055793083],"domain_scores_gemma":[0.9994904,0.00017750573,0.00005077628,0.000064717075,0.00018386821,0.000032728967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006321848,0.0004722085,0.00057652435,0.0012157278,0.00040007313,0.0007310417,0.00056570256,0.00037694533,0.0029590365],"category_scores_gemma":[0.0015437889,0.00025695816,0.00082190614,0.0016526199,0.00018419139,0.001259429,0.00075103395,0.0006385801,0.0014205289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004328566,0.00018959187,0.007112656,0.00016683812,0.00020500354,0.00030514313,0.00019606735,0.04356689,0.040262826,0.015041633,0.019773336,0.8727471],"study_design_scores_gemma":[0.000008583449,0.000039520408,0.0017536514,0.000007638057,0.00003686631,0.00007973716,0.000031407373,0.9804427,0.004953475,0.009475437,0.0031598362,0.000011009925],"about_ca_topic_score_codex":0.0039493777,"about_ca_topic_score_gemma":0.007022544,"teacher_disagreement_score":0.0039493777,"about_ca_system_score_codex":0.00036865062,"about_ca_system_score_gemma":0.00063332275,"threshold_uncertainty_score":0.009898961},"labels":[],"label_agreement":null},{"id":"W2975484689","doi":"10.1007/978-981-13-9282-5_34","title":"Twitter Sentiment Analysis Based on US Presidential Election 2016","year":2019,"lang":"en","type":"book-chapter","venue":"Smart innovation, systems and technologies","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Treasure; Presidential election; Quarter (Canadian coin); Sentiment analysis; Social media; Political science; Advertising; Presidential system; World Wide Web; Internet privacy; Computer science; Geography; Business; Politics; Artificial intelligence; Law","score_opus":0.019200097672502494,"score_gpt":0.24182204837097837,"score_spread":0.22262195069847587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2975484689","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8893254,0.00063032267,0.003777524,0.0013060321,0.0012798341,0.00012834539,0.049982358,0.0006349569,0.052935243],"genre_scores_gemma":[0.9182825,0.0004296998,0.0029986657,0.00014370799,0.00059655466,0.0001134966,0.056472506,0.000081155296,0.0208818],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99979454,0.000029648969,0.000019767034,0.000028036677,0.00008279059,0.000045124856],"domain_scores_gemma":[0.9995547,0.000090089925,0.000065331325,0.000015411319,0.000231955,0.0000424918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029522748,0.00029023417,0.00020239133,0.0013928382,0.0003770627,0.00057968934,0.00013070149,0.0002085253,0.004026593],"category_scores_gemma":[0.0010938206,0.000063370026,0.00026920746,0.0010544027,0.00006728607,0.00041414335,0.00026495205,0.00025398537,0.003549673],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001258656,0.0003490252,0.37941095,0.00046738374,0.00019471212,0.00067685987,0.0010021846,0.0032417825,0.02304161,0.0024638756,0.3002183,0.28767455],"study_design_scores_gemma":[0.00002634909,0.00022039762,0.8045064,0.00007929858,0.00018676739,0.00032335866,0.002370612,0.053069185,0.011203896,0.0006551081,0.12729728,0.000061396175],"about_ca_topic_score_codex":0.0055720513,"about_ca_topic_score_gemma":0.014577166,"teacher_disagreement_score":0.0055720513,"about_ca_system_score_codex":0.00024283037,"about_ca_system_score_gemma":0.0002363133,"threshold_uncertainty_score":0.013470292},"labels":[],"label_agreement":null},{"id":"W2977752818","doi":"10.1109/tcyb.2019.2940520","title":"Fast Supervised Topic Models for Short Text Emotion Detection","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Douglas College","funders":"","keywords":"Computer science; Friendship; Task (project management); Emotion detection; Context (archaeology); Artificial intelligence; Feature (linguistics); Machine learning; Space (punctuation); Emotion classification; Natural language processing; Information retrieval; Emotion recognition; Psychology","score_opus":0.025838934709713244,"score_gpt":0.2469300041359356,"score_spread":0.22109106942622236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2977752818","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029522259,0.0010174807,0.96536636,0.00023218546,0.00013662071,0.00012593511,0.0005674516,0.0020906157,0.00094106613],"genre_scores_gemma":[0.54767686,0.0012628344,0.43379802,0.00029326027,0.00092562294,0.001221771,0.0076915906,0.00058177504,0.0065482534],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987619,0.00044094422,0.00009375396,0.00037312688,0.0002150719,0.0001153729],"domain_scores_gemma":[0.99675167,0.0019558757,0.00024949998,0.00030450808,0.0006616459,0.00007681106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017476503,0.0015842408,0.0012085331,0.0014621336,0.000512485,0.001023898,0.0015159365,0.0011847713,0.0019546777],"category_scores_gemma":[0.0064578056,0.0005682449,0.0014209503,0.0013303631,0.00042836624,0.0023761312,0.0009460196,0.0021877133,0.0025044135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010784863,0.0005035186,0.0046382574,0.0004016327,0.00034000538,0.0002979897,0.0006271018,0.23631488,0.025030563,0.009547021,0.019726172,0.70149446],"study_design_scores_gemma":[0.000018707027,0.000034062585,0.00054239307,0.0000067254286,0.000019989027,0.000032092357,0.000032052532,0.99167377,0.0016697468,0.0047664675,0.0011943543,0.000009675838],"about_ca_topic_score_codex":0.0024599277,"about_ca_topic_score_gemma":0.003117903,"teacher_disagreement_score":0.0024599277,"about_ca_system_score_codex":0.0004959121,"about_ca_system_score_gemma":0.0005752194,"threshold_uncertainty_score":0.009242594},"labels":[],"label_agreement":null},{"id":"W2980403635","doi":"10.1145/3350546.3352503","title":"APNEA: Intelligent Ad-Bidding Using Sentiment Analysis","year":2019,"lang":"en","type":"article","venue":"IEEE/WIC/ACM International Conference on Web Intelligence","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Bidding; Computer science; Advertising; Context (archaeology); Relevance (law); Online advertising; Sentiment analysis; Real-time bidding; Information retrieval; Artificial intelligence; World Wide Web; Business; The Internet; Marketing; Political science","score_opus":0.09411455369664791,"score_gpt":0.34934969096237206,"score_spread":0.25523513726572417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2980403635","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1871235,0.0027554631,0.6582743,0.002187606,0.0016035424,0.0023373328,0.012362633,0.09552461,0.03783102],"genre_scores_gemma":[0.54043084,0.000679416,0.41397783,0.0013609808,0.00054027286,0.0007153071,0.018270701,0.0012587791,0.022765871],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919134,0.00019142518,0.00007601184,0.00015854396,0.00030541382,0.00007727721],"domain_scores_gemma":[0.9990694,0.00027142835,0.00011734318,0.000080391306,0.00039399182,0.00006754034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014832866,0.0012851909,0.0007915621,0.0018451596,0.00053865573,0.0012076348,0.0010616705,0.0006414072,0.006193489],"category_scores_gemma":[0.002749103,0.00036811276,0.0007576235,0.0008882268,0.00017878044,0.0014266206,0.00087331573,0.0007505278,0.004262645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001033863,0.0008209366,0.01317572,0.0004439967,0.00033897496,0.00034036784,0.00027996724,0.008621587,0.0392067,0.0027050877,0.12243803,0.8105948],"study_design_scores_gemma":[0.00019140405,0.00035480506,0.010440978,0.0000537985,0.0002133333,0.00032481612,0.00027220015,0.91908276,0.022843244,0.010225756,0.03590998,0.00008698289],"about_ca_topic_score_codex":0.0036300025,"about_ca_topic_score_gemma":0.0076748757,"teacher_disagreement_score":0.006193489,"about_ca_system_score_codex":0.00062891433,"about_ca_system_score_gemma":0.00063750555,"threshold_uncertainty_score":0.02071929},"labels":[],"label_agreement":null},{"id":"W2980680735","doi":"10.1080/17517575.2019.1669829","title":"The effect of aggregation methods on sentiment classification in Persian reviews","year":2019,"lang":"en","type":"article","venue":"Enterprise Information Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Persian; Computer science; Natural language processing; Artificial intelligence; Linguistics; Philosophy","score_opus":0.016241382288419707,"score_gpt":0.31428360750700046,"score_spread":0.29804222521858076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2980680735","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95929843,0.0026404061,0.031880602,0.00037465495,0.00019225535,0.00013771407,0.00032416434,0.0005606952,0.00459115],"genre_scores_gemma":[0.97314984,0.00038369888,0.02556456,0.000038853643,0.00009666415,0.000041495397,0.00029921683,0.000039621966,0.00038608717],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953739,0.0024383916,0.00044278274,0.00043215236,0.001166921,0.00014586699],"domain_scores_gemma":[0.98060006,0.0117916595,0.0018982115,0.0007858284,0.004656606,0.0002676538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00723424,0.0007467534,0.00068219693,0.002871086,0.00055885693,0.0016550622,0.00024954783,0.00025138637,0.0007338463],"category_scores_gemma":[0.021526027,0.0001560365,0.000577715,0.0020636762,0.00029681725,0.0009876714,0.00069728773,0.00047142347,0.00022699356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003854579,0.00046431378,0.11841716,0.00093940547,0.0009308265,0.0004271927,0.001893328,0.015684422,0.040356215,0.0015916137,0.005808424,0.8096326],"study_design_scores_gemma":[0.0002489439,0.002256028,0.35067254,0.000298671,0.0014099805,0.0007724664,0.0025490369,0.55322194,0.07526413,0.004589328,0.008562357,0.00015459408],"about_ca_topic_score_codex":0.0023371542,"about_ca_topic_score_gemma":0.0021251037,"teacher_disagreement_score":0.00723424,"about_ca_system_score_codex":0.0006470075,"about_ca_system_score_gemma":0.0003137253,"threshold_uncertainty_score":0.03825879},"labels":[],"label_agreement":null},{"id":"W298093259","doi":"10.1609/icwsm.v3i1.13990","title":"Trust Incident Account Model: Preliminary Indicators for Trust Rhetoric and Trust or Distrust in Blogs","year":2009,"lang":"en","type":"article","venue":"Proceedings of the International AAAI Conference on Web and Social Media","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Distrust; Rhetoric; Credibility; Express trust; Context (archaeology); Valence (chemistry); Trustworthiness; Sentiment analysis; Narrative; Social psychology; Psychology; Public relations; Political science; Computer science; Law; Linguistics","score_opus":0.03312851476930827,"score_gpt":0.2814509862121353,"score_spread":0.24832247144282704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W298093259","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5527812,0.00037319685,0.38950244,0.0026738336,0.00012423113,0.0027095282,0.010081732,0.001742664,0.04001122],"genre_scores_gemma":[0.91627103,0.00011512807,0.0768635,0.00006662612,0.000029725292,0.0013052744,0.0035661648,0.00005036153,0.0017322662],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99756277,0.0009153848,0.00037552306,0.00035377019,0.0005835071,0.00020904315],"domain_scores_gemma":[0.98301965,0.009851306,0.0023963447,0.0011306874,0.003106267,0.000495778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040039695,0.0009865632,0.0004509666,0.005213306,0.0013076395,0.0041624354,0.0010850917,0.0009473563,0.004475602],"category_scores_gemma":[0.024113668,0.00038259124,0.0013556863,0.004014775,0.0011830797,0.0062329187,0.0015143544,0.0015139616,0.00091430213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013581695,0.0011775485,0.5953092,0.0009765009,0.0003790778,0.00074630365,0.024421535,0.019449718,0.0049990597,0.13619281,0.013021831,0.20196821],"study_design_scores_gemma":[0.00009754922,0.00054846465,0.17274176,0.0004520729,0.00054788694,0.0009803677,0.016125552,0.7102892,0.0060906787,0.06979983,0.022101039,0.00022563103],"about_ca_topic_score_codex":0.010548856,"about_ca_topic_score_gemma":0.006299966,"teacher_disagreement_score":0.010548856,"about_ca_system_score_codex":0.0028385215,"about_ca_system_score_gemma":0.0022570654,"threshold_uncertainty_score":0.021175265},"labels":[],"label_agreement":null},{"id":"W2982280746","doi":"10.2196/16023","title":"Sentiment Analysis in Health and Well-Being: Systematic Review","year":2019,"lang":"en","type":"review","venue":"JMIR Medical Informatics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":218,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Cardiff University","keywords":"Computer science; Data science","score_opus":0.034072586995473685,"score_gpt":0.36689612087570417,"score_spread":0.3328235338802305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982280746","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009937563,0.99710244,0.00019928196,0.0004276841,0.00016345824,0.00029952705,0.00043488733,0.000008904204,0.00037002141],"genre_scores_gemma":[0.01732807,0.9798068,0.00087194896,0.0006583084,0.00018461754,0.00072478177,0.00029261052,0.000008517932,0.00012443954],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.99251807,0.003038982,0.0024007692,0.0005838925,0.0012765116,0.00018170194],"domain_scores_gemma":[0.94851655,0.040865548,0.0066829133,0.0004740832,0.0031548135,0.00030614305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00927234,0.0014720648,0.0060343915,0.010527626,0.0007297547,0.0030065605,0.0014267939,0.0016954207,0.0059169168],"category_scores_gemma":[0.04723576,0.0006482071,0.0066676936,0.008968943,0.0011631049,0.0028886856,0.0015878917,0.0011080914,0.0004751087],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016034165,0.000020809925,0.0010778831,0.9185397,0.004120425,0.00007905327,0.00038332297,0.000082168146,0.00012825863,0.00032150687,0.003278649,0.07180792],"study_design_scores_gemma":[0.00013797462,0.00017576128,0.0063075754,0.93216956,0.02913004,0.00031952126,0.0007181702,0.0001506259,0.00015353507,0.0007105171,0.029976916,0.000049937607],"about_ca_topic_score_codex":0.003900647,"about_ca_topic_score_gemma":0.011706258,"teacher_disagreement_score":0.010527626,"about_ca_system_score_codex":0.0027521465,"about_ca_system_score_gemma":0.007869775,"threshold_uncertainty_score":0.049037397},"labels":[],"label_agreement":null},{"id":"W2982404528","doi":"10.1109/dasc/picom/cbdcom/cyberscitech.2019.00185","title":"Evaluating the Performance of Machine Learning Sentiment Analysis Algorithms in Software Engineering","year":2019,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Sentiment analysis; Computer science; Lexicon; Machine learning; Artificial intelligence; Domain (mathematical analysis); Software; Algorithm; Natural language processing; Programming language","score_opus":0.021078543803452147,"score_gpt":0.28508771871957195,"score_spread":0.2640091749161198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982404528","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9336615,0.0017957883,0.05508986,0.00049527024,0.00035017193,0.000461833,0.0015184722,0.001107901,0.005519153],"genre_scores_gemma":[0.8914573,0.0005055219,0.102486216,0.00012368185,0.000116993135,0.00025080054,0.0041263495,0.00007928807,0.00085379835],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99350137,0.003017265,0.0008272397,0.000567737,0.0017933309,0.00029303003],"domain_scores_gemma":[0.985789,0.008227636,0.0010045804,0.00075619854,0.003977969,0.0002444658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008404131,0.00092332315,0.0006784788,0.0028039252,0.00059691677,0.0014583978,0.00061094423,0.0010721326,0.0007206629],"category_scores_gemma":[0.022903005,0.00014402978,0.00060299254,0.002065541,0.00033345135,0.0015558307,0.0005964086,0.00061727356,0.0005092992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0036424515,0.0025428047,0.095330805,0.001609411,0.0011198748,0.00021698329,0.00070890575,0.08560658,0.04313553,0.0037171168,0.017235728,0.7451337],"study_design_scores_gemma":[0.00016003194,0.0022610174,0.06048073,0.00010781573,0.00017967154,0.00016102842,0.0006003164,0.89169544,0.036456466,0.002809862,0.005023597,0.000064022024],"about_ca_topic_score_codex":0.0015793721,"about_ca_topic_score_gemma":0.0017146841,"teacher_disagreement_score":0.008404131,"about_ca_system_score_codex":0.0008654477,"about_ca_system_score_gemma":0.00056627573,"threshold_uncertainty_score":0.044445872},"labels":[],"label_agreement":null},{"id":"W2984134878","doi":"10.48550/arxiv.1911.02147","title":"Seq2Emo for Multi-label Emotion Classification Based on Latent Variable Chains Transformation","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Intuition; Artificial intelligence; Emotion classification; Anger; Task (project management); Latent variable; Emotion detection; Class (philosophy); Machine learning; Transformation (genetics); Binary classification; Binary number; Pattern recognition (psychology); Natural language processing; Emotion recognition; Psychology; Mathematics; Support vector machine; Social psychology; Cognitive science","score_opus":0.2014690071332067,"score_gpt":0.2357659610066679,"score_spread":0.03429695387346121,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2984134878","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012659584,0.00027796542,0.9811646,0.00031832108,0.00008582894,0.000079881,0.000626621,0.0034313968,0.0013558812],"genre_scores_gemma":[0.46324077,0.0007102321,0.5134715,0.00062134786,0.00025437714,0.00080528285,0.0077312207,0.0009239484,0.012241292],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950254,0.00018039474,0.000021791937,0.00015655787,0.000096425465,0.00004225622],"domain_scores_gemma":[0.9993563,0.00033411328,0.000054000135,0.00009001177,0.0001293121,0.00003634509],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010099837,0.00076469965,0.00060045853,0.0007781449,0.0003796742,0.00084314874,0.00096531434,0.000830564,0.005105507],"category_scores_gemma":[0.0024265826,0.00024671038,0.001308913,0.0007369262,0.0003799061,0.0013068576,0.001102254,0.0021130936,0.0027265588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00071044476,0.0004939597,0.0098959645,0.00022962871,0.00018532359,0.00028769416,0.00040249238,0.26570863,0.016074153,0.036710117,0.026313847,0.64298767],"study_design_scores_gemma":[0.000009170227,0.000019720928,0.00021813855,0.000005708364,0.0000055525265,0.00001416637,0.000012463649,0.987204,0.001008145,0.009995211,0.0015022048,0.0000054742886],"about_ca_topic_score_codex":0.002894892,"about_ca_topic_score_gemma":0.0045404118,"teacher_disagreement_score":0.005105507,"about_ca_system_score_codex":0.00059493113,"about_ca_system_score_gemma":0.0007332203,"threshold_uncertainty_score":0.017079592},"labels":[],"label_agreement":null},{"id":"W2984263896","doi":"10.32877/bt.v2i1.92","title":"Extraction Opinion of Social Media in Higher Education Using Sentiment Analysis","year":2019,"lang":"en","type":"article","venue":"bit-Tech","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Sentiment analysis; Social media; Political science; Advertising; Media studies; Computer science; Sociology; Artificial intelligence; Business; Law","score_opus":0.05059007995558874,"score_gpt":0.3295014568925162,"score_spread":0.27891137693692747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2984263896","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85600066,0.0011842427,0.09506795,0.0015773241,0.0004982392,0.00067859923,0.00833625,0.0007721691,0.03588452],"genre_scores_gemma":[0.94803005,0.0009004621,0.03978269,0.00016213334,0.00031372963,0.00027501016,0.004054043,0.000048964863,0.0064328276],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994498,0.00011132804,0.00008113017,0.00006544531,0.00022465366,0.00006760687],"domain_scores_gemma":[0.9991048,0.00022423669,0.0001473087,0.000028615981,0.00046511003,0.000029917936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058101193,0.00037012328,0.00027292583,0.0028629112,0.00041282194,0.0011606882,0.00015270572,0.0002970304,0.001805957],"category_scores_gemma":[0.0019076266,0.000106321655,0.0004747588,0.002057727,0.00014671724,0.0009323623,0.0003104805,0.00024662758,0.0014145351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070018164,0.00043738383,0.12046709,0.0011644447,0.00018020417,0.0019262334,0.0028930942,0.002544542,0.11381712,0.0044076554,0.022482594,0.72897947],"study_design_scores_gemma":[0.000073015704,0.0009796178,0.5351015,0.0007031786,0.00057771715,0.0023613707,0.018076275,0.21744682,0.10807761,0.009994971,0.10643081,0.00017716853],"about_ca_topic_score_codex":0.0013109841,"about_ca_topic_score_gemma":0.001938704,"teacher_disagreement_score":0.0028629112,"about_ca_system_score_codex":0.0004217922,"about_ca_system_score_gemma":0.00034765626,"threshold_uncertainty_score":0.006041527},"labels":[],"label_agreement":null},{"id":"W2987134874","doi":"10.48550/arxiv.1911.01217","title":"Detect Toxic Content to Improve Online Conversations","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Python (programming language); Computer science; Naive Bayes classifier; Artificial intelligence; Support vector machine; Social media; Machine learning; Resampling; Natural language processing; Information retrieval; Deep learning; Content (measure theory); World Wide Web; Mathematics","score_opus":0.1210382773475492,"score_gpt":0.2110812787755628,"score_spread":0.0900430014280136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2987134874","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5414622,0.004352048,0.38390464,0.00527943,0.0011461981,0.0006370051,0.012674783,0.013790492,0.036753315],"genre_scores_gemma":[0.9204743,0.00079099555,0.06121995,0.00059687556,0.0005612273,0.00015036856,0.0070292214,0.00031269103,0.008864405],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993413,0.00020150834,0.00003374586,0.00017328796,0.00015367038,0.00009644679],"domain_scores_gemma":[0.9976949,0.0011025248,0.0003168198,0.00015821912,0.0005752873,0.00015224087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009492673,0.0011213681,0.00059920485,0.0016889483,0.00050554087,0.0015347126,0.0006216994,0.0012446442,0.003384639],"category_scores_gemma":[0.005672659,0.00026387608,0.0007475295,0.00076925854,0.00026426252,0.0024789893,0.00091337145,0.0012454119,0.003975586],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013443361,0.0013282597,0.1612586,0.000837516,0.000353936,0.0005857715,0.0018172653,0.038169336,0.05247122,0.005445584,0.04861533,0.68777287],"study_design_scores_gemma":[0.000033056258,0.00046894074,0.040255714,0.00014084423,0.00018656554,0.00037482538,0.0014720429,0.8896689,0.028959652,0.013614249,0.024757322,0.000067897454],"about_ca_topic_score_codex":0.0029607587,"about_ca_topic_score_gemma":0.0048897816,"teacher_disagreement_score":0.003384639,"about_ca_system_score_codex":0.0006697892,"about_ca_system_score_gemma":0.00057006953,"threshold_uncertainty_score":0.011322796},"labels":[],"label_agreement":null},{"id":"W2990522170","doi":"10.1109/tcss.2019.2951326","title":"Improving Sentiment Polarity Detection Through Target Identification","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Computational Social Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick; Université du Québec à Montréal","funders":"","keywords":"Computer science; Lexicon; Polarity (international relations); Sentiment analysis; Identification (biology); Sentence; Artificial intelligence; Natural language processing; Data mining; Machine learning","score_opus":0.017208079475631303,"score_gpt":0.26004954950162157,"score_spread":0.24284147002599027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990522170","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29526561,0.0026200428,0.6768134,0.00090466236,0.0006513976,0.00075905543,0.001392883,0.006767749,0.014825208],"genre_scores_gemma":[0.80579203,0.0010698597,0.18266732,0.0003282867,0.00048023954,0.00032797846,0.0026208854,0.0003319661,0.0063814246],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894637,0.00021806528,0.00009356475,0.00025918477,0.00036849617,0.00011431172],"domain_scores_gemma":[0.9974727,0.00082762016,0.00030174453,0.00012103953,0.0011813077,0.00009550847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011255243,0.001264661,0.0010683683,0.0030118378,0.00058413524,0.0013292979,0.00049685506,0.00065608294,0.0015349619],"category_scores_gemma":[0.0044504516,0.000298651,0.00082908897,0.0011282327,0.000242954,0.0018384972,0.0008157876,0.0007346254,0.002259556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009520786,0.00029212853,0.013336759,0.00052370416,0.00013283137,0.00065748487,0.00054730603,0.004198505,0.14738685,0.0021271794,0.014889578,0.81495553],"study_design_scores_gemma":[0.00010920646,0.0006113764,0.030057332,0.000087412416,0.00033682163,0.0016897613,0.00085286074,0.8086712,0.1335741,0.0058513856,0.01801153,0.00014699457],"about_ca_topic_score_codex":0.0013157141,"about_ca_topic_score_gemma":0.0013978167,"teacher_disagreement_score":0.0030118378,"about_ca_system_score_codex":0.00040886528,"about_ca_system_score_gemma":0.0007309441,"threshold_uncertainty_score":0.005952418},"labels":[],"label_agreement":null},{"id":"W2990784666","doi":"10.48550/arxiv.1912.00741","title":"SemEval-2014 Task 9: Sentiment Analysis in Twitter","year":2019,"lang":"en","type":"preprint","venue":"NPARC","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"SemEval; Computer science; Task (project management); Sentiment analysis; Point (geometry); Natural language processing; Artificial intelligence; Scale (ratio); Arabic; Information retrieval; Linguistics; Geography; Mathematics; Cartography","score_opus":0.022214732384613994,"score_gpt":0.281182413725846,"score_spread":0.25896768134123205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990784666","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26260343,0.0059678955,0.20506155,0.008493613,0.009348478,0.0048862128,0.35090482,0.08817875,0.06455527],"genre_scores_gemma":[0.22212577,0.0008096695,0.21823955,0.0020889817,0.0015330248,0.0044908472,0.5070592,0.0053099524,0.038342986],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9953349,0.0020699333,0.00032315438,0.000924163,0.000839491,0.0005082981],"domain_scores_gemma":[0.994823,0.0022543315,0.0002880958,0.0009388553,0.0012189033,0.0004768396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004855364,0.003532412,0.0016393134,0.002494552,0.002303916,0.0028368211,0.002344392,0.002974262,0.014495962],"category_scores_gemma":[0.013650516,0.00053147064,0.0021255175,0.0019029197,0.0006620192,0.0038098928,0.0045600925,0.0028525547,0.017510444],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012924275,0.00085493486,0.0078059705,0.0012837495,0.00034285037,0.00046142467,0.0006483246,0.004326859,0.013928877,0.0027340841,0.78349537,0.18282516],"study_design_scores_gemma":[0.0011965662,0.0013660132,0.03585536,0.00038053343,0.00033108648,0.0012898601,0.0025047706,0.21655874,0.0498554,0.023291497,0.6670153,0.0003548748],"about_ca_topic_score_codex":0.0056482786,"about_ca_topic_score_gemma":0.016932929,"teacher_disagreement_score":0.014495962,"about_ca_system_score_codex":0.0015300958,"about_ca_system_score_gemma":0.0021457125,"threshold_uncertainty_score":0.048493862},"labels":[],"label_agreement":null},{"id":"W2991136969","doi":"10.26615/978-954-452-056-4_091","title":"Neural Feature Extraction for Contextual Emotion Detection","year":2019,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Support vector machine; Extractor; Artificial intelligence; Classifier (UML); Artificial neural network; Feature extraction; SemEval; F1 score; Pattern recognition (psychology); Task (project management); Emotion recognition; Recurrent neural network; Word (group theory); Speech recognition; Machine learning; Mathematics","score_opus":0.01761202619320946,"score_gpt":0.274281198203004,"score_spread":0.25666917200979456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991136969","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0954487,0.0018992895,0.88227314,0.00027133242,0.00038652407,0.00017624932,0.0019028838,0.010548575,0.007093286],"genre_scores_gemma":[0.66235036,0.00075715774,0.32169968,0.00024487337,0.00029484302,0.00035657317,0.004629033,0.0004992828,0.00916816],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957687,0.00006986726,0.000025180823,0.00016254514,0.00008798506,0.00007759199],"domain_scores_gemma":[0.9996511,0.00009699469,0.00003675771,0.000055126417,0.00014272139,0.000017359436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039386252,0.0011409677,0.00069521554,0.00088471005,0.0002930807,0.00061298703,0.000663538,0.0005171615,0.003468681],"category_scores_gemma":[0.0015243718,0.00022092379,0.00073028373,0.0005909641,0.00015217386,0.0009617456,0.00075616146,0.0007171921,0.0021185563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038295696,0.00019045232,0.0029516676,0.0002448107,0.00011926867,0.00020486211,0.00014470704,0.009647753,0.1227382,0.0016600565,0.009471942,0.85224324],"study_design_scores_gemma":[0.000047025504,0.00046454664,0.018052826,0.000097483535,0.0002989365,0.00048444027,0.00022106392,0.81485134,0.12706167,0.00956856,0.028743658,0.00010845588],"about_ca_topic_score_codex":0.00245517,"about_ca_topic_score_gemma":0.003521601,"teacher_disagreement_score":0.003468681,"about_ca_system_score_codex":0.00034219475,"about_ca_system_score_gemma":0.0003034842,"threshold_uncertainty_score":0.011603951},"labels":[],"label_agreement":null},{"id":"W2996347617","doi":"10.1109/iemcon.2019.8936139","title":"Social Media and Sentiment Analysis: The Nigeria Presidential Election 2019","year":2019,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Nigerians; Sentiment analysis; Presidential system; Social media; Presidential election; Lexicon; Public opinion; Classifier (UML); General election; Artificial intelligence; Political science; Computer science; Politics; Public relations; Law","score_opus":0.00993320778540663,"score_gpt":0.25270194238780347,"score_spread":0.24276873460239684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996347617","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98966134,0.00038372376,0.0005689016,0.00067293906,0.00017426017,0.000055578315,0.0013951901,0.000017854896,0.007070237],"genre_scores_gemma":[0.9906816,0.00057344255,0.0017411874,0.000105589614,0.00017689123,0.000059798374,0.0018714664,0.000013189611,0.0047768904],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996517,0.00012333375,0.00003263584,0.00003274839,0.00010860919,0.000051015148],"domain_scores_gemma":[0.99907553,0.00040759647,0.00016828834,0.000029611378,0.00026539862,0.00005360849],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007061569,0.0001671348,0.00015298539,0.0011901684,0.00083637924,0.0008458275,0.000091792346,0.0002630434,0.00069309963],"category_scores_gemma":[0.001805174,0.0000885039,0.00011183178,0.0013953875,0.00023208461,0.00055130664,0.0003305896,0.00036799963,0.00031624176],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010352673,0.0005631861,0.7099777,0.00065965374,0.00008911996,0.0030327393,0.014709828,0.0014147869,0.022263914,0.0045733424,0.026769275,0.21491125],"study_design_scores_gemma":[0.00001358812,0.00021572686,0.9228132,0.00018525835,0.00004971845,0.00047963724,0.018703679,0.01012765,0.008099091,0.0005431976,0.038735554,0.000033810655],"about_ca_topic_score_codex":0.0074673197,"about_ca_topic_score_gemma":0.017864846,"teacher_disagreement_score":0.0074673197,"about_ca_system_score_codex":0.00058926694,"about_ca_system_score_gemma":0.00033627969,"threshold_uncertainty_score":0.014847755},"labels":[],"label_agreement":null},{"id":"W2997141632","doi":"10.1609/aaai.v34i05.6517","title":"Replicate, Walk, and Stop on Syntax: An Effective Neural Network Model for Aspect-Level Sentiment Classification","year":2020,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Beijing Advanced Innovation Center for Big Data and Brain Computing; Fundamental Research Funds for the Central Universities; State Key Laboratory of Software Development Environment; National Natural Science Foundation of China","keywords":"Computer science; Sentence; Artificial intelligence; Sentiment analysis; Syntax; Leverage (statistics); Natural language processing; Representation (politics); Machine learning","score_opus":0.1952806311226545,"score_gpt":0.3317955325796875,"score_spread":0.13651490145703302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997141632","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22729912,0.0011289804,0.7637787,0.0012506492,0.0001859315,0.00015580918,0.00057945587,0.0016281257,0.003993221],"genre_scores_gemma":[0.8915312,0.00043720787,0.10115234,0.00031628294,0.000081604514,0.0002004006,0.0006939167,0.000108339,0.005478669],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998361,0.000050230716,0.000009566772,0.00005931689,0.000022615704,0.00002210998],"domain_scores_gemma":[0.9995609,0.00021857412,0.000052412783,0.000045524423,0.000095665244,0.00002687809],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005796338,0.0007455936,0.0007574813,0.0006733288,0.00036199138,0.0006490533,0.0017730109,0.0011037919,0.0014566442],"category_scores_gemma":[0.0019217747,0.0003930428,0.0007114937,0.0007302375,0.00050704804,0.0016451018,0.00063981005,0.0012474974,0.00040103422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026510144,0.00017425616,0.002485337,0.000084326115,0.00013417262,0.00015703551,0.00012355234,0.7913265,0.0047783977,0.011906324,0.0038906771,0.18467428],"study_design_scores_gemma":[0.0000034313332,0.000013138475,0.00006871815,0.0000021437254,0.000005526977,0.0000057084835,0.0000023911246,0.9973008,0.00013970937,0.002380002,0.00007629145,0.0000022503532],"about_ca_topic_score_codex":0.0074999537,"about_ca_topic_score_gemma":0.01116521,"teacher_disagreement_score":0.0074999537,"about_ca_system_score_codex":0.00080089027,"about_ca_system_score_gemma":0.0006651604,"threshold_uncertainty_score":0.014912605},"labels":[],"label_agreement":null},{"id":"W2999331497","doi":"10.1007/s00607-019-00777-6","title":"Micro-journal mining to understand mood triggers","year":2020,"lang":"en","type":"article","venue":"Computing","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multinomial logistic regression; Mood; Trigram; Sentiment analysis; Computer science; Word (group theory); Logistic regression; Psychology; Multinomial distribution; Natural language processing; Cognitive psychology; Artificial intelligence; Machine learning; Linguistics; Data science; Social psychology; Econometrics; Mathematics","score_opus":0.05212175688882666,"score_gpt":0.27568205735296525,"score_spread":0.22356030046413858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999331497","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6296371,0.008833351,0.19485497,0.0035891188,0.0026586435,0.00097635435,0.08533958,0.012618509,0.061492354],"genre_scores_gemma":[0.8940346,0.0016733838,0.06432835,0.0003836098,0.0014438551,0.00039633721,0.025481107,0.00042209917,0.011836597],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999209,0.00011040947,0.000109724875,0.0001846734,0.00027631246,0.00010989096],"domain_scores_gemma":[0.9949969,0.0018066546,0.0009224335,0.00040810017,0.001454655,0.0004111719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007715512,0.00057800196,0.0005934176,0.007998873,0.00072790816,0.0019842773,0.0005440411,0.00043704436,0.0064167017],"category_scores_gemma":[0.0054500764,0.00021378377,0.0006240386,0.007478167,0.00018367963,0.0015139594,0.0007874431,0.0005732422,0.0041340073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011695944,0.00058631774,0.19087365,0.0014082253,0.00054075354,0.0010311657,0.0011056269,0.0028187223,0.05253817,0.01229036,0.08957189,0.6460655],"study_design_scores_gemma":[0.00013564754,0.0007984901,0.49890056,0.00037256343,0.0007359954,0.0019450219,0.0033023397,0.2422998,0.04144141,0.053381745,0.15647061,0.00021583847],"about_ca_topic_score_codex":0.0020785104,"about_ca_topic_score_gemma":0.0069496767,"teacher_disagreement_score":0.007998873,"about_ca_system_score_codex":0.0004347175,"about_ca_system_score_gemma":0.000786891,"threshold_uncertainty_score":0.021466017},"labels":[],"label_agreement":null},{"id":"W3004299292","doi":"10.1109/trustcom/bigdatase.2019.00090","title":"Identifying High Value Users in Twitter Based on Text Mining Approaches","year":2019,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Popularity; Boosting (machine learning); Resampling; Classifier (UML); Machine learning; Sentiment analysis; Social media; Artificial intelligence; Data mining; Filter (signal processing); Learning to rank; World Wide Web; Ranking (information retrieval)","score_opus":0.06842911131441637,"score_gpt":0.26482375257508817,"score_spread":0.1963946412606718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004299292","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.856083,0.0010373989,0.12207978,0.0018195272,0.00023367327,0.000661188,0.0061192857,0.0011544451,0.01081174],"genre_scores_gemma":[0.93025523,0.0004768196,0.060786236,0.00015802516,0.00022286712,0.0002471499,0.00395559,0.000032559557,0.0038654667],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930644,0.00013468413,0.00008665753,0.0001328278,0.00024124117,0.00009822758],"domain_scores_gemma":[0.9985965,0.000573834,0.00027500882,0.000064400665,0.0004113296,0.0000790079],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007534329,0.0006748124,0.0006216493,0.0047578453,0.0006078511,0.0012528319,0.0005840784,0.0006901444,0.0010312776],"category_scores_gemma":[0.0019543262,0.00017995368,0.000688907,0.0022863455,0.00017734645,0.0016426399,0.0004159945,0.00049480767,0.0010335334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008683295,0.001280289,0.29560587,0.0006129213,0.00032106094,0.0012900603,0.0011682627,0.01665511,0.039592717,0.0033296396,0.015816139,0.62345964],"study_design_scores_gemma":[0.00003331943,0.00036972898,0.1300377,0.00010859578,0.00015517548,0.0008490124,0.002756349,0.8259201,0.023165032,0.0059814067,0.010536647,0.000086832886],"about_ca_topic_score_codex":0.0020416297,"about_ca_topic_score_gemma":0.004008688,"teacher_disagreement_score":0.0047578453,"about_ca_system_score_codex":0.0004565437,"about_ca_system_score_gemma":0.0003194837,"threshold_uncertainty_score":0.0040594935},"labels":[],"label_agreement":null},{"id":"W3007289217","doi":"10.31542/muse.v4i1.877","title":"Hashtag Politics: A Twitter Sentiment Analysis of the 2015 Canadian Federal Election","year":2020,"lang":"en","type":"article","venue":"MacEwan University Student eJournal","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"MacEwan University","funders":"","keywords":"Sentiment analysis; Federal election; Social media; Democracy; Politics; Political science; Application programming interface; Advertising; Computer science; Law; Business; Artificial intelligence","score_opus":0.018458387467909204,"score_gpt":0.24838341525171498,"score_spread":0.22992502778380577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007289217","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93331146,0.00021707485,0.015533263,0.00082629884,0.000130184,0.0014462309,0.034749392,0.00064018095,0.013145824],"genre_scores_gemma":[0.93836176,0.00019680902,0.02129913,0.00020062891,0.00003891551,0.0015298163,0.025758365,0.00018341941,0.012431076],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99888366,0.00026356606,0.000039763316,0.0001899413,0.0004314586,0.0001916974],"domain_scores_gemma":[0.9977818,0.00062287,0.00019680856,0.000107656095,0.0011522195,0.00013856275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002269772,0.00066615955,0.00049202406,0.0013892045,0.0014669116,0.00088516326,0.00089516514,0.00034781056,0.0048418813],"category_scores_gemma":[0.007093946,0.00026857347,0.0007107293,0.0024452459,0.0004812521,0.00052921555,0.0005911272,0.0007347345,0.00097483577],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002530742,0.0010578324,0.67641747,0.00067087344,0.00072276796,0.00023133174,0.0048869937,0.038474742,0.012400109,0.00862268,0.05957733,0.1944071],"study_design_scores_gemma":[0.00012844759,0.00046614266,0.85402155,0.00006875788,0.00025524417,0.000038689184,0.0026536186,0.10488309,0.0029504877,0.0015793928,0.032817535,0.00013694222],"about_ca_topic_score_codex":0.88043076,"about_ca_topic_score_gemma":0.92942405,"teacher_disagreement_score":0.11956924,"about_ca_system_score_codex":0.009085136,"about_ca_system_score_gemma":0.011079207,"threshold_uncertainty_score":0.24054676},"labels":[],"label_agreement":null},{"id":"W3007466464","doi":"10.1007/s11280-020-00785-z","title":"A comprehensive analysis of adverb types for mining user sentiments on amazon product reviews","year":2020,"lang":"en","type":"article","venue":"World Wide Web","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Adverb; Sentiment analysis; Superlative; Computer science; Natural language processing; Degree (music); Artificial intelligence; Product (mathematics); Meaning (existential); Linguistics; Information retrieval; Noun; Mathematics; Psychology","score_opus":0.05783494863255713,"score_gpt":0.30315538942918363,"score_spread":0.24532044079662652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007466464","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9002478,0.006845478,0.0633941,0.000589491,0.00018682869,0.000490263,0.017102426,0.0021904702,0.008953233],"genre_scores_gemma":[0.888745,0.0018532919,0.081644684,0.00015471231,0.00020858562,0.00025770743,0.022749383,0.00013327168,0.0042534852],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910957,0.00014362088,0.0001009377,0.00012669084,0.00045528414,0.000063878084],"domain_scores_gemma":[0.99830556,0.00058216805,0.0001871239,0.00013566493,0.000695832,0.00009369155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007817094,0.0005797161,0.00061450154,0.0037121486,0.0005130632,0.0009478773,0.00031088397,0.00033644965,0.00110705],"category_scores_gemma":[0.0030951193,0.00021344586,0.0010126781,0.0032310653,0.000117740994,0.0012277571,0.00041941315,0.00041338822,0.0008741474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008302009,0.00072305964,0.15678254,0.0017845775,0.0008981104,0.0012223464,0.00062964344,0.0034227492,0.09658841,0.0024651561,0.034121934,0.7005313],"study_design_scores_gemma":[0.000096005504,0.0008976335,0.607792,0.0002611047,0.001303134,0.002212433,0.0014342269,0.3052136,0.035397816,0.003445874,0.04178447,0.00016167846],"about_ca_topic_score_codex":0.0058170003,"about_ca_topic_score_gemma":0.014611104,"teacher_disagreement_score":0.0058170003,"about_ca_system_score_codex":0.00034579853,"about_ca_system_score_gemma":0.00079669047,"threshold_uncertainty_score":0.011566281},"labels":[],"label_agreement":null},{"id":"W3010353689","doi":"10.23919/cnsm46954.2019.9012679","title":"Detecting Factors Responsible for Diabetes Prevalence in Nigeria using Social Media and Machine Learning","year":2019,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Nigerians; Diabetes mellitus; Type 2 diabetes; Social media; Intervention (counseling); Medicine; Disease; Gerontology; Environmental health; Computer science; Political science; Nursing; Endocrinology; Internal medicine; World Wide Web","score_opus":0.04170551672884616,"score_gpt":0.2776477401356339,"score_spread":0.23594222340678778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010353689","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9948362,0.00041913247,0.00074428634,0.00049033365,0.00004384918,0.00006670916,0.00091290765,0.000013084526,0.002473342],"genre_scores_gemma":[0.9962475,0.0005205365,0.0019965349,0.000067241366,0.000038137085,0.000044364642,0.0005176123,0.000002564353,0.0005654959],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997043,0.00009328661,0.0000406248,0.000046653382,0.00006493384,0.000050110997],"domain_scores_gemma":[0.9987897,0.0005262368,0.00039397887,0.000033322158,0.00018073326,0.000076060605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052049296,0.00023609327,0.00020497799,0.0022040356,0.0004924038,0.00081537216,0.00015643859,0.00030150128,0.00091937365],"category_scores_gemma":[0.0019698893,0.00012829927,0.00026543898,0.0012522885,0.0001473466,0.00065575854,0.00035437485,0.00036860773,0.00018484167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000099281795,0.00022297393,0.95158964,0.00013691119,0.000040421608,0.00033647145,0.0013435185,0.0002531635,0.0013834252,0.00029935138,0.0010073526,0.043287434],"study_design_scores_gemma":[0.000010183737,0.00015531921,0.96744734,0.00028310058,0.00012443372,0.00065856834,0.012726668,0.011918225,0.0017144862,0.00060064293,0.004332532,0.00002859182],"about_ca_topic_score_codex":0.008684003,"about_ca_topic_score_gemma":0.014695567,"teacher_disagreement_score":0.008684003,"about_ca_system_score_codex":0.00034351836,"about_ca_system_score_gemma":0.00033155447,"threshold_uncertainty_score":0.01726693},"labels":[],"label_agreement":null},{"id":"W3013694547","doi":"10.1007/978-3-030-44900-1_10","title":"Using Twitter Streams for Opinion Mining: A Case Study on Airport Noise","year":2020,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"NeuroDevNet","funders":"","keywords":"Computer science; Sentiment analysis; Support vector machine; Lexicon; Classifier (UML); Artificial intelligence; Natural language processing","score_opus":0.19790706520709853,"score_gpt":0.3833335053465498,"score_spread":0.18542644013945128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3013694547","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93392724,0.00068149355,0.038982123,0.003477548,0.00031507388,0.00031918986,0.002228585,0.0004447964,0.019624],"genre_scores_gemma":[0.9586423,0.0007145698,0.028023165,0.00042946276,0.00024608593,0.00012905637,0.0015905307,0.00014213567,0.0100826835],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9989322,0.00047318125,0.000057191115,0.00010337423,0.00033057708,0.0001035706],"domain_scores_gemma":[0.99531806,0.0034693177,0.0002789756,0.00016295812,0.000603287,0.00016736389],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001336362,0.0005064988,0.00032263575,0.0010083584,0.0013006607,0.0015995693,0.000794384,0.0015501974,0.0011879496],"category_scores_gemma":[0.0049360176,0.00015093267,0.00039563305,0.0016401127,0.00040603703,0.001649637,0.00073927344,0.000645227,0.0006195398],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019290025,0.0021455847,0.16063063,0.002186822,0.00043623152,0.021920985,0.039152335,0.03421256,0.049859464,0.011012783,0.079847276,0.59666634],"study_design_scores_gemma":[0.0002209423,0.0017950382,0.14026858,0.0006261724,0.00072250795,0.0074854135,0.12603243,0.4404317,0.06109377,0.015858058,0.20511174,0.000353654],"about_ca_topic_score_codex":0.005687735,"about_ca_topic_score_gemma":0.013185227,"teacher_disagreement_score":0.005687735,"about_ca_system_score_codex":0.0006096875,"about_ca_system_score_gemma":0.0003754323,"threshold_uncertainty_score":0.011309266},"labels":[],"label_agreement":null},{"id":"W3013840972","doi":"","title":"Leveraging Emotion Features in News Recommendations.","year":2019,"lang":"en","type":"article","venue":"Conference on Recommender Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Natural language processing; Information retrieval","score_opus":0.05817123570360822,"score_gpt":0.2920065012852803,"score_spread":0.23383526558167206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3013840972","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49080947,0.037015975,0.40871438,0.009024609,0.0049566,0.0007861642,0.011894614,0.004439103,0.0323591],"genre_scores_gemma":[0.8840563,0.0037592638,0.09448109,0.0006914116,0.0017875014,0.00015216235,0.006785661,0.00013518249,0.0081514595],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991295,0.00030944956,0.00007607733,0.0001715857,0.00022703595,0.00008637578],"domain_scores_gemma":[0.9961014,0.0020663792,0.00021940628,0.00025219622,0.0012240849,0.00013642332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015487971,0.0008994344,0.001008935,0.0019995873,0.00051449344,0.0014780591,0.0005924589,0.0011334022,0.0021677434],"category_scores_gemma":[0.00738541,0.00031590217,0.00067787676,0.0016890969,0.0001489262,0.0023358392,0.00045999952,0.0015339346,0.0022984827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014990281,0.0014525366,0.03179038,0.00066292787,0.00093984435,0.0002839496,0.00036537295,0.01678988,0.019303707,0.0021815763,0.08145218,0.84327865],"study_design_scores_gemma":[0.0001334104,0.00082691293,0.027787728,0.00022287581,0.000949442,0.00031394383,0.00043877473,0.92871183,0.012391121,0.007716656,0.02040108,0.0001061472],"about_ca_topic_score_codex":0.0062326086,"about_ca_topic_score_gemma":0.017898627,"teacher_disagreement_score":0.0062326086,"about_ca_system_score_codex":0.00033245908,"about_ca_system_score_gemma":0.0003352932,"threshold_uncertainty_score":0.0123927},"labels":[],"label_agreement":null},{"id":"W3020396369","doi":"10.1101/2020.04.22.054973","title":"Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Social media; Coronavirus disease 2019 (COVID-19); Computer science; Sentiment analysis; Artificial intelligence; Recurrent neural network; Public health; Natural language processing; The Internet; Pandemic; Coronavirus; Decision tree; Artificial neural network; Deep learning; Data science; World Wide Web; Medicine; Disease","score_opus":0.1185556792798535,"score_gpt":0.31520662270554406,"score_spread":0.19665094342569056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3020396369","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7441862,0.00071024644,0.24367952,0.0022330694,0.00035139202,0.00017824808,0.0015268578,0.0013162073,0.005818175],"genre_scores_gemma":[0.9641472,0.00016658554,0.03078689,0.000122021265,0.00020811558,0.000072445524,0.0015191055,0.000041457795,0.002936184],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995334,0.00016675012,0.00002963597,0.000110941575,0.000059081613,0.00010011454],"domain_scores_gemma":[0.99850637,0.0008473355,0.00019749413,0.000058182944,0.0003317383,0.000058882266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011688515,0.0007382808,0.00041621627,0.0013424066,0.00047918918,0.00079465465,0.0005185903,0.000730777,0.0016362617],"category_scores_gemma":[0.0024868974,0.00021970815,0.000828867,0.00073152955,0.00027137523,0.00095171784,0.0005595307,0.0011099074,0.00062759826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015234215,0.0014332117,0.054764364,0.00048817645,0.00046918332,0.0010554084,0.002191896,0.14384404,0.08939498,0.007275494,0.022033917,0.67552584],"study_design_scores_gemma":[0.000008556503,0.00003811639,0.0030584054,0.000008203949,0.000026333075,0.000013178064,0.00014556153,0.9912218,0.0032465653,0.001628286,0.00059824454,0.00000677118],"about_ca_topic_score_codex":0.004620959,"about_ca_topic_score_gemma":0.005338446,"teacher_disagreement_score":0.004620959,"about_ca_system_score_codex":0.00078344403,"about_ca_system_score_gemma":0.0004638658,"threshold_uncertainty_score":0.009188116},"labels":[],"label_agreement":null},{"id":"W3027304069","doi":"10.1016/b978-0-08-100508-8.00009-6","title":"Sentiment Analysis","year":2016,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":384,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Sentiment analysis; Paralanguage; Computer science; Natural language processing; Task (project management); Valence (chemistry); Artificial intelligence; Tone (literature); Linguistics; Psychology; Communication","score_opus":0.017048555223839523,"score_gpt":0.24697783974290977,"score_spread":0.22992928451907024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3027304069","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01328876,0.003694419,0.32198012,0.0028824653,0.0026172833,0.0008172875,0.014301113,0.014419423,0.6259991],"genre_scores_gemma":[0.08286243,0.0050195446,0.24198024,0.001295581,0.0012034345,0.00085376255,0.034886256,0.0039916085,0.62790716],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992567,0.00009924451,0.0000584787,0.00015059198,0.0003802055,0.000054714852],"domain_scores_gemma":[0.99933213,0.00012872662,0.00004080487,0.000103199265,0.00036375644,0.00003130934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010484452,0.0010586865,0.00053990376,0.002981645,0.0008545935,0.0030162625,0.0006405941,0.00053248374,0.082309194],"category_scores_gemma":[0.0028555968,0.0003631955,0.0009275259,0.0025970049,0.00027227038,0.0019332966,0.0012158632,0.00095571554,0.06671877],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004211648,0.000046410205,0.0007198282,0.00025547776,0.00003892479,0.000063272826,0.00018488162,0.00045709993,0.009999689,0.011181047,0.21078762,0.76622367],"study_design_scores_gemma":[0.000021526663,0.00004423552,0.0045560044,0.0002549925,0.00008061686,0.00047857405,0.00035068297,0.013545723,0.015467148,0.023431744,0.9417278,0.000041042593],"about_ca_topic_score_codex":0.0011059084,"about_ca_topic_score_gemma":0.001812971,"teacher_disagreement_score":0.082309194,"about_ca_system_score_codex":0.00052950496,"about_ca_system_score_gemma":0.00080540683,"threshold_uncertainty_score":0.27535164},"labels":[],"label_agreement":null},{"id":"W3027466640","doi":"10.1007/s11042-020-09030-1","title":"Automatic construction of domain sentiment lexicon for semantic disambiguation","year":2020,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"National Natural Science Foundation of China","keywords":"Lexicon; Computer science; Sentiment analysis; Ambiguity; Natural language processing; Artificial intelligence; Domain (mathematical analysis); Mathematics","score_opus":0.037324365301474954,"score_gpt":0.27874984167008215,"score_spread":0.2414254763686072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3027466640","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08854553,0.0014779172,0.8424422,0.0009860089,0.0010031069,0.00093265437,0.014288704,0.018122574,0.032201238],"genre_scores_gemma":[0.31229177,0.0012356513,0.62971514,0.00035691695,0.0003144109,0.0007608406,0.04462863,0.0018142418,0.008882481],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918824,0.00017215278,0.00012042214,0.00018954008,0.00022683288,0.00010281006],"domain_scores_gemma":[0.9988696,0.00023975689,0.00008485756,0.000103834485,0.0006289882,0.00007292498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006883863,0.0010274241,0.00095207425,0.0062362924,0.0015393207,0.0023872287,0.0006974576,0.0006386677,0.006032084],"category_scores_gemma":[0.0029406366,0.00050566136,0.0010414708,0.0036625983,0.00034871095,0.0021232166,0.0018987354,0.0010501412,0.0064680665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004224393,0.00044624228,0.006517904,0.0011976165,0.0001698327,0.0012516642,0.00094987324,0.002866862,0.15025364,0.029782522,0.10700232,0.6991391],"study_design_scores_gemma":[0.0002134888,0.00032869296,0.01923264,0.0006659372,0.0006180559,0.0030830116,0.0034906592,0.3763872,0.17865168,0.07792275,0.33912683,0.0002789767],"about_ca_topic_score_codex":0.0019770043,"about_ca_topic_score_gemma":0.0035095168,"teacher_disagreement_score":0.0062362924,"about_ca_system_score_codex":0.00071526895,"about_ca_system_score_gemma":0.0020380178,"threshold_uncertainty_score":0.020179331},"labels":[],"label_agreement":null},{"id":"W30283642","doi":"10.1002/evl3.9","title":"Hierarchical versus Flat Classification of Emotions in Text","year":2010,"lang":"en","type":"article","venue":"North American Chapter of the Association for Computational Linguistics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Task (project management); Polarity (international relations); Artificial intelligence; Neutrality; Hierarchical database model; Machine learning; Pattern recognition (psychology); Data mining; Natural language processing; Engineering","score_opus":0.02534671414079879,"score_gpt":0.288668501698871,"score_spread":0.2633217875580722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W30283642","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.695809,0.003976427,0.090496495,0.003414454,0.0009040656,0.0013503054,0.034948155,0.004255372,0.16484576],"genre_scores_gemma":[0.9561647,0.0004670082,0.026244812,0.00023294847,0.00025926632,0.00033302364,0.009479703,0.0001742246,0.0066441875],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99896455,0.00025615958,0.00013881248,0.00019775024,0.00029321993,0.00014948013],"domain_scores_gemma":[0.9960199,0.0021384289,0.0004937918,0.00026782145,0.00078272074,0.00029733047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075858366,0.00052704196,0.00030120995,0.0037434371,0.00072214514,0.0019474206,0.00054845156,0.0006245879,0.017233366],"category_scores_gemma":[0.007433729,0.00009678444,0.00036499102,0.0026034962,0.0008373816,0.003708951,0.0012157562,0.0006932634,0.004993873],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0029948067,0.00032687275,0.08776324,0.0023406905,0.00018601307,0.0014716185,0.010511006,0.003767943,0.042926,0.036833514,0.09067166,0.7202067],"study_design_scores_gemma":[0.0001335662,0.0007669736,0.4836673,0.0012804678,0.00026050652,0.002343067,0.022473047,0.19234423,0.015144279,0.13369347,0.14765589,0.00023715611],"about_ca_topic_score_codex":0.0019637155,"about_ca_topic_score_gemma":0.00253072,"teacher_disagreement_score":0.017233366,"about_ca_system_score_codex":0.0007893389,"about_ca_system_score_gemma":0.00029936736,"threshold_uncertainty_score":0.0576514},"labels":[],"label_agreement":null},{"id":"W3030443804","doi":"10.17577/ijertv9is050615","title":"Sentiment Analysis of Customers Opinions on Hotel Stays using Voted Classifier","year":2020,"lang":"en","type":"article","venue":"International Journal of Engineering Research and","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hatch (Canada)","funders":"","keywords":"Sentiment analysis; Download; Classifier (UML); Computer science; Advertising; Artificial intelligence; Operations research; Information retrieval; World Wide Web; Business; Engineering","score_opus":0.11065046056799255,"score_gpt":0.38041320170129367,"score_spread":0.2697627411333011,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3030443804","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90292865,0.0005972431,0.08007346,0.00063628965,0.0007440605,0.00028324913,0.0025049949,0.00076429296,0.011467686],"genre_scores_gemma":[0.97279876,0.00013359293,0.019702204,0.00006381637,0.00014025517,0.0000771534,0.0026696504,0.000028006927,0.0043866374],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924695,0.00013663061,0.000065774504,0.000115541836,0.00031704977,0.00011803955],"domain_scores_gemma":[0.99898773,0.00020367182,0.00006228783,0.000042646396,0.0006621141,0.000041546333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007900139,0.00031485283,0.0006707957,0.00091310736,0.0003847301,0.000805882,0.0003199397,0.00036639755,0.0016482077],"category_scores_gemma":[0.0015265325,0.000102488695,0.0006865796,0.000630011,0.00011473342,0.00042861194,0.00026344345,0.0004391983,0.00092307635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027231502,0.0010327257,0.08224482,0.00048836245,0.00049941987,0.0007787,0.0008974671,0.020350551,0.13231614,0.001901223,0.03297106,0.7237964],"study_design_scores_gemma":[0.000063916465,0.00067653705,0.08479713,0.00004772271,0.0002305763,0.00034916663,0.0008843943,0.8722186,0.032319147,0.0007866731,0.007552706,0.00007345221],"about_ca_topic_score_codex":0.002608935,"about_ca_topic_score_gemma":0.0031613277,"teacher_disagreement_score":0.002608935,"about_ca_system_score_codex":0.0003985817,"about_ca_system_score_gemma":0.00032724283,"threshold_uncertainty_score":0.005513847},"labels":[],"label_agreement":null},{"id":"W3030953462","doi":"10.1002/spe.2853","title":"<scp>Senti‐eSystem</scp>: A sentiment‐based <scp>eSystem</scp>‐using hybridized fuzzy and deep neural network for measuring customer satisfaction","year":2020,"lang":"en","type":"article","venue":"Software Practice and Experience","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":81,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Customer satisfaction; Computer science; Artificial neural network; Fuzzy logic; Lexicon; Artificial intelligence; Polarity (international relations); Sentiment analysis; Data mining; Business; Marketing; Chemistry","score_opus":0.04259811995502385,"score_gpt":0.28623527109359637,"score_spread":0.2436371511385725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3030953462","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32141113,0.0006063317,0.60206187,0.001804713,0.00063860137,0.00068545394,0.007950062,0.03229287,0.03254899],"genre_scores_gemma":[0.8115838,0.00023769749,0.16619249,0.0005350019,0.00012789329,0.00024745282,0.0058583925,0.0003465249,0.014870807],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998373,0.000023275934,0.000013342824,0.00003466983,0.000075908734,0.000015507763],"domain_scores_gemma":[0.9997695,0.00003600738,0.00002208518,0.000024471445,0.00013259426,0.000015347436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037680147,0.00052434887,0.00028050848,0.0006591675,0.0002669343,0.0004824276,0.00057617144,0.0004807672,0.0061397273],"category_scores_gemma":[0.00078017457,0.00015602788,0.0003564449,0.00045415893,0.00020132256,0.0008172083,0.00052571134,0.00048007845,0.0016044413],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007699228,0.00034389432,0.012523085,0.000365344,0.00023157346,0.00064045796,0.00025049498,0.035190556,0.11623711,0.00471204,0.07204878,0.75668675],"study_design_scores_gemma":[0.00003099791,0.00021881745,0.013513223,0.000028730867,0.000060424267,0.00019937965,0.00009858456,0.9240335,0.045791026,0.004113465,0.011848359,0.00006349068],"about_ca_topic_score_codex":0.0043697096,"about_ca_topic_score_gemma":0.0054427898,"teacher_disagreement_score":0.0061397273,"about_ca_system_score_codex":0.00038592744,"about_ca_system_score_gemma":0.00030879345,"threshold_uncertainty_score":0.020539403},"labels":[],"label_agreement":null},{"id":"W3033366604","doi":"10.1007/s10489-020-01760-x","title":"Is position important? deep multi-task learning for aspect-based sentiment analysis","year":2020,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Sentiment analysis; Autoencoder; Artificial intelligence; Intuition; Position (finance); Benchmark (surveying); Deep learning; Task (project management); Machine learning; Natural language processing","score_opus":0.036586195650200534,"score_gpt":0.29073118694108285,"score_spread":0.2541449912908823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033366604","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43783128,0.0036536914,0.53003466,0.006784743,0.0010795081,0.00012300482,0.0022616833,0.0022365088,0.015994946],"genre_scores_gemma":[0.9565568,0.00039027096,0.037567124,0.0003959674,0.0003148881,0.000033747932,0.001382894,0.00010538502,0.003252976],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953747,0.000138277,0.000027565791,0.00012205231,0.00007126455,0.000103497645],"domain_scores_gemma":[0.9987601,0.0005947964,0.00017285963,0.000093467126,0.00027983362,0.00009898578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011412753,0.00066823565,0.0005363592,0.00059871416,0.0004727463,0.0013388098,0.00071764254,0.00086122024,0.0026334343],"category_scores_gemma":[0.004747292,0.00023194081,0.00042880565,0.0007972931,0.0003228893,0.0026241352,0.00089185004,0.0015849672,0.0013519382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001268308,0.0005924024,0.026711062,0.00036766112,0.00021599341,0.0003084491,0.00063039985,0.02908131,0.037211407,0.012882663,0.060142886,0.8305875],"study_design_scores_gemma":[0.000040359846,0.00009809561,0.0058016395,0.000049044884,0.00009077164,0.00008286232,0.00026028435,0.94876647,0.006282614,0.032978248,0.0055275145,0.00002212634],"about_ca_topic_score_codex":0.0026750127,"about_ca_topic_score_gemma":0.004135902,"teacher_disagreement_score":0.0026750127,"about_ca_system_score_codex":0.0005245988,"about_ca_system_score_gemma":0.00057141914,"threshold_uncertainty_score":0.008809686},"labels":[],"label_agreement":null},{"id":"W3033372266","doi":"10.1109/access.2020.3000093","title":"Exploring Key Issues Affecting African Mobile eCommerce Applications Using Sentiment and Thematic Analysis","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sentiment analysis; Thematic analysis; Computer science; Key (lock); Thematic map; The Internet; World Wide Web; Data science; Internet privacy; Artificial intelligence; Qualitative research; Computer security; Sociology","score_opus":0.21325630107417065,"score_gpt":0.3653650367845088,"score_spread":0.15210873571033817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033372266","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9709863,0.0012806698,0.017336968,0.0012838567,0.00008216091,0.00023497338,0.00087404007,0.0000894516,0.007831624],"genre_scores_gemma":[0.97060823,0.0011818834,0.025446385,0.00016688502,0.000082660015,0.000098718556,0.00068522105,0.000028427556,0.0017015357],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9989666,0.00045615592,0.00010589857,0.000087517314,0.00029324458,0.00009070901],"domain_scores_gemma":[0.9932079,0.003426006,0.0012718636,0.00013625735,0.0018263347,0.00013166248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022753228,0.000399051,0.0002921425,0.0030330631,0.0008096154,0.0018889765,0.00020819354,0.00029974376,0.00094943505],"category_scores_gemma":[0.00772711,0.00013410623,0.00041300437,0.0027999564,0.00031541105,0.0013331097,0.00044242907,0.00041422906,0.00029341562],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004891268,0.00030330598,0.39466235,0.0020712472,0.00028047047,0.0013849812,0.017481644,0.0036094936,0.06015215,0.0033884265,0.008375498,0.50780135],"study_design_scores_gemma":[0.000029129274,0.0005885631,0.71186715,0.0008411206,0.0004592783,0.0015326411,0.06084381,0.12250808,0.042335484,0.0047937604,0.054060772,0.00014017032],"about_ca_topic_score_codex":0.0032235656,"about_ca_topic_score_gemma":0.0073662996,"teacher_disagreement_score":0.0032235656,"about_ca_system_score_codex":0.0005379615,"about_ca_system_score_gemma":0.00056352816,"threshold_uncertainty_score":0.0120331645},"labels":[],"label_agreement":null},{"id":"W3034757448","doi":"10.1145/3397271.3401183","title":"Leveraging Transitions of Emotions for Sarcasm Detection","year":2020,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Sarcasm; Sadness; Happiness; Surprise; Computer science; Emotion detection; Sentiment analysis; Affective computing; Artificial intelligence; Natural language processing; Emotion classification; Cognitive psychology; Psychology; Anger; Social psychology; Irony; Linguistics; Emotion recognition","score_opus":0.05658513576308524,"score_gpt":0.26544380041793586,"score_spread":0.20885866465485062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034757448","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.695728,0.0028352987,0.27689153,0.0011281503,0.00055505434,0.0003784539,0.0034442982,0.0032460822,0.015793197],"genre_scores_gemma":[0.9529894,0.00038591903,0.040421028,0.0001693468,0.0002157779,0.00014969936,0.0028400295,0.00008268903,0.002746206],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994702,0.00012262158,0.000042562748,0.00018960513,0.00010745658,0.000067653586],"domain_scores_gemma":[0.9980708,0.00083483465,0.00037656643,0.00012934099,0.00046921222,0.000119274686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007034457,0.0007902213,0.0004795168,0.0016996154,0.0003284834,0.0008642161,0.00042121595,0.00061645964,0.0015051594],"category_scores_gemma":[0.004104435,0.00016192456,0.00042585595,0.00079156284,0.00024617324,0.0013304815,0.0005895447,0.0010053287,0.00168253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019589772,0.000894984,0.11449884,0.0007489848,0.00031935782,0.00049245835,0.0017296884,0.008450039,0.12022547,0.002784706,0.02154667,0.72634995],"study_design_scores_gemma":[0.00010583869,0.0014841838,0.3095506,0.00028647063,0.00025844382,0.0011262216,0.0029931825,0.598726,0.045915823,0.018830718,0.02050136,0.00022127594],"about_ca_topic_score_codex":0.0005709831,"about_ca_topic_score_gemma":0.001617559,"teacher_disagreement_score":0.0016996154,"about_ca_system_score_codex":0.0002209124,"about_ca_system_score_gemma":0.00017266982,"threshold_uncertainty_score":0.005035281},"labels":[],"label_agreement":null},{"id":"W3035119815","doi":"10.1109/jbhi.2020.3001216","title":"Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach","year":2020,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":379,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"National Social Science Fund of China; National Natural Science Foundation of China; Fundamental Research Funds for the Central Universities; Nanjing Science and Technology Commission; National Science Foundation","keywords":"Computer science; Artificial intelligence; Sentiment analysis; Social media; Recurrent neural network; Coronavirus disease 2019 (COVID-19); Natural language processing; Deep learning; Machine learning; Artificial neural network; Public health; Decision tree; The Internet; Support vector machine; World Wide Web; Medicine; Disease","score_opus":0.2666507334657404,"score_gpt":0.4049349140857755,"score_spread":0.13828418062003506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035119815","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6692168,0.0010921621,0.31655538,0.0020607184,0.0003963042,0.00021632863,0.0014581692,0.0015626956,0.0074414196],"genre_scores_gemma":[0.95380175,0.00031089695,0.039718993,0.00015721511,0.0002461542,0.00009358583,0.0017539625,0.000046025896,0.003871416],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958247,0.00013395624,0.000029466102,0.0001040625,0.000059897626,0.00009011727],"domain_scores_gemma":[0.9990569,0.00049284636,0.00013415139,0.000035525096,0.0002409731,0.00003969295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009390738,0.00075583154,0.0004056606,0.0011841942,0.00042467806,0.000688549,0.0004981231,0.0006485231,0.0013383898],"category_scores_gemma":[0.0020766035,0.0001945182,0.0008300885,0.0006675969,0.00021470035,0.0010115202,0.0004888964,0.0009570431,0.0006181799],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011519896,0.0011672446,0.040608756,0.00046612858,0.00037242236,0.0009550811,0.0021190785,0.09973584,0.06457489,0.0057282983,0.01747351,0.76564676],"study_design_scores_gemma":[0.0000101679625,0.00005352331,0.003164538,0.000010269645,0.00003889791,0.000023442015,0.00017484315,0.9900587,0.0038337482,0.0016846836,0.0009383533,0.000008897466],"about_ca_topic_score_codex":0.0044176043,"about_ca_topic_score_gemma":0.0052566463,"teacher_disagreement_score":0.0044176043,"about_ca_system_score_codex":0.0006600278,"about_ca_system_score_gemma":0.0004884574,"threshold_uncertainty_score":0.008783758},"labels":[],"label_agreement":null},{"id":"W3036460473","doi":"","title":"Du bon usage d'ingrédients linguistiques spéciaux pour classer des recettes exceptionnelles","year":2020,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; National Bank of Canada; Concordia University","funders":"","keywords":"Humanities; Physics; Art; Philosophy","score_opus":0.043599798922067064,"score_gpt":0.2669145432762165,"score_spread":0.22331474435414944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036460473","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.119801134,0.0017240422,0.85526484,0.0016475827,0.00040190195,0.00032199657,0.0011272968,0.00731913,0.012392068],"genre_scores_gemma":[0.53252035,0.001144577,0.4445216,0.0005334181,0.00016228324,0.00037927233,0.0016820007,0.0008146456,0.018241787],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987821,0.00022361746,0.000107747175,0.00045931022,0.00032718547,0.000100008765],"domain_scores_gemma":[0.997142,0.0010904329,0.00021100436,0.0005363898,0.00091944664,0.00010073902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019162009,0.0014828708,0.0011532409,0.0016842087,0.00088270433,0.0031880294,0.0014218816,0.001666009,0.0067809466],"category_scores_gemma":[0.007812159,0.00083911326,0.0015434589,0.0012584686,0.0008304667,0.004622988,0.0013773626,0.0023222321,0.003442644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00081194064,0.00029873927,0.021293996,0.00062146696,0.00039946582,0.0003578955,0.0017792223,0.04320753,0.060754683,0.013250776,0.0093063535,0.8479179],"study_design_scores_gemma":[0.00007496362,0.000355958,0.015347748,0.00028540695,0.00041715382,0.00065483904,0.0007783421,0.8661701,0.048673138,0.025019338,0.04207158,0.00015147487],"about_ca_topic_score_codex":0.011680425,"about_ca_topic_score_gemma":0.023360156,"teacher_disagreement_score":0.011680425,"about_ca_system_score_codex":0.0013769784,"about_ca_system_score_gemma":0.001475728,"threshold_uncertainty_score":0.02322489},"labels":[],"label_agreement":null},{"id":"W3036568251","doi":"10.1016/j.inffus.2020.06.002","title":"Deep learning based emotion analysis of microblog texts","year":2020,"lang":"en","type":"article","venue":"Information Fusion","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":149,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"National Key Research and Development Program of China; Natural Science Foundation of Shandong Province; Key Technology Research and Development Program of Shandong; National Natural Science Foundation of China","keywords":"Word2vec; Computer science; Microblogging; Artificial intelligence; Sentiment analysis; Convolutional neural network; Natural language processing; Support vector machine; Word (group theory); Feature (linguistics); Social media; Convolution (computer science); Feature vector; Artificial neural network; Pattern recognition (psychology); World Wide Web; Mathematics","score_opus":0.012355064780184378,"score_gpt":0.22579551539646575,"score_spread":0.21344045061628136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036568251","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6013192,0.0024595277,0.38068047,0.0013898499,0.0006284368,0.00015303309,0.0023764006,0.0018760621,0.009116975],"genre_scores_gemma":[0.95377386,0.0005621277,0.037937894,0.0001086599,0.00023000035,0.000054441905,0.0018152106,0.000059812013,0.0054580187],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996722,0.00006881891,0.00002412327,0.000058657602,0.000100118894,0.00007621214],"domain_scores_gemma":[0.99941623,0.00020417293,0.00006928036,0.000026962844,0.00025161405,0.00003165132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005396774,0.0005539678,0.00036523485,0.0012089412,0.00025496996,0.000740794,0.00027493763,0.00041254025,0.0015870845],"category_scores_gemma":[0.0014390121,0.00010342787,0.0004345694,0.00083281874,0.00014673291,0.00097031845,0.00045017974,0.00076044846,0.0009891688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008694697,0.0007825097,0.013539608,0.00034110612,0.00024544384,0.0003274859,0.0005880944,0.026303928,0.1422445,0.0033309623,0.018132733,0.7932942],"study_design_scores_gemma":[0.000013730553,0.00016122889,0.014675873,0.000030291932,0.000092055765,0.00008866494,0.00029273066,0.95376235,0.023599166,0.0033015597,0.003958308,0.000024056631],"about_ca_topic_score_codex":0.0019475605,"about_ca_topic_score_gemma":0.0029868674,"teacher_disagreement_score":0.0019475605,"about_ca_system_score_codex":0.0003935795,"about_ca_system_score_gemma":0.00025860683,"threshold_uncertainty_score":0.0053093433},"labels":[],"label_agreement":null},{"id":"W3037031158","doi":"10.1609/aaai.v34i10.7173","title":"Trimodal Attention Module for Multimodal Sentiment Analysis (Student Abstract)","year":2020,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Modalities; Utterance; Feature (linguistics); Artificial intelligence; Task (project management); Multimodality; Sentiment analysis; Process (computing); Modality (human–computer interaction); Information fusion; Fusion; Artificial neural network; Natural language processing; Machine learning; Linguistics","score_opus":0.0922357668056459,"score_gpt":0.327130698095469,"score_spread":0.23489493128982314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037031158","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11858909,0.0018246382,0.8490056,0.001208302,0.00073515,0.00028194985,0.0011101577,0.009791041,0.0174541],"genre_scores_gemma":[0.792063,0.00061952276,0.18020461,0.00085991423,0.00038849484,0.00027018666,0.0016938054,0.00024817663,0.023652341],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997626,0.000036097557,0.000009343963,0.00008781479,0.00004272969,0.00006146459],"domain_scores_gemma":[0.9998041,0.000038122387,0.000016665224,0.000023078248,0.00009561965,0.000022515622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005111665,0.0008753918,0.00047524573,0.0007291197,0.00035831434,0.0005755342,0.0008185605,0.00083728426,0.012042343],"category_scores_gemma":[0.0008424317,0.00018741409,0.0007960128,0.000538829,0.00025286738,0.0009879143,0.0009130186,0.00091363594,0.0030790912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004952936,0.00036243987,0.0026742094,0.00020554551,0.00022145807,0.00022812562,0.00016687608,0.017027102,0.1134287,0.0042078397,0.022930838,0.83805174],"study_design_scores_gemma":[0.000033376247,0.00030405665,0.00607097,0.000049050876,0.00019301967,0.00014606326,0.00009113109,0.9141828,0.061321016,0.007860691,0.009707282,0.00004050483],"about_ca_topic_score_codex":0.004352905,"about_ca_topic_score_gemma":0.0063773156,"teacher_disagreement_score":0.012042343,"about_ca_system_score_codex":0.00055942533,"about_ca_system_score_gemma":0.00051206775,"threshold_uncertainty_score":0.040285647},"labels":[],"label_agreement":null},{"id":"W3040884840","doi":"10.1057/s41599-020-0523-3","title":"Sentiments and emotions evoked by news headlines of coronavirus disease (COVID-19) outbreak","year":2020,"lang":"en","type":"article","venue":"Humanities and Social Sciences Communications","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":278,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sadness; Anger; Coronavirus disease 2019 (COVID-19); Psychology; Sentence; Sentiment analysis; Coronavirus; News media; Social psychology; Disease; Medicine; Linguistics; Sociology; Media studies; Infectious disease (medical specialty); Computer science","score_opus":0.3228928790328312,"score_gpt":0.39407785476027946,"score_spread":0.07118497572744825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3040884840","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9908562,0.0004047377,0.0006974991,0.00017257662,0.00010027787,0.00007140163,0.0043017697,0.000041155683,0.0033545105],"genre_scores_gemma":[0.99049103,0.00045805072,0.0016382588,0.000088639135,0.00018594688,0.000091545866,0.0055289236,0.000016267595,0.0015013521],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99958426,0.00011656925,0.000059857644,0.00005569681,0.0001320337,0.00005165985],"domain_scores_gemma":[0.9968746,0.0015284,0.0008081304,0.000055478802,0.00060599693,0.00012745477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060615776,0.00030598976,0.00021008251,0.0017163656,0.00035410674,0.0008877326,0.000083236715,0.00024019634,0.0016594527],"category_scores_gemma":[0.0034901972,0.00005858785,0.00024960184,0.00126826,0.00019521776,0.00040114857,0.00035013404,0.00025435493,0.00043298776],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0035020623,0.00033473942,0.72891665,0.0031343852,0.0004432093,0.0018972085,0.013126612,0.0013582266,0.05707045,0.00066223345,0.027551735,0.1620025],"study_design_scores_gemma":[0.000017473903,0.00024595612,0.97634107,0.00014562122,0.00013949366,0.00036346662,0.006601205,0.0030724306,0.0043849573,0.00012636115,0.008531737,0.000030147601],"about_ca_topic_score_codex":0.0018342049,"about_ca_topic_score_gemma":0.0027949095,"teacher_disagreement_score":0.0018342049,"about_ca_system_score_codex":0.00031393472,"about_ca_system_score_gemma":0.00015189957,"threshold_uncertainty_score":0.005551398},"labels":[],"label_agreement":null},{"id":"W3042081424","doi":"10.1017/s1351324920000376","title":"Negation detection for sentiment analysis: A case study in Spanish","year":2020,"lang":"en","type":"article","venue":"Natural Language Engineering","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Negation; Computer science; Natural language processing; Sentiment analysis; Scope (computer science); Artificial intelligence; Identification (biology); Task (project management); Context (archaeology); Programming language","score_opus":0.011398076280530871,"score_gpt":0.25877069535249575,"score_spread":0.24737261907196487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3042081424","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94309145,0.0010525736,0.027733028,0.0042672916,0.00020742853,0.0003392085,0.0012005136,0.00066490564,0.021443503],"genre_scores_gemma":[0.96353126,0.0007975101,0.02725538,0.00071977364,0.00008423092,0.000104448365,0.0010894829,0.00033640963,0.006081631],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981489,0.000820623,0.00013198175,0.00019121288,0.0005768647,0.00013041873],"domain_scores_gemma":[0.9907273,0.004992947,0.0004519163,0.00043461277,0.0031091478,0.00028404046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033138602,0.00051362324,0.00034553837,0.0011342084,0.0012312082,0.0013719591,0.0006932235,0.0011349849,0.001696068],"category_scores_gemma":[0.0149469515,0.0001331724,0.0003641557,0.0013269136,0.0008062138,0.00076269824,0.00086754985,0.00071048114,0.0006390769],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013733093,0.0017724575,0.22924161,0.0021496569,0.0001931768,0.0669272,0.05179111,0.013179717,0.047789223,0.011015917,0.062013127,0.5125535],"study_design_scores_gemma":[0.00047884436,0.00091173366,0.2258507,0.0011563213,0.0003529171,0.027738744,0.08349016,0.1435168,0.07639556,0.017812755,0.42202115,0.00027439956],"about_ca_topic_score_codex":0.02677096,"about_ca_topic_score_gemma":0.02742238,"teacher_disagreement_score":0.02677096,"about_ca_system_score_codex":0.0017864378,"about_ca_system_score_gemma":0.0013020668,"threshold_uncertainty_score":0.053230226},"labels":[],"label_agreement":null},{"id":"W3042185317","doi":"10.1155/2020/5824873","title":"Aspect-Level Sentiment Analysis Based on Position Features Using Multilevel Interactive Bidirectional GRU and Attention Mechanism","year":2020,"lang":"en","type":"article","venue":"Discrete Dynamics in Nature and Society","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Xihua University; Chengdu Science and Technology Bureau; Department of Science and Technology of Sichuan Province; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Computer science; Sentiment analysis; Sentence; Artificial intelligence; Position (finance); Context (archaeology); Polarity (international relations); Word (group theory); Word embedding; Mechanism (biology); Artificial neural network; Embedding; Machine learning; Natural language processing; Pattern recognition (psychology); Mathematics","score_opus":0.014576419084567206,"score_gpt":0.2801459802758872,"score_spread":0.26556956119132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3042185317","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28991285,0.0007430974,0.7003441,0.00036361217,0.00012991861,0.00017185113,0.00026676874,0.0018472831,0.0062205913],"genre_scores_gemma":[0.95700127,0.00015009464,0.040449213,0.00006432051,0.000035921814,0.0000724857,0.00020243393,0.000041034604,0.0019831317],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971956,0.00005394681,0.000017338338,0.00008462737,0.00006712451,0.000057518107],"domain_scores_gemma":[0.9997198,0.00007415581,0.00005203991,0.000025431207,0.000106149375,0.000022386677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004272036,0.0007115127,0.00057376345,0.000766173,0.00026949894,0.0005968007,0.00076207996,0.0004242397,0.0015039007],"category_scores_gemma":[0.0012483454,0.00020021247,0.0007192159,0.00059742416,0.00030532505,0.001002725,0.0006485061,0.00060476846,0.00047268972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00065260124,0.00035988388,0.019140733,0.0002631965,0.00029547254,0.0005092786,0.0006955777,0.13810194,0.09597795,0.009747489,0.0047475537,0.7295084],"study_design_scores_gemma":[0.000008257292,0.00009260037,0.0033230768,0.000010248357,0.000051206905,0.000048234764,0.00004726444,0.98754954,0.005440882,0.002865689,0.0005492895,0.000013599883],"about_ca_topic_score_codex":0.004903372,"about_ca_topic_score_gemma":0.0058278586,"teacher_disagreement_score":0.004903372,"about_ca_system_score_codex":0.00058344804,"about_ca_system_score_gemma":0.00042933962,"threshold_uncertainty_score":0.009749651},"labels":[],"label_agreement":null},{"id":"W3043958695","doi":"10.1109/access.2020.3011123","title":"NeedFull – a Tweet Analysis Platform to Study Human Needs During the COVID-19 Pandemic in New York State","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Communications Research Centre Canada; University of Ottawa","funders":"","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Computer science; Visualization; Scalability; State (computer science); Data science; Data visualization; Data collection; 2019-20 coronavirus outbreak; World Wide Web; Computer security; Database; Artificial intelligence; Sociology; Virology","score_opus":0.15405692185144843,"score_gpt":0.37064471666162896,"score_spread":0.21658779481018053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043958695","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.56473476,0.0018146685,0.13612504,0.007400367,0.0011837843,0.0039695147,0.14209618,0.0967218,0.04595386],"genre_scores_gemma":[0.7417378,0.0010876101,0.13341615,0.0019372611,0.00036233294,0.0032407367,0.081142984,0.0017111715,0.035363883],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967945,0.000089336085,0.000030645824,0.00008221741,0.000080740836,0.000037569396],"domain_scores_gemma":[0.9986363,0.000659229,0.00011712766,0.0001395327,0.00023584766,0.00021191308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008381471,0.0006428129,0.0003244705,0.0014738012,0.0006597573,0.00079689245,0.00059843145,0.0006456354,0.007156816],"category_scores_gemma":[0.002741896,0.00019274322,0.0002851322,0.0006849542,0.00019196924,0.002212864,0.0015046721,0.00059730123,0.0016571428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027367743,0.0009113697,0.09318474,0.0025952435,0.00039533994,0.0016804881,0.01374577,0.00536711,0.066236496,0.008453474,0.41208166,0.3926116],"study_design_scores_gemma":[0.00053107645,0.0016062774,0.20728797,0.00046307087,0.00028356267,0.00089695293,0.01394459,0.20292588,0.027636722,0.020825421,0.52311003,0.0004883881],"about_ca_topic_score_codex":0.009466633,"about_ca_topic_score_gemma":0.02394408,"teacher_disagreement_score":0.009466633,"about_ca_system_score_codex":0.0005422726,"about_ca_system_score_gemma":0.0006851125,"threshold_uncertainty_score":0.023941934},"labels":[],"label_agreement":null},{"id":"W3044533399","doi":"10.2196/22734","title":"Health, Psychosocial, and Social Issues Emanating From the COVID-19 Pandemic Based on Social Media Comments: Text Mining and Thematic Analysis Approach","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Saskatchewan; Dalhousie University","funders":"","keywords":"Thematic analysis; Social media; Psychological intervention; Pandemic; Perception; Social issues; Public health; Coronavirus disease 2019 (COVID-19)","score_opus":0.08122896001787273,"score_gpt":0.3778356966039714,"score_spread":0.2966067365860987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3044533399","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9466756,0.0007312209,0.023477145,0.0021400303,0.00012085067,0.0029666896,0.014788377,0.0001658184,0.00893426],"genre_scores_gemma":[0.92150605,0.0009377625,0.05943056,0.0004311486,0.00020481735,0.004136176,0.010176913,0.000057009536,0.0031195274],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.99841535,0.00065988477,0.0002042844,0.00022338808,0.00035048687,0.00014656059],"domain_scores_gemma":[0.9898169,0.007254829,0.0013353379,0.00021243501,0.001161618,0.00021886532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035186603,0.0005271598,0.00039738507,0.010183938,0.0012333996,0.0017167578,0.00073395914,0.00069809664,0.001461743],"category_scores_gemma":[0.0076920358,0.00019341668,0.00076326285,0.0076584527,0.00084854313,0.0017342708,0.0015490509,0.0006174695,0.00039754497],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007242533,0.0007365765,0.293082,0.0076527563,0.00028439154,0.0052919835,0.2803234,0.0031497562,0.029645855,0.008357108,0.019193916,0.3515579],"study_design_scores_gemma":[0.000053730593,0.00034411036,0.4050341,0.0017064323,0.00029688378,0.0016524112,0.4640171,0.045209568,0.010633937,0.010856814,0.060004786,0.00019012208],"about_ca_topic_score_codex":0.0037519452,"about_ca_topic_score_gemma":0.0065969364,"teacher_disagreement_score":0.010183938,"about_ca_system_score_codex":0.0013147859,"about_ca_system_score_gemma":0.0012897889,"threshold_uncertainty_score":0.01860863},"labels":[],"label_agreement":null},{"id":"W3048471839","doi":"10.2196/19618","title":"YouTube Video Comments on Healthy Eating: Descriptive and Predictive Analysis","year":2020,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Thematic analysis; Psychology; Perception; Categorization; Food choice; Descriptive statistics; Structural equation modeling; Social psychology; Applied psychology; Medicine; Qualitative research; Computer science; Geography; Sociology; Artificial intelligence; Mathematics","score_opus":0.06051326839206452,"score_gpt":0.3066721666207334,"score_spread":0.24615889822866885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048471839","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.983405,0.00006244031,0.00038773753,0.00008963948,0.000010741907,0.00026592147,0.013978747,0.000025837422,0.0017739639],"genre_scores_gemma":[0.98202765,0.00014316432,0.0015589288,0.000038474354,0.000023196004,0.0007610008,0.014314684,0.000010720134,0.0011222418],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99919003,0.00018907823,0.00011363973,0.0000904562,0.00031136177,0.00010546485],"domain_scores_gemma":[0.9872907,0.007094344,0.0022865634,0.00031978512,0.0025672703,0.00044135228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001391908,0.00025657628,0.00028810554,0.00331156,0.00053393835,0.00069378165,0.00034277333,0.00030203004,0.0023205006],"category_scores_gemma":[0.0123908315,0.000121855905,0.00034645322,0.0034903185,0.0003061342,0.00075444015,0.00072123215,0.00043701375,0.00066889386],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026481206,0.00022582826,0.97336745,0.00022017326,0.000050981707,0.0002687771,0.0027368856,0.00047009953,0.0005975844,0.00018139757,0.0031607014,0.018455349],"study_design_scores_gemma":[0.000008896366,0.00017171392,0.98579675,0.00006706885,0.00002588219,0.0001907997,0.005206801,0.0060278648,0.0005374319,0.00008293769,0.0018657787,0.000018118928],"about_ca_topic_score_codex":0.019786518,"about_ca_topic_score_gemma":0.024293462,"teacher_disagreement_score":0.019786518,"about_ca_system_score_codex":0.0008569779,"about_ca_system_score_gemma":0.0005972739,"threshold_uncertainty_score":0.0393427},"labels":[],"label_agreement":null},{"id":"W3048777071","doi":"10.22215/datapower.v2017i0.130","title":"News recommendation based on opinion mining: an approach to assist the automatic classification of controversies","year":2017,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Information overload; Personalization; Reading (process); World Wide Web; Computer science; The Internet; Sentiment analysis; Selection (genetic algorithm); Recommender system; Task (project management); Semantics (computer science); Public opinion; Internet privacy; Political science; Artificial intelligence; Engineering; Politics; Law","score_opus":0.09071241884948761,"score_gpt":0.3309656466654663,"score_spread":0.2402532278159787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048777071","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06471392,0.0019933404,0.9074328,0.0020804794,0.00051585917,0.0015162668,0.005774785,0.006472299,0.009500268],"genre_scores_gemma":[0.22427456,0.0011173016,0.7590815,0.0005113521,0.00061267026,0.0008080067,0.0089503415,0.0001782689,0.004466011],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99710184,0.00069893926,0.0003785813,0.000695109,0.0008847544,0.00024075992],"domain_scores_gemma":[0.9929333,0.003412342,0.0007571532,0.00044116325,0.0022508907,0.00020520986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036336419,0.0016622365,0.0015113241,0.009349065,0.0010762336,0.0028650914,0.0022425628,0.0018324719,0.0025126964],"category_scores_gemma":[0.011784005,0.00051056076,0.0020774296,0.0062386245,0.0005084642,0.0024230878,0.0011358996,0.0020666686,0.003460724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045400744,0.00078652805,0.01978207,0.00059987477,0.00041044602,0.00048284014,0.001314787,0.0048286733,0.013244851,0.003908822,0.023730995,0.9304562],"study_design_scores_gemma":[0.00013008641,0.0004007765,0.021548849,0.0004179536,0.00068975537,0.00078238006,0.0026432641,0.87767994,0.021586029,0.02921616,0.044734262,0.00017051383],"about_ca_topic_score_codex":0.0070949267,"about_ca_topic_score_gemma":0.010947618,"teacher_disagreement_score":0.009349065,"about_ca_system_score_codex":0.00097247487,"about_ca_system_score_gemma":0.0011662847,"threshold_uncertainty_score":0.019216776},"labels":[],"label_agreement":null},{"id":"W3049351399","doi":"10.32628/cseit206375","title":"Traffic Detection using Sentimental Analysis","year":2020,"lang":"en","type":"article","venue":"International Journal of Scientific Research in Computer Science Engineering and Information Technology","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Expansive; Sentiment analysis; Computer science; Data science; Social media; Internet privacy; World Wide Web; Information retrieval; Artificial intelligence","score_opus":0.03562062216535736,"score_gpt":0.31884800022580423,"score_spread":0.28322737806044684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3049351399","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6607583,0.0012286762,0.22960134,0.0017853364,0.0013274013,0.002187949,0.018033177,0.0061904327,0.07888742],"genre_scores_gemma":[0.8982972,0.0008024432,0.07947117,0.0002391959,0.00047300663,0.0005170684,0.008738865,0.00019951859,0.011261508],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905306,0.00012161296,0.000102879516,0.00015730053,0.00043270545,0.00013248989],"domain_scores_gemma":[0.998171,0.00024563255,0.00027248074,0.00004861885,0.0011857658,0.00007650825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084747816,0.0007961084,0.0006241445,0.006383032,0.00064545666,0.0016964686,0.00035456193,0.0004971197,0.0037009863],"category_scores_gemma":[0.0026536046,0.00018454315,0.00077035587,0.002991796,0.00023525019,0.00121861,0.00048624678,0.00055506633,0.0034754006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006789671,0.0006923123,0.12076885,0.0008709243,0.00034915283,0.0009991108,0.000988931,0.007902241,0.0805571,0.0040871,0.049227517,0.73287773],"study_design_scores_gemma":[0.00006635523,0.0006224262,0.26338658,0.0002410362,0.0003686495,0.0010389261,0.0041429666,0.6187083,0.04334073,0.007659849,0.06023108,0.00019313503],"about_ca_topic_score_codex":0.0033989036,"about_ca_topic_score_gemma":0.003419542,"teacher_disagreement_score":0.006383032,"about_ca_system_score_codex":0.0006937849,"about_ca_system_score_gemma":0.0005012051,"threshold_uncertainty_score":0.012380958},"labels":[],"label_agreement":null},{"id":"W3064274253","doi":"10.5815/ijem.2020.04.02","title":"The Multimedia Sentiment Model Based on Online Homestay Reviews","year":2020,"lang":"en","type":"article","venue":"International Journal of Engineering and Manufacturing","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Government of Jiangsu Province; National Natural Science Foundation of China","keywords":"Sentiment analysis; Computer science; Word2vec; Artificial intelligence; Convolutional neural network; Support vector machine; Image (mathematics); Decision tree; Cluster analysis; Machine learning; Pattern recognition (psychology)","score_opus":0.02136477646101821,"score_gpt":0.25946308424199327,"score_spread":0.23809830778097507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3064274253","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31574056,0.00101003,0.6623457,0.0011547795,0.000401093,0.00039432765,0.0018130348,0.001974042,0.015166376],"genre_scores_gemma":[0.92086816,0.00052200514,0.067617066,0.00015977376,0.00024911974,0.00024453722,0.0016447157,0.00006143712,0.008633229],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974734,0.00003798827,0.000017791843,0.00008189275,0.00008467623,0.000030408442],"domain_scores_gemma":[0.9997235,0.000041747302,0.000034420198,0.000010769193,0.00017635741,0.000013160472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037082948,0.0007465421,0.00038609857,0.0008233649,0.00023887196,0.00059356954,0.00046338674,0.00035921505,0.001687701],"category_scores_gemma":[0.000934406,0.00016700641,0.000716917,0.00047529762,0.00016332448,0.0008189576,0.00022228915,0.00038562645,0.00083355635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011063876,0.0004403975,0.026798544,0.00042669039,0.00043056777,0.00080645434,0.0006923248,0.18170483,0.06908102,0.009824714,0.023496328,0.6851918],"study_design_scores_gemma":[0.000009340473,0.00005805135,0.0043788576,0.000007878906,0.000039808794,0.000063622705,0.000053308522,0.98943454,0.0033178742,0.0010002276,0.0016225252,0.00001392045],"about_ca_topic_score_codex":0.0069546844,"about_ca_topic_score_gemma":0.007062434,"teacher_disagreement_score":0.0069546844,"about_ca_system_score_codex":0.000600375,"about_ca_system_score_gemma":0.00033426937,"threshold_uncertainty_score":0.013828397},"labels":[],"label_agreement":null},{"id":"W3080989070","doi":"10.1145/3410566.3410594","title":"A practical application for sentiment analysis on social media textual data","year":2020,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Sentiment analysis; Computer science; Social media; Data science; Information retrieval; Feeling; Product (mathematics); World Wide Web; Natural language processing; Psychology","score_opus":0.17243507795899576,"score_gpt":0.3873082962926292,"score_spread":0.21487321833363343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3080989070","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01407486,0.00031658998,0.9537303,0.0018016911,0.00045231334,0.00087524037,0.003386419,0.015693795,0.009668793],"genre_scores_gemma":[0.103179194,0.00043833195,0.8855104,0.00045203912,0.00031151742,0.00068507466,0.0024286385,0.0006552901,0.006339505],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981414,0.00057694974,0.00019736393,0.00029143866,0.00069312786,0.000099716504],"domain_scores_gemma":[0.99672604,0.0015708605,0.00020989537,0.00031366738,0.0010917175,0.000087775385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025669155,0.0010659101,0.00061512645,0.0031781103,0.0010698243,0.0017339975,0.00061903044,0.0009974687,0.013977073],"category_scores_gemma":[0.01103842,0.00046407524,0.0012987958,0.0027471294,0.0004002149,0.0017669793,0.0011662729,0.0011581293,0.009738095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031998407,0.00022601594,0.0067541366,0.0007845018,0.00020817689,0.000809327,0.0010879504,0.0043693013,0.059381653,0.013737326,0.06863752,0.84368414],"study_design_scores_gemma":[0.00016983139,0.00027987023,0.012987617,0.00039122716,0.00014442354,0.0019249807,0.0022197105,0.64565825,0.05718273,0.06884661,0.21003324,0.00016150992],"about_ca_topic_score_codex":0.0014759979,"about_ca_topic_score_gemma":0.0018544103,"teacher_disagreement_score":0.013977073,"about_ca_system_score_codex":0.00043262448,"about_ca_system_score_gemma":0.0005967373,"threshold_uncertainty_score":0.046757996},"labels":[],"label_agreement":null},{"id":"W3081287165","doi":"10.1177/2158244020951268","title":"Using Machine Learning for Analyzing Sentiment Orientations Toward Eight Countries","year":2020,"lang":"en","type":"article","venue":"SAGE Open","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Ingroups and outgroups; Outgroup; China; Politics; Power (physics); Political science; Sentiment analysis; Negativity effect; Kingdom; Sociology; Social psychology; Political economy; Psychology; Law","score_opus":0.10015654532273371,"score_gpt":0.34869693996637613,"score_spread":0.24854039464364241,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081287165","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8296177,0.00095836946,0.15141445,0.0008626107,0.00034615447,0.0005435296,0.0034382837,0.0009673328,0.011851512],"genre_scores_gemma":[0.9347147,0.00030929578,0.06110891,0.000069950016,0.00013701088,0.0002606052,0.0025034545,0.000027258404,0.0008688182],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985331,0.0005970487,0.00017214067,0.00020888002,0.00034806534,0.00014075288],"domain_scores_gemma":[0.9960433,0.0027155243,0.0005037403,0.00013974762,0.0005045013,0.00009324178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024155763,0.000858937,0.0006384182,0.0073333755,0.0006589455,0.0017337095,0.0003410599,0.0005835849,0.001643915],"category_scores_gemma":[0.008014587,0.00017705989,0.0009483579,0.0048416057,0.00031073074,0.0008094805,0.0005173437,0.0008644917,0.0008065707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066565775,0.0010062234,0.19347285,0.00040926412,0.0006648926,0.00038566193,0.0010075029,0.031892423,0.009491097,0.003275256,0.009950256,0.74777895],"study_design_scores_gemma":[0.00006653374,0.00035881548,0.1246559,0.00011715518,0.00019977252,0.00022438934,0.0015125091,0.85541415,0.005519522,0.006784714,0.005075762,0.000070748785],"about_ca_topic_score_codex":0.0024751602,"about_ca_topic_score_gemma":0.0022383237,"teacher_disagreement_score":0.0073333755,"about_ca_system_score_codex":0.000787747,"about_ca_system_score_gemma":0.0005324203,"threshold_uncertainty_score":0.012775004},"labels":[],"label_agreement":null},{"id":"W3082192086","doi":"10.1163/26660393-bja10008","title":"Generic Structure and Rhetorical Relations of Online Book Reviews in English, Japanese and Chinese","year":2020,"lang":"en","type":"article","venue":"Contrastive Pragmatics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Rhetorical question; Linguistics; Coherence (philosophical gambling strategy); Genre analysis; Computer science; Sociology; Philosophy","score_opus":0.023005744126562495,"score_gpt":0.2725865304527214,"score_spread":0.2495807863261589,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3082192086","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9817477,0.0005968961,0.004129409,0.00018476357,0.000016966173,0.000070627895,0.00020325139,0.00004027416,0.013010012],"genre_scores_gemma":[0.9971077,0.00010302887,0.001930172,0.000026307976,0.000018339044,0.000037460704,0.00016439523,0.000015262698,0.00059742294],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99780864,0.0010663945,0.00018430274,0.00034087463,0.00047749229,0.00012234757],"domain_scores_gemma":[0.97745687,0.012083714,0.005375141,0.0008259696,0.003720495,0.000537795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024089047,0.0002013552,0.0003475376,0.004312595,0.0014884952,0.002403841,0.0003203337,0.00041533954,0.0013919723],"category_scores_gemma":[0.017237242,0.00024157227,0.0002110325,0.0028386794,0.002183559,0.0020015684,0.0010987986,0.0005028297,0.00016924123],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008377734,0.00021044796,0.21141979,0.0027772377,0.00016839681,0.0033208372,0.55420023,0.0010384006,0.070919506,0.04594923,0.002478905,0.10667922],"study_design_scores_gemma":[0.00005463454,0.00021846377,0.871724,0.00030415243,0.00014964619,0.0012843123,0.07970751,0.007379537,0.008196194,0.0061657755,0.02467604,0.00013969939],"about_ca_topic_score_codex":0.0062420396,"about_ca_topic_score_gemma":0.011259249,"teacher_disagreement_score":0.0062420396,"about_ca_system_score_codex":0.0017278107,"about_ca_system_score_gemma":0.0009756985,"threshold_uncertainty_score":0.012739658},"labels":[],"label_agreement":null},{"id":"W3084513432","doi":"10.1007/978-981-15-8731-3_5","title":"Emotion Detection on Twitter Textual Data","year":2020,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Sadness; Sentiment analysis; Computer science; Social media; Anger; Feeling; Emotion detection; Information retrieval; Data science; World Wide Web; Natural language processing; Artificial intelligence; Psychology; Social psychology; Emotion recognition","score_opus":0.06260205722995042,"score_gpt":0.301748586922208,"score_spread":0.23914652969225758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3084513432","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53615856,0.006105436,0.32985246,0.004230542,0.0027755026,0.00088491343,0.05700454,0.014163875,0.048824076],"genre_scores_gemma":[0.7371638,0.0028404286,0.16514054,0.0005222171,0.0016357257,0.00062425365,0.05659823,0.0005379443,0.034936946],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995739,0.000075821095,0.000036951264,0.0000791311,0.00017597337,0.000058265265],"domain_scores_gemma":[0.99909294,0.00038483815,0.000100854326,0.00006717257,0.0003060879,0.000048140955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051608775,0.00062804436,0.0004062618,0.002372369,0.00043214925,0.0012306237,0.00031393924,0.00047959265,0.003227245],"category_scores_gemma":[0.0023242624,0.00014039227,0.00044608186,0.0018017705,0.00013603595,0.0012551969,0.00049898203,0.00054942264,0.0049078153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005536644,0.0002523702,0.018595252,0.00060545316,0.00010753735,0.0005056187,0.0004126704,0.0039795563,0.09183254,0.0025991402,0.0890698,0.7914864],"study_design_scores_gemma":[0.00005382874,0.0004212913,0.07761528,0.00030030077,0.00023207214,0.0015166306,0.0022242104,0.64983267,0.13555692,0.009923255,0.12216303,0.0001605352],"about_ca_topic_score_codex":0.0015421881,"about_ca_topic_score_gemma":0.0028546115,"teacher_disagreement_score":0.003227245,"about_ca_system_score_codex":0.0003746545,"about_ca_system_score_gemma":0.00023074067,"threshold_uncertainty_score":0.010796189},"labels":[],"label_agreement":null},{"id":"W3088709138","doi":"10.1109/compe49325.2020.9200054","title":"Long Short Term Memory (LSTM) based Deep Learning for Sentiment Analysis of English and Spanish Data","year":2020,"lang":"en","type":"article","venue":"2020 International Conference on Computational Performance Evaluation (ComPE)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University of Edmonton","funders":"","keywords":"Computer science; Dropout (neural networks); Deep learning; Artificial intelligence; Sentiment analysis; Recurrent neural network; Long short term memory; Regularization (linguistics); Term (time); Machine learning; Artificial neural network; Field (mathematics); Domain (mathematical analysis); Natural language processing","score_opus":0.11620505070300648,"score_gpt":0.3522960819755168,"score_spread":0.23609103127251035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088709138","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4564558,0.0040228846,0.51759684,0.0014684035,0.0007309256,0.00021443336,0.002348666,0.005122073,0.012040044],"genre_scores_gemma":[0.86972076,0.001043709,0.118872896,0.00028267232,0.000133239,0.00010654129,0.0034202687,0.00014372577,0.006276161],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997571,0.00007691728,0.000018985727,0.000048787308,0.00005553654,0.00004270758],"domain_scores_gemma":[0.99965954,0.00010788421,0.000035571076,0.000029370625,0.00015015827,0.00001753633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007535115,0.00064027734,0.00031225657,0.00039401985,0.00022306984,0.00041030627,0.00043050334,0.0003817007,0.0017978479],"category_scores_gemma":[0.0015995791,0.000117717085,0.00045515358,0.00046000432,0.00013606384,0.0007775883,0.00041554705,0.0008756867,0.0009671712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067441113,0.00047219085,0.006484908,0.00039934876,0.000230516,0.00032945265,0.00036410426,0.05866086,0.068745196,0.003028702,0.018143153,0.84246725],"study_design_scores_gemma":[0.000017193153,0.00014394456,0.0028751672,0.000024585865,0.000049167156,0.000033878372,0.00012406053,0.97338086,0.016861405,0.0027916655,0.0036826201,0.000015390664],"about_ca_topic_score_codex":0.0040584547,"about_ca_topic_score_gemma":0.0066377856,"teacher_disagreement_score":0.0040584547,"about_ca_system_score_codex":0.00032331963,"about_ca_system_score_gemma":0.00039046555,"threshold_uncertainty_score":0.008069634},"labels":[],"label_agreement":null},{"id":"W3092005458","doi":"10.3390/iot1020014","title":"Sentiment Analysis on Twitter Data of World Cup Soccer Tournament Using Machine Learning","year":2020,"lang":"en","type":"article","venue":"IoT","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":92,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Artificial intelligence; Computer science; Sentiment analysis; Natural language processing; WordNet; Lexical analysis; Support vector machine; Naive Bayes classifier; Lexicon; Machine learning; Parsing; Stop words; Random forest; Preprocessor","score_opus":0.14971659179755684,"score_gpt":0.34274720639763406,"score_spread":0.19303061460007723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092005458","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86431044,0.00054417015,0.017931215,0.0010970441,0.0004927376,0.00074054225,0.091808654,0.0021970898,0.020878166],"genre_scores_gemma":[0.8676392,0.0005031377,0.02919312,0.00016773822,0.00025020566,0.00078778545,0.09221198,0.00010501487,0.009141814],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994973,0.000080722035,0.00007208368,0.000082198356,0.00018493149,0.000082796134],"domain_scores_gemma":[0.99939454,0.00013953281,0.00008570868,0.000040462986,0.0002960619,0.000043700464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048469892,0.00055617397,0.00036496582,0.002456553,0.00042571797,0.00062178174,0.00024468312,0.00033044055,0.0020856904],"category_scores_gemma":[0.0012613558,0.0000729852,0.000523135,0.0016869869,0.00014325345,0.00043771017,0.00037682214,0.00033302436,0.0018228455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00169667,0.001046373,0.2674533,0.0020185737,0.000432987,0.002775233,0.0017767759,0.01169308,0.07307068,0.0027964688,0.10828247,0.5269573],"study_design_scores_gemma":[0.000083549035,0.0008788922,0.65138227,0.00023993824,0.00018244715,0.0013460311,0.0049650003,0.19372763,0.04721281,0.0021765472,0.09763316,0.00017170538],"about_ca_topic_score_codex":0.0040767766,"about_ca_topic_score_gemma":0.006146947,"teacher_disagreement_score":0.0040767766,"about_ca_system_score_codex":0.00039671056,"about_ca_system_score_gemma":0.00031709316,"threshold_uncertainty_score":0.0081061125},"labels":[],"label_agreement":null},{"id":"W3092970899","doi":"","title":"Machine Learning Evaluation of the Echo-Chamber Effect in Medical Forums.","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; University of Ottawa","funders":"","keywords":"Echo (communications protocol); Class (philosophy); Core (optical fiber); Computer science; Artificial intelligence; Unit (ring theory); Machine learning; Natural language processing; Psychology; Mathematics education; Computer security; Telecommunications","score_opus":0.08509234080077223,"score_gpt":0.23443639509585767,"score_spread":0.14934405429508546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092970899","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90767777,0.0012544689,0.07496904,0.0012587844,0.00048337164,0.0005463117,0.0013930325,0.00058489636,0.011832287],"genre_scores_gemma":[0.9873876,0.000068985195,0.011226477,0.000058595913,0.00010203903,0.00006743384,0.00059227657,0.000012468438,0.00048406792],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98761034,0.00889514,0.0004884385,0.0008889818,0.00181168,0.0003053677],"domain_scores_gemma":[0.8587167,0.120511435,0.007485139,0.0037819205,0.007353886,0.0021509496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024833748,0.000685575,0.00056404964,0.0019383951,0.0005871019,0.0015189368,0.00065607985,0.0014220021,0.0013557586],"category_scores_gemma":[0.084111266,0.00012819753,0.0005776599,0.0008619049,0.0005635075,0.0019704124,0.0011318501,0.0011955258,0.00040002624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0058321976,0.0037952156,0.4146626,0.00090547797,0.0011333704,0.00025720376,0.0013780753,0.12340362,0.010868356,0.00817786,0.012933464,0.41665265],"study_design_scores_gemma":[0.00012846867,0.0017965614,0.0957075,0.000083466315,0.00018032937,0.000107633285,0.0003926368,0.888364,0.006196727,0.0047661876,0.0022131691,0.000063322695],"about_ca_topic_score_codex":0.0012375696,"about_ca_topic_score_gemma":0.001529106,"teacher_disagreement_score":0.024833748,"about_ca_system_score_codex":0.00093602063,"about_ca_system_score_gemma":0.00050684024,"threshold_uncertainty_score":0.13133502},"labels":[],"label_agreement":null},{"id":"W3093184805","doi":"10.22215/etd/2019-13842","title":"A comprehensive topic-model based hybrid sentiment analysis system","year":2019,"lang":"en","type":"dissertation","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Sentiment analysis; Variety (cybernetics); Pipeline (software); Artificial intelligence; Machine learning; Topic model; Data science; Coherence (philosophical gambling strategy); Data mining","score_opus":0.017883169380100854,"score_gpt":0.2716602899728654,"score_spread":0.2537771205927645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093184805","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06256806,0.0009070304,0.7906917,0.0010858751,0.0004999779,0.0011264903,0.012559138,0.11684098,0.013720797],"genre_scores_gemma":[0.3316409,0.00066638563,0.6065851,0.00074781734,0.00049625186,0.0015171675,0.037119865,0.0020639393,0.01916264],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995691,0.000059428665,0.0000527344,0.00014809855,0.00013082508,0.000039724626],"domain_scores_gemma":[0.9994832,0.00008413361,0.000040797262,0.000059086757,0.0002850093,0.000047740246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000879807,0.000898854,0.0007826881,0.0016334614,0.0006076828,0.0012871964,0.0009402771,0.0006717146,0.005573296],"category_scores_gemma":[0.0015008363,0.00039186317,0.0008245289,0.001060948,0.0001396012,0.0020122707,0.0009933936,0.0007167531,0.0067540905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011224574,0.0008217338,0.00973068,0.000662459,0.00047062186,0.0006431178,0.0007837279,0.012379096,0.13630955,0.005915615,0.15234536,0.6788156],"study_design_scores_gemma":[0.00011502188,0.0002152021,0.0054515935,0.00004310631,0.0001787264,0.0002474753,0.00022263383,0.9121727,0.02819849,0.0054106186,0.047645383,0.000099043435],"about_ca_topic_score_codex":0.0029637136,"about_ca_topic_score_gemma":0.0032016372,"teacher_disagreement_score":0.005573296,"about_ca_system_score_codex":0.0005373558,"about_ca_system_score_gemma":0.0007284026,"threshold_uncertainty_score":0.018644571},"labels":[],"label_agreement":null},{"id":"W3097009866","doi":"10.5539/mas.v14n11p36","title":"Online Messages Sentiments Analysis Based on Long Short-Term Memory","year":2020,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Construct (python library); Long short term memory; Pandemic; Sentiment analysis; China; Term (time); Sample (material); Social media; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Computer science; Psychology; History; Artificial neural network; Artificial intelligence; Recurrent neural network; Medicine; World Wide Web","score_opus":0.035902928344225954,"score_gpt":0.28403600010666813,"score_spread":0.24813307176244218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3097009866","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9554594,0.00042511127,0.035358205,0.00027651375,0.00011310491,0.00015158813,0.0013082859,0.0002857871,0.0066219885],"genre_scores_gemma":[0.99148786,0.00013876092,0.006485453,0.000024730498,0.00004825599,0.000042975673,0.0006249216,0.0000088548495,0.0011382204],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997781,0.00004154971,0.000025947393,0.000044190732,0.00007593009,0.000034273788],"domain_scores_gemma":[0.9993437,0.0001967977,0.00010255547,0.000023309905,0.0003006444,0.00003291085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004535027,0.00030740118,0.00027666282,0.0014219811,0.00022496501,0.0005279731,0.00018530224,0.00021684365,0.0014065552],"category_scores_gemma":[0.0016550914,0.000068398076,0.00037698544,0.0009930236,0.00013620767,0.0007326943,0.00016866466,0.00024741233,0.0005633053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016670435,0.0007317492,0.26670468,0.00043196187,0.00040114182,0.0010058137,0.0010835096,0.016368581,0.08853993,0.0032144533,0.01067752,0.6091736],"study_design_scores_gemma":[0.000034816956,0.0005573965,0.2870379,0.000044862598,0.00021208504,0.0003597731,0.0010005403,0.683219,0.020569293,0.0026537862,0.0042538173,0.000056785353],"about_ca_topic_score_codex":0.0021320528,"about_ca_topic_score_gemma":0.0026830747,"teacher_disagreement_score":0.0021320528,"about_ca_system_score_codex":0.00035820433,"about_ca_system_score_gemma":0.00016178103,"threshold_uncertainty_score":0.0047053695},"labels":[],"label_agreement":null},{"id":"W3098931820","doi":"10.18653/v1/2020.nlpcss-1.5","title":"I miss you babe: Analyzing Emotion Dynamics During COVID-19 Pandemic","year":2020,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Disgust; Anger; Transition (genetics); Sentiment analysis; Emotion classification; Dynamics (music); Construct (python library); Psychology; Cognitive psychology; Computer science; Artificial intelligence; Social psychology","score_opus":0.04968700986881276,"score_gpt":0.29228012632791583,"score_spread":0.2425931164591031,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3098931820","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9895654,0.00026721778,0.0054686232,0.00080804364,0.000095193056,0.000040556675,0.0014843146,0.00012656393,0.0021440908],"genre_scores_gemma":[0.99511504,0.00016021903,0.0026562298,0.000072170726,0.000064797096,0.000027967902,0.0011430976,0.000019497911,0.0007408898],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99979836,0.0000668378,0.000010710409,0.00004662779,0.000032931443,0.00004452464],"domain_scores_gemma":[0.99927634,0.0003782553,0.00012356088,0.000039457642,0.00010757486,0.00007485324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004180827,0.0003358912,0.00021315747,0.0006745048,0.00032432732,0.00070176437,0.00017427343,0.00044708417,0.0007768051],"category_scores_gemma":[0.0024790743,0.00009781133,0.00027588423,0.0005437816,0.00017800038,0.0008531526,0.000388687,0.0005255663,0.00048375523],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016479705,0.00045572707,0.73492444,0.0003878939,0.00026992313,0.0014650162,0.0065249116,0.025169522,0.032481335,0.0029668377,0.022307632,0.1713989],"study_design_scores_gemma":[0.00001962151,0.00029661378,0.6494999,0.000083587955,0.00010180443,0.0003232388,0.010069233,0.3222393,0.0054369164,0.0029426976,0.008915288,0.000071796596],"about_ca_topic_score_codex":0.005173542,"about_ca_topic_score_gemma":0.0061341315,"teacher_disagreement_score":0.005173542,"about_ca_system_score_codex":0.00027707804,"about_ca_system_score_gemma":0.00016680555,"threshold_uncertainty_score":0.010286868},"labels":[],"label_agreement":null},{"id":"W3101412217","doi":"10.5220/0010145702890295","title":"Personalised Recommendation Systems and the Impact of COVID-19: Perspectives, Opportunities and Challenges","year":2020,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Computer science; Swift; Pandemic; Data science; Recommender system; 2019-20 coronavirus outbreak; Machine learning","score_opus":0.20021287226586393,"score_gpt":0.33187440946250135,"score_spread":0.13166153719663742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3101412217","genre_codex":"commentary","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09112035,0.31957078,0.116520606,0.37128463,0.0029845007,0.00014092211,0.0007461501,0.0005301639,0.09710199],"genre_scores_gemma":[0.79230344,0.1266791,0.05557172,0.008583512,0.004540171,0.000089026784,0.0005260207,0.00008517726,0.011621762],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9957541,0.0020294548,0.00016553228,0.0004496451,0.0012573586,0.00034389287],"domain_scores_gemma":[0.98467016,0.010857169,0.0005637934,0.00078639085,0.002429929,0.0006925733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00769426,0.00046980046,0.0009585539,0.0012046443,0.0010122022,0.007696492,0.0015497021,0.0043359958,0.0041291653],"category_scores_gemma":[0.013225921,0.00037251392,0.00051596086,0.0021593403,0.0023184526,0.010342131,0.0023281567,0.004155196,0.001657463],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029608005,0.00037617073,0.013471237,0.0010599217,0.00013471898,0.0004186566,0.0011962042,0.014939632,0.0018089435,0.23090948,0.03882429,0.69656473],"study_design_scores_gemma":[0.00008868108,0.0006282529,0.015207848,0.0016406041,0.00010118806,0.0013425525,0.008294796,0.2342884,0.0030681507,0.41783935,0.31722817,0.00027196622],"about_ca_topic_score_codex":0.0050855856,"about_ca_topic_score_gemma":0.003947917,"teacher_disagreement_score":0.007696492,"about_ca_system_score_codex":0.0018911886,"about_ca_system_score_gemma":0.0011880589,"threshold_uncertainty_score":0.040691614},"labels":[],"label_agreement":null},{"id":"W3105333265","doi":"10.48550/arxiv.1906.02331","title":"OutdoorSent: Sentiment Analysis of Urban Outdoor Images by Using Semantic and Deep Features","year":2019,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Sentiment analysis; Generalization; Context (archaeology); Artificial intelligence; Information retrieval; Data science; Machine learning; Geography","score_opus":0.025212592817863977,"score_gpt":0.18803978823163553,"score_spread":0.16282719541377155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3105333265","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7920022,0.0016235623,0.14252603,0.0010581495,0.00070331467,0.0006701718,0.024246117,0.011221061,0.025949419],"genre_scores_gemma":[0.8886929,0.00041323694,0.07085726,0.00032020212,0.00022753353,0.00025432534,0.029248105,0.00022908792,0.00975738],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998411,0.00002276172,0.000008812914,0.000048237875,0.00003604535,0.000043069354],"domain_scores_gemma":[0.99984455,0.00002944253,0.000032412372,0.000020164756,0.000055401735,0.000017936447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029263785,0.0012779995,0.00034536363,0.0011673811,0.00025175096,0.0005940934,0.00045407042,0.00050115015,0.0026831157],"category_scores_gemma":[0.0006366783,0.0001417636,0.0005547265,0.00074571016,0.00015383118,0.0007747347,0.0005042362,0.00046720935,0.0013899833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012600037,0.0010838102,0.07269788,0.00071425847,0.0007065905,0.00059624715,0.00055486686,0.024725344,0.09457637,0.0022320983,0.104292095,0.6965603],"study_design_scores_gemma":[0.00012191571,0.000704544,0.086521395,0.0000987683,0.00026986163,0.00037496854,0.0010364767,0.84125984,0.03872752,0.0040100035,0.026805868,0.00006893344],"about_ca_topic_score_codex":0.005090749,"about_ca_topic_score_gemma":0.015014162,"teacher_disagreement_score":0.005090749,"about_ca_system_score_codex":0.00045030826,"about_ca_system_score_gemma":0.00027071653,"threshold_uncertainty_score":0.01012224},"labels":[],"label_agreement":null},{"id":"W3107845597","doi":"10.1080/1206212x.2020.1851501","title":"A two-level deep learning approach for emotion recognition in Arabic news headlines","year":2020,"lang":"en","type":"article","venue":"International Journal of Computers and Applications","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université TÉLUQ","funders":"Qatar National Library","keywords":"Computer science; Sadness; Sentiment analysis; Artificial intelligence; Disgust; Machine learning; Decision tree; Trigram; Naive Bayes classifier; Happiness; Convolutional neural network; Surprise; Random forest; Support vector machine; Anger; Natural language processing; Psychology; Social psychology","score_opus":0.06159624203719277,"score_gpt":0.3058940405224249,"score_spread":0.24429779848523214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107845597","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55081135,0.0020145928,0.42966804,0.001360716,0.0005098383,0.00019547145,0.0014676013,0.0029847368,0.01098766],"genre_scores_gemma":[0.92641664,0.00047578063,0.06183269,0.0002624943,0.00013822349,0.00010966021,0.0018867137,0.000059233458,0.008818541],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984014,0.000024504514,0.000010457852,0.000041672458,0.000029476283,0.000053780863],"domain_scores_gemma":[0.9998248,0.000043490145,0.00002075243,0.000009997888,0.00008671563,0.000014234632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003056591,0.0006460281,0.00035615568,0.0006016541,0.00029016775,0.00056548073,0.00044536107,0.0005729339,0.00141538],"category_scores_gemma":[0.00070326874,0.00019956181,0.000495707,0.00040198155,0.0001462786,0.00057009724,0.00035530986,0.0010177933,0.000757455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000988614,0.0007179091,0.012928292,0.00019136308,0.00015779758,0.00056660845,0.0005440609,0.061062243,0.057968035,0.0017389039,0.01632953,0.8468065],"study_design_scores_gemma":[0.000010226204,0.00009445168,0.004387262,0.0000140619695,0.000029100116,0.000038828304,0.00013265703,0.9862233,0.0067534996,0.0006739762,0.0016300693,0.000012621873],"about_ca_topic_score_codex":0.006188824,"about_ca_topic_score_gemma":0.0071461885,"teacher_disagreement_score":0.006188824,"about_ca_system_score_codex":0.00048697295,"about_ca_system_score_gemma":0.00027440392,"threshold_uncertainty_score":0.012305558},"labels":[],"label_agreement":null},{"id":"W3108275114","doi":"10.1177/1461445620966923","title":"The interplay of complexity and subjectivity in opinionated discourse","year":2020,"lang":"en","type":"article","venue":"Discourse Studies","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Subjectivity; Computer science; Lexicon; Natural language processing; Argumentation theory; Linguistics; Sentiment analysis; Corpus linguistics; Artificial intelligence; Text corpus; Epistemology","score_opus":0.0702093281791194,"score_gpt":0.3817233659634742,"score_spread":0.3115140377843548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3108275114","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5903734,0.0016819966,0.38347018,0.002280669,0.00010740065,0.00030540748,0.00079428597,0.00022479479,0.020761896],"genre_scores_gemma":[0.96782213,0.00031743827,0.030441081,0.000091329675,0.00012835198,0.00021989105,0.0002869493,0.000074249794,0.00061867223],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.9826166,0.009716142,0.0010496881,0.0016805712,0.0045620883,0.00037483813],"domain_scores_gemma":[0.84072095,0.1353491,0.012080851,0.0045626992,0.006308898,0.0009775118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014286386,0.0005564595,0.00083813426,0.007335705,0.0020462284,0.007199082,0.00093184813,0.00082901,0.0017218582],"category_scores_gemma":[0.0803749,0.00052700425,0.00071009895,0.0047453465,0.00865518,0.0123733375,0.003738172,0.001657015,0.00019421593],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006864198,0.00016134274,0.07549623,0.002072566,0.00043624235,0.0008886291,0.16388358,0.01641697,0.022746572,0.52357703,0.00204608,0.19158842],"study_design_scores_gemma":[0.000062320054,0.00022400319,0.10495252,0.000604352,0.000235436,0.0005922746,0.033158965,0.0809543,0.008572114,0.74588066,0.0244533,0.00030977392],"about_ca_topic_score_codex":0.0013156026,"about_ca_topic_score_gemma":0.0012600565,"teacher_disagreement_score":0.014286386,"about_ca_system_score_codex":0.0023952445,"about_ca_system_score_gemma":0.00114404,"threshold_uncertainty_score":0.07555455},"labels":[],"label_agreement":null},{"id":"W3111354974","doi":"10.1109/smc42975.2020.9282942","title":"Analyzing Machine Learning Algorithms for Sentiments in Arabic Text","year":2020,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Artificial intelligence; Arabic; Data pre-processing; Feature selection; Preprocessor; Sentiment analysis; Machine learning; Natural language processing; Real estate; Support vector machine; Statistical classification; Deep learning; Data science; Linguistics","score_opus":0.041045087148508665,"score_gpt":0.2888834318624437,"score_spread":0.24783834471393504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111354974","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38509127,0.0037639001,0.58154035,0.0025166804,0.0006702005,0.0006113837,0.0023241031,0.0036355748,0.019846542],"genre_scores_gemma":[0.7461854,0.0011943082,0.24146324,0.00034479375,0.00035633665,0.00037843708,0.003972606,0.00015454066,0.0059503275],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991399,0.0002528801,0.00011038921,0.00017529327,0.00023409189,0.000087486675],"domain_scores_gemma":[0.99771714,0.0010826201,0.00020867611,0.00012202072,0.00082273514,0.000046840785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018255867,0.00095813203,0.0005177665,0.0024661417,0.000511595,0.0014090006,0.00047165385,0.0005827997,0.0026867366],"category_scores_gemma":[0.006962665,0.00017598251,0.0006840253,0.0014811646,0.00025534193,0.0011957439,0.0004252144,0.0010307864,0.0024384765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039668754,0.00027288677,0.019942012,0.00034720683,0.00021288122,0.00021411652,0.0005226981,0.029166475,0.017861895,0.0043380493,0.014390493,0.91233456],"study_design_scores_gemma":[0.000024956198,0.00015919525,0.011635544,0.000079620666,0.000053410706,0.00012790391,0.00050628826,0.9611757,0.011207067,0.007854974,0.007148448,0.000026960419],"about_ca_topic_score_codex":0.0016628403,"about_ca_topic_score_gemma":0.0020255453,"teacher_disagreement_score":0.0026867366,"about_ca_system_score_codex":0.00070415897,"about_ca_system_score_gemma":0.00047971614,"threshold_uncertainty_score":0.00965476},"labels":[],"label_agreement":null},{"id":"W3112639224","doi":"10.1007/978-3-030-63846-7_68","title":"Algerian Dialect Translation Applied on COVID-19 Social Media Comments","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Misinformation; Coronavirus disease 2019 (COVID-19); Translation (biology); Process (computing); Social media; Computer science; Arabic; Linguistics; Natural language processing; Artificial intelligence; Embedding; Speech recognition; Medicine; World Wide Web; Philosophy; Computer security; Biology","score_opus":0.055358080876738276,"score_gpt":0.265294304882306,"score_spread":0.20993622400556772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112639224","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5302828,0.00239451,0.09412213,0.005358235,0.0058377734,0.0006381807,0.03489416,0.008730336,0.31774196],"genre_scores_gemma":[0.79244214,0.001104772,0.087095566,0.00048381655,0.0007204516,0.0003005359,0.023463551,0.0019301716,0.09245892],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916255,0.00028337378,0.00007658433,0.00013282336,0.00026016714,0.00008455523],"domain_scores_gemma":[0.99831164,0.00029856473,0.00008432198,0.00012635929,0.001138342,0.000040766696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008819578,0.00054686127,0.00024942603,0.0016711059,0.00086168764,0.0014997727,0.00023380344,0.0002825605,0.019178549],"category_scores_gemma":[0.0026165135,0.00012897674,0.00021471774,0.0013082474,0.00034895772,0.0008244676,0.0009277575,0.00055995106,0.009874563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011424144,0.00020289021,0.008362815,0.0010601224,0.00005486706,0.00082794076,0.0073221633,0.0012488533,0.046646215,0.022471357,0.123660795,0.78699964],"study_design_scores_gemma":[0.00007916919,0.00024851432,0.040638387,0.00043899933,0.00009762981,0.0013909483,0.0095845405,0.018291427,0.06151015,0.0051459814,0.8624681,0.000106155196],"about_ca_topic_score_codex":0.007363699,"about_ca_topic_score_gemma":0.0065785004,"teacher_disagreement_score":0.019178549,"about_ca_system_score_codex":0.0008098755,"about_ca_system_score_gemma":0.000989068,"threshold_uncertainty_score":0.06415862},"labels":[],"label_agreement":null},{"id":"W3113814228","doi":"10.1145/3395035.3425964","title":"Group Performance Prediction with Limited Context","year":2020,"lang":"en","type":"article","venue":"Companion Publication of the 2020 International Conference on Multimodal Interaction","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of the Fraser Valley","funders":"","keywords":"Conversation; Computer science; Task (project management); Artificial intelligence; Natural language processing; Context (archaeology); Graph; Machine learning; Predictive modelling; Speech recognition; Linguistics; Theoretical computer science; Engineering","score_opus":0.06001396133382283,"score_gpt":0.2811997498870267,"score_spread":0.22118578855320384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3113814228","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9304417,0.00086381275,0.054835398,0.0007508773,0.00014130502,0.00012449043,0.0043295138,0.0013138177,0.007199113],"genre_scores_gemma":[0.9889854,0.000081944025,0.00651907,0.00003958188,0.000054310814,0.00008084913,0.002545391,0.000045982917,0.0016473674],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907315,0.00034725154,0.000030219278,0.0002869664,0.00011929318,0.00014307103],"domain_scores_gemma":[0.99629253,0.0017747422,0.00042075038,0.00039402247,0.0006409339,0.00047694318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016534871,0.001298154,0.0006355134,0.0012010502,0.0003132456,0.0007475958,0.00066619925,0.0008966304,0.0025331466],"category_scores_gemma":[0.007031697,0.00018514664,0.000507474,0.0006136096,0.00019033981,0.0010372427,0.00083640905,0.0010325762,0.0025951047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0029064408,0.001169941,0.27975395,0.00029014572,0.00047745195,0.00039914527,0.0010095077,0.30319977,0.009727929,0.0015899274,0.02561449,0.3738613],"study_design_scores_gemma":[0.000039398677,0.00031892603,0.05831131,0.000021744656,0.00005317262,0.00003764126,0.00025570364,0.93429244,0.0020580038,0.0028813242,0.0016957616,0.00003459899],"about_ca_topic_score_codex":0.0075499383,"about_ca_topic_score_gemma":0.009114218,"teacher_disagreement_score":0.0075499383,"about_ca_system_score_codex":0.0005021036,"about_ca_system_score_gemma":0.0004917769,"threshold_uncertainty_score":0.015011966},"labels":[],"label_agreement":null},{"id":"W3115233994","doi":"10.1109/iemcon51383.2020.9284875","title":"Quarantine Quibbles: A Sentiment Analysis of COVID-19 Tweets","year":2020,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Sentiment analysis; Coronavirus disease 2019 (COVID-19); Social media; Microblogging; Pessimism; Pandemic; Computer science; Order (exchange); 2019-20 coronavirus outbreak; Quarantine; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Data science; Internet privacy; World Wide Web; Artificial intelligence; Business; Outbreak; Medicine; Virology","score_opus":0.05395947702658516,"score_gpt":0.31328581936417793,"score_spread":0.2593263423375928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3115233994","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9114672,0.0005019252,0.066921234,0.0012384212,0.0003909284,0.000651311,0.0050194506,0.0017342274,0.012075276],"genre_scores_gemma":[0.91459024,0.00032678567,0.07175835,0.00019665652,0.00022022425,0.00016282452,0.005880499,0.00009916984,0.00676525],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9996519,0.00006674972,0.00003846712,0.000065606546,0.00012768219,0.000049578888],"domain_scores_gemma":[0.99925226,0.00025799393,0.00011059931,0.0000356722,0.00029201075,0.000051518877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074807,0.00043422158,0.00032057872,0.001991086,0.00061367307,0.0010751868,0.00026009863,0.00031747218,0.0016898152],"category_scores_gemma":[0.0017389032,0.00011022235,0.00041915357,0.0010417998,0.00018666859,0.0007444134,0.00045334277,0.00045317705,0.0010485497],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011295171,0.0007018671,0.15108117,0.0005302313,0.00027987896,0.0012514729,0.0048828567,0.0033993346,0.13497122,0.0030453715,0.02644071,0.6722864],"study_design_scores_gemma":[0.00009559988,0.0014021636,0.36214098,0.00021237842,0.0003237759,0.0016660636,0.014454423,0.46226752,0.08417844,0.008190479,0.06491379,0.00015438686],"about_ca_topic_score_codex":0.0017614596,"about_ca_topic_score_gemma":0.0040339874,"teacher_disagreement_score":0.001991086,"about_ca_system_score_codex":0.00032106688,"about_ca_system_score_gemma":0.00033420624,"threshold_uncertainty_score":0.0056530237},"labels":[],"label_agreement":null},{"id":"W3116718057","doi":"10.18653/v1/2020.coling-main.20","title":"Affective and Contextual Embedding for Sarcasm Detection","year":2020,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":108,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University; York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sarcasm; Computer science; Artificial intelligence; Word2vec; Task (project management); Natural language processing; Classifier (UML); Autoencoder; Sentiment analysis; Machine learning; Artificial neural network; Speech recognition; Embedding; Linguistics; Irony","score_opus":0.03166326039572741,"score_gpt":0.2849153203189651,"score_spread":0.2532520599232377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3116718057","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61129826,0.0036600966,0.3600605,0.0013497327,0.0007032252,0.00017969486,0.0016815846,0.0031593705,0.017907633],"genre_scores_gemma":[0.9654339,0.00045913344,0.02636287,0.00014407725,0.00011573852,0.00006913884,0.001174386,0.000045717425,0.0061949953],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985075,0.000039894425,0.000011959496,0.000038234855,0.000032671083,0.000026525558],"domain_scores_gemma":[0.9996344,0.000106322244,0.00006600308,0.000041513715,0.00012665082,0.000025127387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002669137,0.0007191248,0.00020838398,0.00047005553,0.00018630462,0.00033285297,0.00026738338,0.00038485392,0.0019407081],"category_scores_gemma":[0.0012269433,0.00011973894,0.0002211377,0.00029337395,0.00020412351,0.00068819243,0.00035521318,0.00060998363,0.0008539236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00075742125,0.0006029288,0.022764303,0.00038404434,0.00018697006,0.00043393593,0.0008291225,0.03830208,0.07127711,0.005437477,0.026035836,0.83298874],"study_design_scores_gemma":[0.000020808708,0.0003826926,0.029018434,0.00008539238,0.000113637,0.00027197102,0.00039389712,0.9318226,0.018279374,0.00709464,0.012466433,0.000050077513],"about_ca_topic_score_codex":0.00096818624,"about_ca_topic_score_gemma":0.00304293,"teacher_disagreement_score":0.0019407081,"about_ca_system_score_codex":0.00024230928,"about_ca_system_score_gemma":0.00018263303,"threshold_uncertainty_score":0.006492257},"labels":[],"label_agreement":null},{"id":"W3121591929","doi":"10.1109/icdabi51230.2020.9325679","title":"Tweets Sentiment Analysis During COVID-19 Pandemic","year":2020,"lang":"en","type":"article","venue":"2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sentiment analysis; Cluster analysis; Coronavirus disease 2019 (COVID-19); Pandemic; Christian ministry; Cluster (spacecraft); Computer science; Social media; Arabic; Word (group theory); k-means clustering; Natural language processing; Geography; Artificial intelligence; World Wide Web; Mathematics; Political science; Linguistics; Medicine","score_opus":0.14168586828082244,"score_gpt":0.34015495362364423,"score_spread":0.1984690853428218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121591929","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9853064,0.00040761012,0.002324674,0.00069103827,0.00020764595,0.00008701551,0.0057331547,0.00008926084,0.0051531666],"genre_scores_gemma":[0.9904817,0.00028739523,0.002478714,0.000107670894,0.000119302946,0.00006200568,0.0050965594,0.000015928841,0.0013507429],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995346,0.00010499644,0.00005056277,0.00007511082,0.00014227792,0.00009257433],"domain_scores_gemma":[0.99897337,0.00031637165,0.00019785915,0.00003429245,0.00041031197,0.00006788552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005832876,0.00033968192,0.00035226592,0.0010845993,0.00044602295,0.0006939218,0.00018098029,0.00035592675,0.00087217765],"category_scores_gemma":[0.0020068523,0.00008419733,0.00039565083,0.0008967364,0.00018244644,0.00065830303,0.0003504237,0.00031918657,0.00043880957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0032109143,0.00050330284,0.6889844,0.0016967069,0.00043992567,0.001946947,0.0046255006,0.014675039,0.06191562,0.0024612148,0.03907225,0.18046826],"study_design_scores_gemma":[0.000043206095,0.00057335774,0.8375418,0.0001837027,0.00024564093,0.00068928406,0.013247898,0.100991175,0.02164252,0.0013489949,0.023385406,0.00010698374],"about_ca_topic_score_codex":0.004895714,"about_ca_topic_score_gemma":0.005842403,"teacher_disagreement_score":0.004895714,"about_ca_system_score_codex":0.00045462244,"about_ca_system_score_gemma":0.0002798671,"threshold_uncertainty_score":0.009734452},"labels":[],"label_agreement":null},{"id":"W3123424328","doi":"10.1088/1742-6596/1725/1/012015","title":"The accuracy of transfer learning using neural network method for sentiment analysis problem on Indonesian tweets","year":2021,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Trigram; Artificial intelligence; Transfer of learning; Sentiment analysis; Artificial neural network; Machine learning; Feature selection; Bigram; Feature (linguistics); Classifier (UML)","score_opus":0.04542772278488369,"score_gpt":0.3201307835648031,"score_spread":0.27470306077991946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123424328","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8537681,0.00064585573,0.1350219,0.0007975912,0.00030853588,0.000081762795,0.00029893458,0.0008262082,0.008251126],"genre_scores_gemma":[0.9893128,0.000079472884,0.009660746,0.000017470184,0.000021796906,0.000020062107,0.00019787451,0.000015377002,0.00067448436],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992982,0.00022270714,0.000071429145,0.00013369968,0.00018702168,0.00008685903],"domain_scores_gemma":[0.9968731,0.0019144743,0.00017406444,0.00018510994,0.00078604225,0.00006723484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019948843,0.00057361467,0.00043839193,0.0009406561,0.00046790883,0.00077637774,0.0005452938,0.00079422764,0.0012801881],"category_scores_gemma":[0.00854606,0.00012603539,0.00046552936,0.0004632315,0.0002858047,0.001081503,0.00037687836,0.00072331046,0.00034202644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015293356,0.00049857463,0.074127644,0.00035313566,0.00026525152,0.0004169404,0.0004489417,0.4410812,0.016812388,0.0028142983,0.0058670146,0.4557853],"study_design_scores_gemma":[0.0000037584318,0.00003935343,0.0037129575,0.0000069663606,0.000008946102,0.00001837064,0.000049635084,0.99299777,0.002573387,0.00046403782,0.0001190098,0.000005655909],"about_ca_topic_score_codex":0.0052930103,"about_ca_topic_score_gemma":0.0024612993,"teacher_disagreement_score":0.0052930103,"about_ca_system_score_codex":0.0008479951,"about_ca_system_score_gemma":0.00039804136,"threshold_uncertainty_score":0.010550022},"labels":[],"label_agreement":null},{"id":"W3123538310","doi":"10.18653/v1/2021.eacl-main.135","title":"SpanEmo: Casting Multi-label Emotion Classification as Span-prediction","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Open Text (Canada)","funders":"Umm Al-Qura University","keywords":"Sentence; Computer science; Natural language processing; Artificial intelligence; Sentiment analysis; Function (biology); Profiling (computer programming); Machine learning; Speech recognition","score_opus":0.12075204807784735,"score_gpt":0.32916913097716655,"score_spread":0.2084170828993192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123538310","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040993806,0.0004525117,0.9466285,0.0012659455,0.00041709008,0.0002103466,0.0009883284,0.005237317,0.0038062122],"genre_scores_gemma":[0.46242994,0.00053606584,0.515346,0.0012601761,0.00075363595,0.00056208746,0.0053547216,0.00055894296,0.013198479],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991155,0.00027856405,0.000050589213,0.0003278861,0.00015110784,0.00007623612],"domain_scores_gemma":[0.99847466,0.00061903783,0.00015386354,0.0002561116,0.00040567812,0.00009064289],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020180496,0.0015096817,0.0007264304,0.0009285171,0.00041389492,0.001310645,0.0012067093,0.0012829034,0.00337513],"category_scores_gemma":[0.0040677455,0.00030370854,0.0011072741,0.0005535698,0.0004515047,0.0027145168,0.0014642738,0.0022748336,0.00221527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011174271,0.0007692048,0.013155949,0.00032129174,0.00032758585,0.00040473373,0.00084720174,0.06693229,0.032531932,0.017992055,0.042279106,0.82332116],"study_design_scores_gemma":[0.000020102892,0.000118154305,0.0016743436,0.000025188046,0.000047491896,0.00008783455,0.00010389431,0.97418267,0.0050206315,0.013927106,0.004768938,0.000023743947],"about_ca_topic_score_codex":0.0014444276,"about_ca_topic_score_gemma":0.0025724838,"teacher_disagreement_score":0.00337513,"about_ca_system_score_codex":0.00051584997,"about_ca_system_score_gemma":0.00052978715,"threshold_uncertainty_score":0.011290967},"labels":[],"label_agreement":null},{"id":"W3123889119","doi":"10.2139/ssrn.2652876","title":"Econometrics Meets Sentiment: An Overview of Methodology and Applications","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke; Center for Interuniversity Research and Analysis on Organizations; HEC Montréal","funders":"","keywords":"Sentiment analysis; Econometrics; Field (mathematics); Computer science; Econometric model; Software; Data science; Artificial intelligence; Economics; Mathematics","score_opus":0.09112290359342227,"score_gpt":0.34203892028287985,"score_spread":0.25091601668945757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123889119","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017644065,0.0541587,0.91744,0.007836615,0.00079913187,0.0002599042,0.0009041672,0.0009023001,0.01593482],"genre_scores_gemma":[0.083450146,0.11555465,0.77263874,0.004375866,0.009225447,0.0016280434,0.0019943628,0.001042374,0.010090408],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9910218,0.0058763456,0.00060833024,0.0007387351,0.0015185864,0.00023632814],"domain_scores_gemma":[0.9762942,0.01861519,0.0009945572,0.0016367041,0.0021291475,0.00033019728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013601688,0.0016119496,0.0028623736,0.0076112812,0.0006933355,0.0056645125,0.0018198271,0.00308298,0.0074211513],"category_scores_gemma":[0.03651498,0.0010003209,0.0018931834,0.009713998,0.0018639704,0.004584092,0.0022006566,0.004038001,0.006434233],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006620798,0.00014476039,0.0076824944,0.0024091832,0.00032824796,0.00017579633,0.00031858392,0.0079036495,0.0010198471,0.28999865,0.038582295,0.65137035],"study_design_scores_gemma":[0.00006644157,0.000113814414,0.009015564,0.0015464205,0.0001467624,0.00049452094,0.00039976725,0.06484047,0.0013861071,0.723232,0.19861569,0.00014247099],"about_ca_topic_score_codex":0.002900116,"about_ca_topic_score_gemma":0.0020893328,"teacher_disagreement_score":0.013601688,"about_ca_system_score_codex":0.001592982,"about_ca_system_score_gemma":0.0027784903,"threshold_uncertainty_score":0.07193345},"labels":[],"label_agreement":null},{"id":"W3123946626","doi":"10.2139/ssrn.3167181","title":"Can Media and Text Analytics Provide Insights into Labour Market Conditions in China?","year":2018,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Government of Canada; Bank of Canada","funders":"","keywords":"China; Analytics; Data science; Business; Social media; Industrial organization; Political science; Computer science; World Wide Web; Law","score_opus":0.006913561388059448,"score_gpt":0.24540835288052434,"score_spread":0.23849479149246489,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123946626","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9899344,0.00048296616,0.00021734825,0.0014156034,0.000039000402,0.000014586776,0.0015665992,0.000013621705,0.0063160085],"genre_scores_gemma":[0.9974381,0.0003093765,0.000090789785,0.000081025726,0.00007841114,0.000009328925,0.0006504837,0.0000037445732,0.0013388327],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99964356,0.000056850997,0.000030584604,0.00004715042,0.000106678,0.00011509512],"domain_scores_gemma":[0.99852043,0.0003297036,0.0005448495,0.000042641208,0.00035622745,0.00020614761],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061947334,0.0002393822,0.00025131518,0.003784034,0.0006547047,0.001771543,0.0002905138,0.00034214926,0.0034338017],"category_scores_gemma":[0.0017925978,0.00010772345,0.00025186065,0.00410447,0.0004864392,0.0017479066,0.0006419062,0.00033871358,0.00042217755],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012863893,0.00006165504,0.94733727,0.00017443432,0.00008818009,0.0006342606,0.0042974334,0.00067341526,0.0023007146,0.0035943557,0.004837659,0.03587201],"study_design_scores_gemma":[0.0000047848907,0.000037603502,0.9848424,0.00004057654,0.000028326258,0.000035734403,0.005728494,0.0029984491,0.00043414213,0.00097199506,0.0048587825,0.000018588122],"about_ca_topic_score_codex":0.043739438,"about_ca_topic_score_gemma":0.06338347,"teacher_disagreement_score":0.043739438,"about_ca_system_score_codex":0.0012138498,"about_ca_system_score_gemma":0.0014037531,"threshold_uncertainty_score":0.08696967},"labels":[],"label_agreement":null},{"id":"W3128581029","doi":"","title":"Using Machine Learning to Improve the Sustainability of the Online Review Market","year":2020,"lang":"en","type":"article","venue":"Journal of the Association for Information Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Sustainability; Computer science; Machine learning","score_opus":0.024981044985258626,"score_gpt":0.28979219515022125,"score_spread":0.26481115016496265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128581029","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7065467,0.004308879,0.2599179,0.0044033737,0.0003305801,0.0006117089,0.0005767531,0.0022999127,0.021004062],"genre_scores_gemma":[0.9120245,0.00068124273,0.08493053,0.0002592381,0.0001894625,0.000086056716,0.00049311726,0.00007327888,0.0012624946],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957178,0.0020751578,0.0002743191,0.00040742703,0.0012622313,0.0002629914],"domain_scores_gemma":[0.9814625,0.01071952,0.002939118,0.00057411747,0.004004007,0.000300745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008006497,0.00076834863,0.001017563,0.004164555,0.0006120953,0.0028612618,0.00078967476,0.0009920761,0.0013493128],"category_scores_gemma":[0.027491683,0.0003309617,0.00064892776,0.0019656864,0.00043544854,0.004200083,0.0008303587,0.0009324619,0.0007623121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005293091,0.00125812,0.08524603,0.00059073395,0.00028293248,0.00023941969,0.00042552868,0.06320725,0.009948104,0.004820085,0.005297871,0.82815456],"study_design_scores_gemma":[0.00004602308,0.000649301,0.025659652,0.00016066086,0.0001391181,0.00013913508,0.00058528135,0.94428027,0.012241915,0.009355571,0.006678618,0.0000644903],"about_ca_topic_score_codex":0.0027975568,"about_ca_topic_score_gemma":0.0037934212,"teacher_disagreement_score":0.008006497,"about_ca_system_score_codex":0.001338073,"about_ca_system_score_gemma":0.0017793747,"threshold_uncertainty_score":0.0423429},"labels":[],"label_agreement":null},{"id":"W3130924245","doi":"10.1109/icdmw51313.2020.00010","title":"Sentiment is an Attitude not a Feeling","year":2020,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Feeling; Lexicon; Sentiment analysis; Object (grammar); Mood; Confusion; Product (mathematics); Psychology; Computer science; Cognitive psychology; Cognition; Social psychology; Natural language processing; Artificial intelligence; Mathematics","score_opus":0.07464231217629862,"score_gpt":0.29643485081631954,"score_spread":0.2217925386400209,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3130924245","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40238342,0.005915634,0.146463,0.043502685,0.0049366574,0.00025626886,0.0027913658,0.0006637667,0.39308724],"genre_scores_gemma":[0.9678954,0.0015084968,0.011322168,0.003328624,0.00064727577,0.000058939735,0.00050383236,0.0000909471,0.014644269],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984018,0.00046697783,0.00012786737,0.00029158167,0.00062942674,0.0000824073],"domain_scores_gemma":[0.9971501,0.0011250373,0.00059614907,0.0002041127,0.0007810714,0.00014349472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014014515,0.0002778084,0.00039335672,0.0005357841,0.0006382828,0.0039997897,0.00029129587,0.00080649817,0.0038270787],"category_scores_gemma":[0.0060775853,0.0001604546,0.00028542784,0.0010000484,0.0031869097,0.00304461,0.0006702678,0.0010229246,0.0015256308],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039657005,0.00020835336,0.0771133,0.0020077948,0.00076768384,0.0011295737,0.014255495,0.0014410142,0.055020824,0.4448303,0.08315976,0.3196694],"study_design_scores_gemma":[0.000058598376,0.0004378496,0.15424058,0.00096121035,0.00047940147,0.0028442931,0.011734455,0.010139299,0.010383564,0.33197984,0.47654873,0.00019221181],"about_ca_topic_score_codex":0.0011981981,"about_ca_topic_score_gemma":0.0009978765,"teacher_disagreement_score":0.0039997897,"about_ca_system_score_codex":0.0007270575,"about_ca_system_score_gemma":0.0005259166,"threshold_uncertainty_score":0.01280278},"labels":[],"label_agreement":null},{"id":"W3136929761","doi":"10.3390/su13063346","title":"Public Opinions about Online Learning during COVID-19: A Sentiment Analysis Approach","year":2021,"lang":"en","type":"article","venue":"Sustainability","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Lexicon; Sentiment analysis; Newspaper; Coronavirus disease 2019 (COVID-19); Pandemic; Public opinion; Subjectivity; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Public health; Computer science; Artificial intelligence; Political science; Medicine; Sociology; Media studies; Disease; Infectious disease (medical specialty); Pathology","score_opus":0.035660775959911434,"score_gpt":0.3143715490838005,"score_spread":0.27871077312388903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136929761","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9803627,0.0005453288,0.0058367113,0.0009083116,0.000103929386,0.00021748281,0.0023102865,0.000047747442,0.0096674515],"genre_scores_gemma":[0.990334,0.00048462304,0.0054333885,0.00015622764,0.00017946587,0.00014843394,0.0014722982,0.000016848579,0.0017747912],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99894017,0.00038251144,0.000110878485,0.00010214692,0.00033061218,0.00013369205],"domain_scores_gemma":[0.99435836,0.0026562992,0.0011589685,0.00010479524,0.0015294658,0.00019216684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002027779,0.00027564075,0.00038539452,0.0040231314,0.0005547474,0.0016979915,0.00018038438,0.00036536687,0.0013322129],"category_scores_gemma":[0.0048075486,0.000100211815,0.0004024068,0.002835451,0.00041751415,0.0013856086,0.000539403,0.00043307216,0.0005273625],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013401576,0.0005889944,0.52701217,0.0017573881,0.00029401886,0.0013047587,0.022230929,0.0016972916,0.042136285,0.0033097258,0.01372678,0.3846015],"study_design_scores_gemma":[0.00005053344,0.0007326929,0.8526711,0.00043848934,0.00031310163,0.00061834697,0.057641502,0.03356418,0.013670586,0.0035104384,0.036661968,0.00012702579],"about_ca_topic_score_codex":0.0018523644,"about_ca_topic_score_gemma":0.0029611292,"teacher_disagreement_score":0.0040231314,"about_ca_system_score_codex":0.0006387335,"about_ca_system_score_gemma":0.0004971405,"threshold_uncertainty_score":0.010724068},"labels":[],"label_agreement":null},{"id":"W3143431740","doi":"10.1109/asonam49781.2020.9381467","title":"A Pre-training Approach for Stance Classification in Online Forums","year":2020,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université TÉLUQ; Université de Sherbrooke","funders":"","keywords":"Training (meteorology); Computer science; Artificial intelligence; Natural language processing; Geography","score_opus":0.13346400178339757,"score_gpt":0.3184066830872647,"score_spread":0.18494268130386715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3143431740","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27873123,0.0022416306,0.68207276,0.0012179371,0.0007981431,0.0020218533,0.005388727,0.013315847,0.014211904],"genre_scores_gemma":[0.57066333,0.00042236934,0.41062206,0.00034394793,0.0004568997,0.0012741295,0.00915975,0.00023329988,0.0068241796],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998453,0.0004454271,0.00015318554,0.00043023963,0.0003172199,0.000200968],"domain_scores_gemma":[0.9947299,0.0025100315,0.0004904203,0.0005086897,0.0014816836,0.00027927963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004621623,0.0014738034,0.0009070608,0.003608162,0.0010214638,0.0012277049,0.00155478,0.0018963703,0.0040682014],"category_scores_gemma":[0.0076630125,0.00040923513,0.0011270338,0.0012854554,0.0004249972,0.0019114537,0.0010262248,0.0023249271,0.004945836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067407434,0.0013450831,0.033046216,0.0004863264,0.00013622604,0.00034104858,0.00084899744,0.009458194,0.053817943,0.002536309,0.017520465,0.8797891],"study_design_scores_gemma":[0.000107506356,0.00085239526,0.022006974,0.00017840562,0.0001052543,0.0005828524,0.00082346855,0.89941347,0.047614984,0.005740468,0.022514537,0.00005974506],"about_ca_topic_score_codex":0.0013405451,"about_ca_topic_score_gemma":0.0030767357,"teacher_disagreement_score":0.004621623,"about_ca_system_score_codex":0.0006848296,"about_ca_system_score_gemma":0.0010823569,"threshold_uncertainty_score":0.024441779},"labels":[],"label_agreement":null},{"id":"W3154106396","doi":"10.1109/tcss.2021.3069413","title":"Feature-Based Twitter Sentiment Analysis With Improved Negation Handling","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Computational Social Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Negation; Computer science; Artificial intelligence; SemEval; Sentiment analysis; Classifier (UML); Support vector machine; Preprocessor; Natural language processing; Lexicon; Salience (neuroscience); Machine learning; Feature (linguistics); Naive Bayes classifier; Pattern recognition (psychology); Task (project management)","score_opus":0.01909870085038485,"score_gpt":0.2592975266665071,"score_spread":0.24019882581612229,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3154106396","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22161579,0.0007979335,0.746684,0.001092948,0.0004513435,0.0006151248,0.0038423128,0.01765099,0.0072496287],"genre_scores_gemma":[0.7336871,0.00029850143,0.2556199,0.00023309207,0.00026298582,0.0003271301,0.005882202,0.00023933829,0.0034497287],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936897,0.00010041052,0.00008194596,0.00011556498,0.0002687427,0.000064354725],"domain_scores_gemma":[0.9986002,0.00026262528,0.00017399724,0.000111270434,0.0008095131,0.000042433374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076612085,0.0007310961,0.0008990018,0.0020422633,0.00052399165,0.001037642,0.0007016196,0.0004909114,0.0021530145],"category_scores_gemma":[0.0032685006,0.00018177599,0.00068580435,0.0010690831,0.00016527144,0.0016320398,0.0007233491,0.00062618975,0.0019829867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007680531,0.000339906,0.013489391,0.0003379279,0.00016574337,0.00054053124,0.00035307874,0.0077052596,0.14100757,0.0021036568,0.022294007,0.81089485],"study_design_scores_gemma":[0.00009630015,0.00038877083,0.018247865,0.000059964234,0.00016017981,0.00072499015,0.0004095017,0.8559072,0.09979115,0.0060418644,0.018045187,0.00012702763],"about_ca_topic_score_codex":0.0018361737,"about_ca_topic_score_gemma":0.0023854258,"teacher_disagreement_score":0.0021530145,"about_ca_system_score_codex":0.0003731601,"about_ca_system_score_gemma":0.00046149347,"threshold_uncertainty_score":0.007202506},"labels":[],"label_agreement":null},{"id":"W3154236075","doi":"10.1016/j.intmar.2021.02.001","title":"Using Speech Acts to Elicit Positive Emotions for Complainants on Social Media","year":2021,"lang":"en","type":"article","venue":"Journal of Interactive Marketing","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Northern British Columbia","funders":"Natural Resources, Energy and Science Authority of Sri Lanka; National Science Foundation","keywords":"Social media; Psychology; Cognitive psychology; Computer science; World Wide Web","score_opus":0.08470621916703934,"score_gpt":0.3699638223338486,"score_spread":0.28525760316680926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3154236075","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98826444,0.0002079151,0.007017129,0.00022472709,0.000042353055,0.00010014691,0.00030277562,0.00012215426,0.0037182826],"genre_scores_gemma":[0.9935134,0.0001555346,0.004624389,0.0001248793,0.00004407061,0.00007620233,0.00028175954,0.000027139078,0.0011527209],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99880683,0.0005787608,0.00008165101,0.00014221286,0.00029301964,0.000097522],"domain_scores_gemma":[0.9930582,0.004889581,0.0011459677,0.00022495826,0.00051948923,0.00016184665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001285734,0.000567861,0.00030277588,0.0006969994,0.00044560584,0.0012106784,0.0002537061,0.0004524614,0.0011239005],"category_scores_gemma":[0.008446298,0.00012483346,0.00043241234,0.000566475,0.0005257379,0.0009068025,0.0006698444,0.00077867974,0.00046453398],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0039004972,0.0007862669,0.41923583,0.0023122807,0.0004588122,0.0017462518,0.03323503,0.005180407,0.2057592,0.0023046997,0.0066430317,0.3184377],"study_design_scores_gemma":[0.00006831308,0.0012777518,0.853921,0.00022449829,0.00044264615,0.00080136507,0.02218302,0.056602534,0.047154255,0.0024800585,0.0146590425,0.00018551153],"about_ca_topic_score_codex":0.0012019887,"about_ca_topic_score_gemma":0.0026317101,"teacher_disagreement_score":0.001285734,"about_ca_system_score_codex":0.00043432318,"about_ca_system_score_gemma":0.00017832979,"threshold_uncertainty_score":0.006799698},"labels":[],"label_agreement":null},{"id":"W3155208491","doi":"10.48550/arxiv.2104.09586","title":"Semantic Knowledge Discovery and Discussion Mining of Incel Online Community: Topic modeling","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Data science; Sentiment analysis; Latent semantic analysis; Information retrieval; Topic model; Question answering; World Wide Web; Knowledge extraction; Artificial intelligence","score_opus":0.11843579345336841,"score_gpt":0.22813544640831224,"score_spread":0.10969965295494383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3155208491","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37223327,0.0015525358,0.6101784,0.0026047288,0.00013395373,0.0007091486,0.0055267587,0.0011047368,0.005956404],"genre_scores_gemma":[0.8154549,0.0005839578,0.17462735,0.0001649191,0.00023644148,0.0007123652,0.0058260392,0.00008697764,0.0023070402],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9971411,0.0012279971,0.00023747263,0.00075321324,0.00043401527,0.00020622164],"domain_scores_gemma":[0.99222296,0.0054331333,0.00081083056,0.0004853701,0.0008198726,0.0002278636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036568828,0.00069845695,0.0006911811,0.0074719726,0.0011730269,0.002212601,0.0013974434,0.001347472,0.0013406747],"category_scores_gemma":[0.010882779,0.0003557583,0.0018568372,0.004746495,0.00079893693,0.0037694355,0.0012917055,0.0014708989,0.000617934],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013212428,0.0020842284,0.14842732,0.0019520718,0.0009862912,0.0016477478,0.019436544,0.08445945,0.02595795,0.07211451,0.021125033,0.62048763],"study_design_scores_gemma":[0.00007317953,0.00010571037,0.024329176,0.00010903334,0.00015823278,0.00030988778,0.0032190657,0.89160836,0.005643594,0.06146164,0.012920016,0.000062133964],"about_ca_topic_score_codex":0.0051635336,"about_ca_topic_score_gemma":0.006115543,"teacher_disagreement_score":0.0074719726,"about_ca_system_score_codex":0.0012775024,"about_ca_system_score_gemma":0.0012152154,"threshold_uncertainty_score":0.01933968},"labels":[],"label_agreement":null},{"id":"W3155260882","doi":"10.1515/jisys-2020-0115","title":"Arabic sentiment analysis about online learning to mitigate covid-19","year":2021,"lang":"en","type":"article","venue":"Journal of Intelligent Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sentiment analysis; Artificial intelligence; Support vector machine; Naive Bayes classifier; Machine learning; Computer science; Lexicon; Preprocessor; Natural language processing","score_opus":0.038494869519449235,"score_gpt":0.32783823086357433,"score_spread":0.2893433613441251,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3155260882","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.963395,0.00036546733,0.023237187,0.001342179,0.00026284967,0.00019361035,0.0012020257,0.00026175202,0.009739835],"genre_scores_gemma":[0.9860482,0.0002012077,0.010550687,0.00013033398,0.000096444346,0.00005464004,0.0007145896,0.000018609815,0.0021852662],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99952674,0.00016412686,0.00004127595,0.00005591372,0.00016074436,0.000051318755],"domain_scores_gemma":[0.9985191,0.00059666834,0.00023313017,0.0000547329,0.00052974303,0.00006655161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000982444,0.00037023533,0.0002770749,0.00082679116,0.00046572107,0.00082901394,0.00021340803,0.0002975857,0.00196365],"category_scores_gemma":[0.0034802181,0.00006558617,0.00034497536,0.0005833032,0.00020642523,0.0006518134,0.0003010176,0.00042931834,0.00074818195],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022000738,0.0009800979,0.21015798,0.001052115,0.0002713568,0.0017293678,0.0059466297,0.02129831,0.07983933,0.004193286,0.03566493,0.6366666],"study_design_scores_gemma":[0.00006671046,0.0010759666,0.28456727,0.00026085268,0.00034121942,0.00062989094,0.014132009,0.60594374,0.05270602,0.005115408,0.035031,0.00012994853],"about_ca_topic_score_codex":0.0013883951,"about_ca_topic_score_gemma":0.0018815364,"teacher_disagreement_score":0.00196365,"about_ca_system_score_codex":0.00047509535,"about_ca_system_score_gemma":0.00026684525,"threshold_uncertainty_score":0.006569028},"labels":[],"label_agreement":null},{"id":"W3157029782","doi":"10.7717/peerj-cs.786","title":"AdCOFE: Advanced Contextual Feature Extraction in conversations for emotion classification","year":2021,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Feature extraction; Computer science; Feature (linguistics); Artificial intelligence; Extraction (chemistry); Pattern recognition (psychology); Natural language processing; Psychology; Linguistics; Chemistry; Chromatography","score_opus":0.036545576219580295,"score_gpt":0.3188550560258584,"score_spread":0.2823094798062781,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157029782","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09293097,0.0014999885,0.87943256,0.0005319942,0.00035771405,0.0005442106,0.0068921302,0.011590947,0.0062194145],"genre_scores_gemma":[0.5608758,0.0006585882,0.41296527,0.00028507574,0.00028154269,0.0010396839,0.014961786,0.00042875306,0.008503536],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953365,0.0000963586,0.000032888143,0.00014051457,0.00009735973,0.00009925744],"domain_scores_gemma":[0.9995435,0.00017022272,0.000042894648,0.000064929714,0.00014402141,0.00003434387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006093253,0.0011194065,0.00062399526,0.0013385555,0.00048207756,0.0006623542,0.0005798888,0.000676733,0.003959919],"category_scores_gemma":[0.0022924098,0.0001655787,0.0008503157,0.0006803886,0.00017559683,0.0010437713,0.0011166784,0.0008454154,0.0020750416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006516811,0.00042956063,0.00728332,0.00035355677,0.00015399017,0.0003693039,0.0008824079,0.0058749462,0.07889049,0.002582591,0.027371442,0.8751567],"study_design_scores_gemma":[0.000104249564,0.0006496556,0.040524036,0.00015819303,0.00023095582,0.0009877683,0.0015103633,0.7975214,0.073069006,0.018220888,0.066843994,0.00017942295],"about_ca_topic_score_codex":0.0030111922,"about_ca_topic_score_gemma":0.004254116,"teacher_disagreement_score":0.003959919,"about_ca_system_score_codex":0.0003332038,"about_ca_system_score_gemma":0.00041978774,"threshold_uncertainty_score":0.0132472515},"labels":[],"label_agreement":null},{"id":"W3159042761","doi":"10.3390/su13094986","title":"Effects of the COVID-19 Pandemic on Classrooms: A Case Study on Foreigners in South Korea Using Applied Machine Learning","year":2021,"lang":"en","type":"article","venue":"Sustainability","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Sentiment analysis; Timeline; Pandemic; Coronavirus disease 2019 (COVID-19); Social media; Coping (psychology); Government (linguistics); Creativity; Public opinion; Psychology; Data science; Computer science; Public relations; Artificial intelligence; World Wide Web; Political science; Geography; Social psychology","score_opus":0.034910104265666216,"score_gpt":0.31803928739031456,"score_spread":0.28312918312464835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159042761","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9969158,0.00008675001,0.00039216428,0.0007155539,0.000016958355,0.000048843904,0.000313017,0.000011969463,0.0014987729],"genre_scores_gemma":[0.9980186,0.00013549448,0.00064867956,0.00017887556,0.000013799389,0.000032492815,0.00022795859,0.0000068971776,0.00073726947],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994603,0.00024192643,0.000021740452,0.00006820186,0.00005733776,0.00015062194],"domain_scores_gemma":[0.99840313,0.00071171526,0.00031160587,0.000080506354,0.0002255827,0.00026758172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008542332,0.0003502402,0.00024285815,0.00078251807,0.0011522049,0.0008716908,0.00044136736,0.00072708493,0.0016377339],"category_scores_gemma":[0.0018755757,0.00012588334,0.00035175317,0.00081051054,0.0007514284,0.00095084915,0.0009433333,0.00086965365,0.00028414384],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000339089,0.001023407,0.86884356,0.00048006623,0.00012284292,0.010191766,0.03534821,0.0072469315,0.0044494155,0.0014238863,0.007805249,0.062725544],"study_design_scores_gemma":[0.000037665475,0.00065832085,0.67830724,0.00024446676,0.00008930432,0.0012434445,0.28162354,0.019392364,0.0026513305,0.0010022966,0.014661086,0.000088939625],"about_ca_topic_score_codex":0.02776911,"about_ca_topic_score_gemma":0.060087394,"teacher_disagreement_score":0.02776911,"about_ca_system_score_codex":0.0013315444,"about_ca_system_score_gemma":0.0009155217,"threshold_uncertainty_score":0.05521494},"labels":[],"label_agreement":null},{"id":"W3162042679","doi":"10.2196/26255","title":"Methodological Clarifications and Generalizing From Weibo Data. Comment on “Nature and Diffusion of COVID-19–related Oral Health Information on Chinese Social Media: Analysis of Tweets on Weibo”","year":2021,"lang":"en","type":"letter","venue":"Journal of Medical Internet Research","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Social media; Coronavirus disease 2019 (COVID-19); Microblogging; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Internet privacy; Health information; China; Computer science; Psychology; Data science; Sociology; World Wide Web; Political science; Medicine; Health care; Virology","score_opus":0.3554690349088374,"score_gpt":0.5189613256908213,"score_spread":0.1634922907819839,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162042679","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00027999707,0.00026615578,0.0002969344,0.9875822,0.010358357,0.000029382434,0.00029636035,0.000036054946,0.0008547026],"genre_scores_gemma":[0.0020855698,0.00016593537,0.00055275194,0.9864933,0.008299709,0.00011461781,0.00006326249,0.000029426661,0.0021954281],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.96453565,0.01387725,0.0049658176,0.003734115,0.01076861,0.002118591],"domain_scores_gemma":[0.7984863,0.14163053,0.00807672,0.005509972,0.042184625,0.0041118544],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03804776,0.001269599,0.0012707284,0.002191689,0.0070105437,0.0068169846,0.0052113277,0.03910129,0.0066526546],"category_scores_gemma":[0.2128689,0.0011031511,0.0021980614,0.0027420812,0.007918833,0.006729727,0.0034397969,0.051514514,0.007984447],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021816428,0.000008239172,0.00038498171,0.000047166828,0.000012861705,0.0002056723,0.0006899374,0.000024498853,0.00011199253,0.0020470098,0.9944916,0.001954249],"study_design_scores_gemma":[0.00009447681,0.00005981785,0.0048012515,0.0008540749,0.0000714328,0.0007706676,0.004505621,0.001049825,0.0011588642,0.017501907,0.9689233,0.0002088595],"about_ca_topic_score_codex":0.038286783,"about_ca_topic_score_gemma":0.06488689,"teacher_disagreement_score":0.9619522,"about_ca_system_score_codex":0.008115653,"about_ca_system_score_gemma":0.00928381,"threshold_uncertainty_score":0.20121819},"labels":[],"label_agreement":null},{"id":"W3163550658","doi":"10.1016/j.neucom.2021.05.040","title":"MASAD: A large-scale dataset for multimodal aspect-based sentiment analysis","year":2021,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":88,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; York University","funders":"East China Normal University","keywords":"Sentiment analysis; Computer science; Task (project management); Artificial intelligence; Scale (ratio); Mainstream; Natural language processing; Information retrieval; Machine learning","score_opus":0.01964842168906004,"score_gpt":0.2903483329775977,"score_spread":0.27069991128853765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163550658","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.078014776,0.0009129878,0.010995693,0.00070732425,0.00046357143,0.0007673953,0.8900657,0.008316416,0.00975617],"genre_scores_gemma":[0.06356147,0.00037497043,0.027094003,0.00027668368,0.00013203295,0.0010793089,0.90191996,0.00029684734,0.0052646813],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99931026,0.00012625717,0.000089848785,0.00012799617,0.00026505953,0.000080627615],"domain_scores_gemma":[0.9987036,0.0002973445,0.00014563512,0.00020643766,0.00046724326,0.00017969446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006519265,0.0011369582,0.00051689765,0.0030289895,0.00073703093,0.0009163525,0.00094320753,0.0010769059,0.0066297413],"category_scores_gemma":[0.0035092116,0.00022730784,0.00082036736,0.002633869,0.00019520806,0.0010397567,0.0011769353,0.0009481207,0.006598502],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00090518035,0.00072095694,0.030675888,0.0019687135,0.00041501186,0.0007224891,0.0006657766,0.0037477878,0.023720471,0.00255261,0.8061156,0.1277895],"study_design_scores_gemma":[0.00053041504,0.0005238442,0.18211815,0.00036777303,0.0003723688,0.001213138,0.0020457972,0.04838774,0.0188966,0.005814943,0.7394166,0.00031270564],"about_ca_topic_score_codex":0.009293117,"about_ca_topic_score_gemma":0.026326656,"teacher_disagreement_score":0.009293117,"about_ca_system_score_codex":0.0006359475,"about_ca_system_score_gemma":0.0010687863,"threshold_uncertainty_score":0.02217865},"labels":[],"label_agreement":null},{"id":"W3164507854","doi":"10.18280/ria.350209","title":"A Multilabel Classifier for Text Classification and Enhanced BERT System","year":2021,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Classifier (UML); Pooling; Word embedding; Machine learning; Encoder; Sentiment analysis; Embedding; Natural language processing","score_opus":0.06416258640954162,"score_gpt":0.298775168279697,"score_spread":0.2346125818701554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164507854","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.064531446,0.0006302894,0.8871337,0.0004325115,0.00040487057,0.00055737334,0.0022266544,0.03901271,0.005070363],"genre_scores_gemma":[0.4615963,0.0004453722,0.50572443,0.00047266265,0.0002757782,0.000687769,0.010691196,0.000549305,0.019557284],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985991,0.00017161341,0.00011273341,0.00034905068,0.0005753866,0.00019214657],"domain_scores_gemma":[0.9991316,0.00016700785,0.00006164647,0.000087895816,0.0005058848,0.000045931847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010457723,0.0012264041,0.0011719207,0.0023680113,0.0007885397,0.0009949423,0.0014277386,0.0011864896,0.0050590313],"category_scores_gemma":[0.0023832163,0.0003415058,0.0008754548,0.0014724174,0.00023426533,0.0022820127,0.0010357053,0.0012380283,0.0045025134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00084307673,0.0005162735,0.0021505367,0.00020916169,0.00010084348,0.00054521835,0.00016555554,0.022019058,0.05628559,0.0032994633,0.022529872,0.8913354],"study_design_scores_gemma":[0.000039403512,0.00022261508,0.001375155,0.000017326165,0.000054654156,0.00028084428,0.000076816636,0.9552996,0.03142455,0.002294648,0.008853024,0.00006141991],"about_ca_topic_score_codex":0.008557999,"about_ca_topic_score_gemma":0.009342023,"teacher_disagreement_score":0.008557999,"about_ca_system_score_codex":0.0012614423,"about_ca_system_score_gemma":0.0014261941,"threshold_uncertainty_score":0.01701641},"labels":[],"label_agreement":null},{"id":"W3164920349","doi":"10.1016/j.scs.2021.103048","title":"n-Gram based language processing using Twitter dataset to identify COVID-19 patients","year":2021,"lang":"en","type":"article","venue":"Sustainable Cities and Society","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; Seneca Polytechnic","funders":"Alfaisal University","keywords":"Coronavirus disease 2019 (COVID-19); n-gram; Gram; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Computer science; Natural language processing; Artificial intelligence; Medicine; Language model; Virology; Biology; Pathology; Outbreak","score_opus":0.028934895796787725,"score_gpt":0.3423196018229588,"score_spread":0.3133847060261711,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164920349","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85109425,0.0012278538,0.03639241,0.004161242,0.0008594057,0.00092799665,0.0886138,0.0026413093,0.014081731],"genre_scores_gemma":[0.8952859,0.00053445704,0.035315108,0.00062717195,0.00046709224,0.00045981823,0.06109209,0.00009396814,0.006124265],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947757,0.000109589106,0.00008037125,0.00008818658,0.00013666447,0.000107600325],"domain_scores_gemma":[0.9989857,0.000356352,0.00015519466,0.0000466881,0.00034787608,0.0001082093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005168213,0.0005127603,0.00039764974,0.0014523842,0.00043936062,0.0006625203,0.00030570923,0.0004932866,0.0030722017],"category_scores_gemma":[0.0024503935,0.000076755285,0.0005771844,0.0010030502,0.00011774111,0.00043459568,0.00051662914,0.00047776708,0.0023788626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0035969552,0.0012264359,0.34875217,0.0011077864,0.0004186797,0.0029698338,0.001371904,0.0065618404,0.03850881,0.0018224657,0.14044507,0.45321807],"study_design_scores_gemma":[0.0002328056,0.0015946353,0.4208364,0.00030690987,0.000607821,0.0037243636,0.009204826,0.44599274,0.028246436,0.006828401,0.082205035,0.00021966959],"about_ca_topic_score_codex":0.0055363593,"about_ca_topic_score_gemma":0.010639698,"teacher_disagreement_score":0.0055363593,"about_ca_system_score_codex":0.0004708722,"about_ca_system_score_gemma":0.00082721043,"threshold_uncertainty_score":0.011008263},"labels":[],"label_agreement":null},{"id":"W3165298424","doi":"10.1631/fitee.2000689","title":"China in the eyes of news media: a case study under COVID-19 epidemic","year":2021,"lang":"en","type":"article","venue":"Frontiers of Information Technology & Electronic Engineering","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"China; Mainstream; News media; Livelihood; Period (music); Coronavirus disease 2019 (COVID-19); Political science; Advertising; Geography; Medicine; Law; Business","score_opus":0.007047439495837047,"score_gpt":0.24256779885347615,"score_spread":0.2355203593576391,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165298424","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9803437,0.0012419572,0.00048880215,0.0054707523,0.00008752364,0.00008774703,0.00019166098,0.000029316381,0.012058437],"genre_scores_gemma":[0.98946524,0.0020919486,0.0008602358,0.0018175961,0.00016173329,0.00006540613,0.0001730427,0.00003400248,0.005330818],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9981475,0.00091060414,0.00009075398,0.00013322261,0.00030458136,0.00041331555],"domain_scores_gemma":[0.9966583,0.0015273297,0.00062470295,0.00018844938,0.00032288337,0.0006782868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014219581,0.0008232308,0.0004965244,0.0028148685,0.009582972,0.0036961546,0.00097013544,0.003142053,0.0033995726],"category_scores_gemma":[0.0052947127,0.00044405623,0.0005516867,0.0040522665,0.003220737,0.003691374,0.0025042659,0.002438922,0.0004413581],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022480682,0.00047208142,0.11604527,0.0005417625,0.00008452983,0.19528659,0.6293862,0.00031679377,0.0016245662,0.009705651,0.017026981,0.029284751],"study_design_scores_gemma":[0.000031772855,0.00020260438,0.06537297,0.0005234271,0.00012013052,0.058001433,0.80886304,0.002075041,0.0016352644,0.0024021706,0.06065173,0.000120444616],"about_ca_topic_score_codex":0.031847063,"about_ca_topic_score_gemma":0.04594498,"teacher_disagreement_score":0.031847063,"about_ca_system_score_codex":0.0038943768,"about_ca_system_score_gemma":0.0019732718,"threshold_uncertainty_score":0.06332338},"labels":[],"label_agreement":null},{"id":"W3165505241","doi":"10.1145/3446678","title":"Two New Large Corpora for Vietnamese Aspect-based Sentiment Analysis at Sentence Level","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Asian and Low-Resource Language Information Processing","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Benchmark (surveying); Vietnamese; Natural language processing; Artificial intelligence; Task (project management); Sentence; Deep learning; Sentiment analysis; Resource (disambiguation); Code (set theory); Linguistics","score_opus":0.01779395492959392,"score_gpt":0.27241162286545834,"score_spread":0.25461766793586443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165505241","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28434804,0.0028196583,0.091714695,0.0028596343,0.0025403935,0.0037018366,0.5133112,0.0112988185,0.08740575],"genre_scores_gemma":[0.17979854,0.0007699857,0.13039318,0.00053644174,0.0004301823,0.0039422717,0.6633475,0.0019245632,0.01885733],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984596,0.00039253882,0.00026983302,0.00034527972,0.0004124779,0.0001203901],"domain_scores_gemma":[0.9917964,0.002206992,0.0005252098,0.0010619917,0.0038449622,0.00056448067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016058191,0.00093334937,0.0005776467,0.0035659047,0.0016086877,0.0010590764,0.0010333094,0.00070307136,0.017072646],"category_scores_gemma":[0.0069185295,0.00050714397,0.0005140655,0.004691625,0.0007026363,0.0016731262,0.0014409065,0.0015185557,0.0062789735],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00084628514,0.0009935794,0.016415002,0.0043000374,0.00013770843,0.0027784011,0.008181942,0.0032124855,0.06703721,0.011459311,0.56461406,0.32002398],"study_design_scores_gemma":[0.00033303953,0.00045169983,0.10650397,0.00049487303,0.00014361138,0.0026613656,0.004207536,0.019300198,0.03690002,0.0055717435,0.8231493,0.0002826296],"about_ca_topic_score_codex":0.009724562,"about_ca_topic_score_gemma":0.022023302,"teacher_disagreement_score":0.017072646,"about_ca_system_score_codex":0.0009266409,"about_ca_system_score_gemma":0.0018400345,"threshold_uncertainty_score":0.057113707},"labels":[],"label_agreement":null},{"id":"W3167191254","doi":"10.4018/joeuc.20210701.oa6","title":"Assessing Public Opinions of Products Through Sentiment Analysis","year":2021,"lang":"en","type":"article","venue":"Journal of Organizational and End User Computing","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Sentiment analysis; Product (mathematics); Computer science; Process (computing); Social media; Word (group theory); User-generated content; Data science; Information retrieval; Natural language processing; World Wide Web; Linguistics; Mathematics","score_opus":0.038648806735345084,"score_gpt":0.294562860242331,"score_spread":0.25591405350698593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3167191254","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8176577,0.00051912025,0.15019245,0.0008622528,0.00016218069,0.00055848144,0.0012358878,0.0006204263,0.028191485],"genre_scores_gemma":[0.9611576,0.0003405497,0.03558808,0.000082659164,0.000097729484,0.0001556151,0.00059806055,0.000036092795,0.001943623],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99744594,0.0007703479,0.00022832109,0.00020469225,0.0011932843,0.00015743708],"domain_scores_gemma":[0.994803,0.0017844875,0.0008696322,0.00017951352,0.0022579394,0.00010542865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032476098,0.00050362566,0.0005870417,0.003496397,0.0006428892,0.0018677464,0.00029642816,0.00047544716,0.0012589187],"category_scores_gemma":[0.007129424,0.00016490089,0.00047176782,0.0018789234,0.00034619073,0.0015387909,0.00066681096,0.00048962416,0.00072899146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011346209,0.00055541995,0.1178305,0.0008380576,0.00031147237,0.00056964054,0.0048639,0.0075493543,0.091761306,0.00773268,0.011617126,0.75523585],"study_design_scores_gemma":[0.00008129392,0.0015471587,0.3242731,0.0003695133,0.00061432127,0.0009668962,0.018475268,0.51055586,0.08242287,0.019154513,0.04122036,0.00031884154],"about_ca_topic_score_codex":0.0010843483,"about_ca_topic_score_gemma":0.0015066407,"teacher_disagreement_score":0.003496397,"about_ca_system_score_codex":0.0006948708,"about_ca_system_score_gemma":0.00048771486,"threshold_uncertainty_score":0.017175198},"labels":[],"label_agreement":null},{"id":"W3170492283","doi":"10.4309/jgi.2021.47.5","title":"Understanding the Emotions of Those With a Gambling Disorder: Insights From Automated Text Analysis","year":2021,"lang":"en","type":"article","venue":"Journal of Gambling Issues","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of Ottawa","funders":"","keywords":"Sadness; Psychology; Humanities; Social media; Sentiment analysis; Anger; Social psychology; World Wide Web; Computer science; Philosophy; Artificial intelligence","score_opus":0.17503598767444337,"score_gpt":0.3564891989659389,"score_spread":0.18145321129149553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3170492283","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9893188,0.00028608512,0.004617865,0.0008590768,0.000022023962,0.00008135523,0.0010021719,0.00003560022,0.0037769652],"genre_scores_gemma":[0.98836166,0.00044885156,0.0080479225,0.00025093206,0.00004864558,0.00014163862,0.0011679478,0.000028274017,0.0015040676],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99920183,0.00037152896,0.00008763071,0.000102690545,0.00015968383,0.00007653768],"domain_scores_gemma":[0.99338335,0.0046751373,0.0010414637,0.00011832321,0.00063591375,0.0001457014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013904097,0.00026023382,0.00021706417,0.0021515142,0.0006682268,0.0012951079,0.00029424092,0.00043512136,0.0012965298],"category_scores_gemma":[0.007941885,0.00011175534,0.00027142698,0.0014644092,0.00054727634,0.001376842,0.0008164751,0.0005036722,0.00049135473],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050016283,0.00035633644,0.48098317,0.0010078892,0.000058584472,0.0024034563,0.22733878,0.0011884917,0.023367252,0.0018976853,0.0074134176,0.25348485],"study_design_scores_gemma":[0.000019165298,0.00020474281,0.7433853,0.00042462515,0.000067239365,0.0013596638,0.20577094,0.017375324,0.0044752876,0.0052452465,0.02158959,0.0000828932],"about_ca_topic_score_codex":0.0028363196,"about_ca_topic_score_gemma":0.0051129316,"teacher_disagreement_score":0.0028363196,"about_ca_system_score_codex":0.00044868412,"about_ca_system_score_gemma":0.0003761592,"threshold_uncertainty_score":0.007353306},"labels":[],"label_agreement":null},{"id":"W3170767617","doi":"10.21428/594757db.08d5c187","title":"Using Sentiment Information for Preemptive Detection of Harmful Comments in Online Conversations","year":2021,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Research Canada","funders":"Mitacs","keywords":"Conversation; Computer science; Task (project management); Focus (optics); Moderation; Sentiment analysis; Subject (documents); Data science; Artificial intelligence; World Wide Web; Machine learning; Psychology; Engineering; Communication","score_opus":0.059043499777345944,"score_gpt":0.32077848935909203,"score_spread":0.26173498958174607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3170767617","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79774576,0.0016363848,0.1775666,0.0012956454,0.00047798318,0.000754931,0.0038023961,0.002727538,0.0139927035],"genre_scores_gemma":[0.92186517,0.0004786983,0.072405994,0.00018043423,0.00034778912,0.000189969,0.002364764,0.00012566439,0.002041553],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998262,0.0005596607,0.00015513104,0.0002966017,0.0005462665,0.00018040054],"domain_scores_gemma":[0.9865953,0.0065367017,0.0023507574,0.00070483226,0.0033101018,0.0005022978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024766217,0.0010144038,0.0007300225,0.004039288,0.0008378784,0.0011521088,0.00047247732,0.00082731294,0.0016537226],"category_scores_gemma":[0.015191902,0.00025467356,0.00050198083,0.0014084894,0.0003027929,0.001980479,0.00085148116,0.0012748331,0.0016705609],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001906735,0.00083147374,0.23795635,0.0012797355,0.0003116644,0.0010279028,0.0039654267,0.0036958405,0.17417315,0.0024461597,0.0137354,0.5586701],"study_design_scores_gemma":[0.00011765981,0.0011289263,0.37989926,0.00044331676,0.00063304626,0.001892297,0.0051535107,0.45335487,0.11710087,0.011854743,0.028105276,0.0003163119],"about_ca_topic_score_codex":0.0014212101,"about_ca_topic_score_gemma":0.0025390028,"teacher_disagreement_score":0.004039288,"about_ca_system_score_codex":0.00040187678,"about_ca_system_score_gemma":0.0005747985,"threshold_uncertainty_score":0.013097763},"labels":[],"label_agreement":null},{"id":"W3170847021","doi":"10.2196/28292","title":"Improving Human Happiness Analysis Based on Transfer Learning: Algorithm Development and Validation","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Happiness; Artificial intelligence; Computer science; Agency (philosophy); Sociality; Machine learning; Encoder; Natural language processing; Psychology; Cognitive psychology; Social psychology; Sociology; Social science","score_opus":0.018662429675573498,"score_gpt":0.2798577792954367,"score_spread":0.2611953496198632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3170847021","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2880001,0.003052556,0.6893457,0.00088734314,0.00041961498,0.0009106928,0.0008222615,0.011053274,0.0055084834],"genre_scores_gemma":[0.77722454,0.0005536871,0.21486324,0.00037456478,0.00008865555,0.0007180569,0.0021945182,0.0001892955,0.0037933479],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896336,0.00032187073,0.00007659564,0.0003045023,0.00018233646,0.00015143205],"domain_scores_gemma":[0.9982888,0.00070840423,0.00010243666,0.0002095667,0.0006074432,0.00008337752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035066095,0.0016432859,0.0009701854,0.0011818131,0.00061642175,0.0008262958,0.0018193248,0.0015746932,0.002687808],"category_scores_gemma":[0.0056304284,0.0003397629,0.0008730469,0.00082870986,0.00051414,0.001260119,0.0015037984,0.0018049,0.001426675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00086649996,0.0007294728,0.012141085,0.00019801827,0.0002659226,0.0001824663,0.00014621756,0.27818406,0.00810954,0.0012759315,0.010237131,0.6876636],"study_design_scores_gemma":[0.000021404638,0.00007104028,0.0008436837,0.000008811934,0.000016177977,0.000022124274,0.000033697965,0.9958476,0.0022274335,0.00059071847,0.00031006074,0.00000723996],"about_ca_topic_score_codex":0.008495632,"about_ca_topic_score_gemma":0.005439425,"teacher_disagreement_score":0.008495632,"about_ca_system_score_codex":0.0012822754,"about_ca_system_score_gemma":0.001342801,"threshold_uncertainty_score":0.018544972},"labels":[],"label_agreement":null},{"id":"W3171449285","doi":"10.3390/ijerph18115993","title":"Sentiment Analysis on COVID-19-Related Social Distancing in Canada Using Twitter Data","year":2021,"lang":"en","type":"article","venue":"International Journal of Environmental Research and Public Health","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":90,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Social distance; Confusion matrix; Social media; Sentiment analysis; Support vector machine; Confusion; Computer science; Distancing; Coronavirus disease 2019 (COVID-19); Artificial intelligence; Psychology; Internet privacy; World Wide Web; Medicine","score_opus":0.19620024025943247,"score_gpt":0.4338740922199852,"score_spread":0.2376738519605527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3171449285","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96046257,0.0003576066,0.0013303574,0.0007867522,0.00007037475,0.0002588297,0.026880058,0.00015697588,0.009696438],"genre_scores_gemma":[0.9582481,0.0005399784,0.0036657369,0.0001273085,0.00004109802,0.00017333566,0.029188283,0.000031355004,0.007984779],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99921167,0.00006854871,0.000053525982,0.000087659915,0.0003962198,0.00018244005],"domain_scores_gemma":[0.9971923,0.00037927882,0.00024308183,0.00006615534,0.0019246805,0.000194451],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007551613,0.00038235716,0.00029648916,0.0028757243,0.0021013243,0.0011920376,0.00047142754,0.00031061348,0.0013687387],"category_scores_gemma":[0.0033464949,0.000115856354,0.00032406027,0.0052933865,0.00049781526,0.00041795376,0.00070813246,0.00037862814,0.00048884],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010769006,0.00023868284,0.7341802,0.0011149389,0.00015157215,0.0025845075,0.014800888,0.0063370215,0.01768369,0.0025162965,0.06009673,0.15921862],"study_design_scores_gemma":[0.000021746295,0.00008303944,0.8840255,0.00017963412,0.000086251974,0.00022883865,0.026856974,0.03084527,0.005445722,0.0003032406,0.05182815,0.000095579184],"about_ca_topic_score_codex":0.9131058,"about_ca_topic_score_gemma":0.9369789,"teacher_disagreement_score":0.086894214,"about_ca_system_score_codex":0.010341594,"about_ca_system_score_gemma":0.009013399,"threshold_uncertainty_score":0.17481184},"labels":[],"label_agreement":null},{"id":"W3173150014","doi":"10.22215/etd/2021-14411","title":"Studying the Evolution of Bitcoin-Related Topics Extracted from an Online Forum","year":2021,"lang":"en","type":"dissertation","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Cryptocurrency; Relation (database); Sentiment analysis; Social media; Work (physics); Data science; Computer science; Political science; World Wide Web; Engineering; Data mining; Artificial intelligence","score_opus":0.03100246955707635,"score_gpt":0.2980567743071657,"score_spread":0.26705430475008934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173150014","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98133355,0.0015193465,0.0031229495,0.00035282597,0.00025224016,0.00005141601,0.008001123,0.00022266126,0.0051439432],"genre_scores_gemma":[0.9753702,0.00069337094,0.0041150916,0.00006909256,0.00034102963,0.000070115515,0.015729759,0.000045318793,0.00356608],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99941623,0.000108797445,0.000045745954,0.00013536266,0.00018598152,0.000107874825],"domain_scores_gemma":[0.9972487,0.001299948,0.00042465414,0.0000977987,0.00061494124,0.00031395516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071617233,0.00039574696,0.00028784416,0.005580219,0.0006224179,0.0010039374,0.00014644678,0.0004886257,0.0011483808],"category_scores_gemma":[0.0031103077,0.00008425499,0.0003690161,0.0039904984,0.00017629827,0.0011687106,0.00040377976,0.00053427916,0.0010526844],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011478306,0.00076705724,0.5637555,0.0010915106,0.00029470774,0.0015010319,0.005426911,0.0026331646,0.06698253,0.0022962645,0.041499916,0.31260356],"study_design_scores_gemma":[0.000023295874,0.000270451,0.912798,0.00009972657,0.00015860694,0.0009979873,0.004874436,0.039924238,0.008060173,0.0011449,0.031583205,0.000065035194],"about_ca_topic_score_codex":0.0030590743,"about_ca_topic_score_gemma":0.0050686672,"teacher_disagreement_score":0.005580219,"about_ca_system_score_codex":0.00031016546,"about_ca_system_score_gemma":0.00022114888,"threshold_uncertainty_score":0.0060825944},"labels":[],"label_agreement":null},{"id":"W3173485134","doi":"10.1007/s10614-021-10111-y","title":"A Two-Dimensional Sentiment Analysis of Online Public Opinion and Future Financial Performance of Publicly Listed Companies","year":2021,"lang":"en","type":"article","venue":"Computational Economics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Valence (chemistry); Sentiment analysis; Arousal; Quarter (Canadian coin); Lexicon; Psychology; Stock price; Stock (firearms); Business; Computer science; Social psychology; Artificial intelligence; History; Chemistry","score_opus":0.02363227815902713,"score_gpt":0.25683721059267145,"score_spread":0.23320493243364432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173485134","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9908823,0.000063218205,0.00485047,0.00022450645,0.000033951274,0.00003465493,0.0012077731,0.000059992035,0.0026432066],"genre_scores_gemma":[0.99410534,0.000043275108,0.003476051,0.000027647324,0.000051974373,0.000025542615,0.0015503997,0.0000049956407,0.0007146647],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996315,0.00012277008,0.000025856049,0.000067632696,0.00010081463,0.000051406725],"domain_scores_gemma":[0.9987282,0.0005597686,0.0002012127,0.00006675283,0.00033171993,0.00011232449],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007066914,0.00025080293,0.00027993566,0.0014614112,0.00042226308,0.0010559683,0.00023181547,0.00039918744,0.0015650137],"category_scores_gemma":[0.0024019033,0.000103177066,0.00054153753,0.0011076903,0.00016069284,0.00079789467,0.0004399878,0.00040250094,0.00044682875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016756975,0.002087463,0.69524336,0.00021645705,0.0007159734,0.0004903396,0.0009461969,0.010143846,0.03808002,0.0031364427,0.015614825,0.23164937],"study_design_scores_gemma":[0.00004409149,0.000494004,0.7319588,0.000024768276,0.00020496832,0.0001727775,0.0014877416,0.25722075,0.0045831767,0.001555731,0.0022050212,0.000048134956],"about_ca_topic_score_codex":0.0035997562,"about_ca_topic_score_gemma":0.0041251183,"teacher_disagreement_score":0.0035997562,"about_ca_system_score_codex":0.00042019933,"about_ca_system_score_gemma":0.00029602935,"threshold_uncertainty_score":0.007157624},"labels":[],"label_agreement":null},{"id":"W3173838631","doi":"10.18653/v1/2021.findings-acl.208","title":"P-Stance: A Large Dataset for Stance Detection in Political Domain","year":2021,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":85,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Amazon Web Services; National Science Foundation","keywords":"Politics; Domain (mathematical analysis); Computer science; Artificial intelligence; Political science; Mathematics","score_opus":0.020144245080168096,"score_gpt":0.30629306554779084,"score_spread":0.28614882046762274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173838631","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0724292,0.001313352,0.006470985,0.0011465845,0.0006095559,0.0005081108,0.8929121,0.004593125,0.020016994],"genre_scores_gemma":[0.061149627,0.00038966825,0.01673687,0.00035384911,0.00016452986,0.00069770316,0.91334134,0.00027875009,0.0068877614],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925894,0.00015446948,0.00010533782,0.00016582463,0.0002189879,0.00009645898],"domain_scores_gemma":[0.9985291,0.00035208097,0.00026496826,0.00022861263,0.00043558414,0.0001896965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005704927,0.0009778277,0.00039786266,0.0023166952,0.0010865611,0.0008954723,0.0007323617,0.001593171,0.00592514],"category_scores_gemma":[0.0031902317,0.00021572325,0.0004871752,0.0023383142,0.00030506204,0.0012694787,0.0011112145,0.0012819311,0.009040061],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00082613115,0.00043662178,0.01984852,0.0012603052,0.00009875399,0.00056814094,0.0009923292,0.001425953,0.011369362,0.0035414707,0.88280904,0.0768234],"study_design_scores_gemma":[0.00031298058,0.00025691514,0.079193614,0.00042459441,0.00006918241,0.00092817005,0.0018918865,0.017375542,0.013035854,0.00414244,0.8822117,0.00015716955],"about_ca_topic_score_codex":0.0061525083,"about_ca_topic_score_gemma":0.018713884,"teacher_disagreement_score":0.0061525083,"about_ca_system_score_codex":0.00070585206,"about_ca_system_score_gemma":0.0009525943,"threshold_uncertainty_score":0.019821584},"labels":[],"label_agreement":null},{"id":"W3174860526","doi":"10.18653/v1/2021.findings-acl.128","title":"Semantic and Syntactic Enhanced Aspect Sentiment Triplet Extraction","year":2021,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"National Key Research and Development Program of China","keywords":"Computer science; Natural language processing; Artificial intelligence; Sentence; Sentiment analysis; Inference; ENCODE; Graph; Exploit; Ontology; Pipeline (software); Theoretical computer science","score_opus":0.01717425674907036,"score_gpt":0.277706360302578,"score_spread":0.26053210355350764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174860526","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05256576,0.0007427113,0.9175859,0.000680896,0.000315116,0.0003998603,0.005945621,0.01119464,0.010569509],"genre_scores_gemma":[0.43762305,0.0006888303,0.52812266,0.00048104912,0.00023871937,0.00034707322,0.022313733,0.0007691077,0.009415827],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996202,0.000057577257,0.000034491033,0.00012693365,0.00011233136,0.000048494716],"domain_scores_gemma":[0.9995683,0.00010048459,0.000059744685,0.00007357291,0.00017584287,0.000022060362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047518482,0.0015394797,0.00078017195,0.0018953966,0.0004710754,0.000848734,0.0007900734,0.00077723345,0.0038629046],"category_scores_gemma":[0.0017762758,0.00037732863,0.0014481981,0.0014642334,0.0002887859,0.002166363,0.0011730526,0.0012551323,0.00248591],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004220798,0.00026853933,0.0053932676,0.00065111025,0.0002485829,0.0010100305,0.00038454635,0.022695681,0.11672971,0.014998358,0.051158726,0.7860394],"study_design_scores_gemma":[0.00005962279,0.00017491322,0.005687031,0.000072923576,0.00019174968,0.00062091474,0.0002876975,0.8629124,0.0516543,0.046768267,0.03149462,0.00007547097],"about_ca_topic_score_codex":0.0021673567,"about_ca_topic_score_gemma":0.0049675056,"teacher_disagreement_score":0.0038629046,"about_ca_system_score_codex":0.00049303885,"about_ca_system_score_gemma":0.0007706482,"threshold_uncertainty_score":0.012922764},"labels":[],"label_agreement":null},{"id":"W3176071257","doi":"10.1109/hora52670.2021.9461354","title":"Sentiment Analysis of Meeting Room","year":2021,"lang":"en","type":"article","venue":"2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Sentiment analysis; Normalization (sociology); Artificial neural network; Big data; Artificial intelligence; Data modeling; Speech recognition; Machine learning; Data mining; Database","score_opus":0.03038711447665572,"score_gpt":0.33021873555788744,"score_spread":0.29983162108123174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176071257","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8563419,0.0021868297,0.039210875,0.0010061249,0.0020646015,0.0005549705,0.024252428,0.0013286112,0.07305363],"genre_scores_gemma":[0.9641783,0.00064823654,0.009574252,0.00012709042,0.00038927182,0.00021140983,0.014315712,0.0000920342,0.01046356],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875367,0.00016958709,0.0001147302,0.00020045457,0.000617632,0.00014377742],"domain_scores_gemma":[0.998319,0.0002078634,0.00018891286,0.00006237032,0.0011443166,0.000077683624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006889491,0.00050561846,0.00041594676,0.0013556095,0.00044487286,0.0009292039,0.00032750654,0.00026048181,0.0051690056],"category_scores_gemma":[0.0029378333,0.00009151407,0.00047149314,0.0012791558,0.0001702241,0.0006656728,0.00037779924,0.00032200888,0.0031871584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023921106,0.00041820633,0.12325395,0.0018815838,0.00038862592,0.0010145123,0.0021771963,0.005382339,0.11381749,0.0030063535,0.092989944,0.6532777],"study_design_scores_gemma":[0.000086403015,0.0011660471,0.6158738,0.0003178424,0.00042691643,0.0016067359,0.012584791,0.1261898,0.07262061,0.0027329987,0.1661164,0.0002776687],"about_ca_topic_score_codex":0.0016277876,"about_ca_topic_score_gemma":0.002561865,"teacher_disagreement_score":0.0051690056,"about_ca_system_score_codex":0.00053899415,"about_ca_system_score_gemma":0.00024332771,"threshold_uncertainty_score":0.017292023},"labels":[],"label_agreement":null},{"id":"W3185982650","doi":"10.1016/j.mlwa.2021.100114","title":"Restaurant recommender system based on sentiment analysis","year":2021,"lang":"en","type":"article","venue":"Machine Learning with Applications","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":113,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Recommender system; Computer science; Precision and recall; Information retrieval; Similarity (geometry); Context (archaeology); Recall; Quality (philosophy); Sentiment analysis; Service (business); Semantic similarity; Semantic analysis (machine learning); World Wide Web; Artificial intelligence","score_opus":0.01087090913641009,"score_gpt":0.24661849414779888,"score_spread":0.2357475850113888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3185982650","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46185526,0.005892361,0.43275425,0.002061949,0.0013938395,0.0025579988,0.01892482,0.02953271,0.045026787],"genre_scores_gemma":[0.6711483,0.0020861123,0.2861594,0.00059411343,0.00045630935,0.0005584718,0.01935744,0.00020897062,0.019430894],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939644,0.00007469415,0.0000848909,0.00017325935,0.00021132697,0.00005935338],"domain_scores_gemma":[0.9991208,0.00009493971,0.00006687326,0.000057360987,0.00061515096,0.000044949364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071470917,0.00087532675,0.0010375471,0.0018623786,0.00064216845,0.0007304174,0.0005872418,0.00061576144,0.0029987467],"category_scores_gemma":[0.0014699601,0.00030004798,0.0009835019,0.0010215961,0.000081162056,0.0010081831,0.00039243366,0.0005024483,0.0027706416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021731935,0.0011701981,0.06773264,0.0013567558,0.00095088896,0.0012217606,0.0006384792,0.007693986,0.106768444,0.0022331534,0.085248604,0.7228118],"study_design_scores_gemma":[0.00040704524,0.0011364394,0.07651558,0.00017395831,0.0014427352,0.0014501456,0.0006025014,0.80790824,0.052970495,0.0021591245,0.05492866,0.00030521114],"about_ca_topic_score_codex":0.014659287,"about_ca_topic_score_gemma":0.02038833,"teacher_disagreement_score":0.014659287,"about_ca_system_score_codex":0.00043529016,"about_ca_system_score_gemma":0.0005564523,"threshold_uncertainty_score":0.029147923},"labels":[],"label_agreement":null},{"id":"W3193405709","doi":"10.1080/10584609.2021.1952497","title":"The Automatic Analysis of Emotion in Political Speech Based on Transcripts","year":2021,"lang":"en","type":"article","venue":"Political Communication","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal; University of Toronto","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Sentiment analysis; Computer science; Emotion detection; Natural language processing; Artificial intelligence; Word (group theory); Politics; Emotion recognition; Linguistics; Speech recognition","score_opus":0.03438306487592343,"score_gpt":0.3140308064692799,"score_spread":0.2796477415933565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193405709","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5158718,0.00096684543,0.40704864,0.0015459711,0.0010488875,0.0013203904,0.040157937,0.0049157836,0.02712363],"genre_scores_gemma":[0.73840964,0.00074025715,0.20506077,0.00015973787,0.0004257794,0.0014065529,0.044945214,0.00047750358,0.008374576],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99766797,0.0011960892,0.00019871631,0.00034038478,0.00045437473,0.0001425081],"domain_scores_gemma":[0.993093,0.0031897463,0.0008943024,0.0006935084,0.0019975605,0.0001319254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018357184,0.00062225043,0.00028185773,0.0024025547,0.00048658365,0.0011130744,0.00034008714,0.00041202267,0.0031827758],"category_scores_gemma":[0.010984974,0.0001841417,0.00042237077,0.0018217058,0.0005000708,0.0013538788,0.0010234909,0.0007497543,0.0040227724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008946913,0.00021492738,0.042661376,0.001606574,0.00011549062,0.00078238855,0.00799733,0.0052830926,0.16712816,0.009024482,0.051449187,0.7128423],"study_design_scores_gemma":[0.00018821034,0.00097563997,0.2320434,0.0009915618,0.00031475382,0.0019041027,0.02529381,0.2689165,0.19779518,0.040444802,0.23076767,0.00036444972],"about_ca_topic_score_codex":0.0012779569,"about_ca_topic_score_gemma":0.0016634602,"teacher_disagreement_score":0.0031827758,"about_ca_system_score_codex":0.00035066067,"about_ca_system_score_gemma":0.00050458405,"threshold_uncertainty_score":0.010647416},"labels":[],"label_agreement":null},{"id":"W3193458124","doi":"10.1007/s42979-021-00807-1","title":"Specialists, Scientists, and Sentiments: Word2Vec and Doc2Vec in Analysis of Scientific and Medical Texts","year":2021,"lang":"en","type":"article","venue":"SN Computer Science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; University of Ottawa","funders":"","keywords":"Word2vec; Computer science; Sentiment analysis; Word embedding; Data mining; Artificial intelligence; Information retrieval; Embedding","score_opus":0.013153829664077327,"score_gpt":0.2771378444782079,"score_spread":0.2639840148141306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193458124","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7919427,0.0068835285,0.15163112,0.0041880636,0.0012459463,0.0003521183,0.031086866,0.005365575,0.0073039387],"genre_scores_gemma":[0.8027482,0.0019379115,0.14034098,0.0005363168,0.00044454736,0.00036278038,0.049064845,0.0004706568,0.004093712],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897754,0.0004477567,0.00012722847,0.00014018362,0.0002157861,0.0000915745],"domain_scores_gemma":[0.99840504,0.0007784719,0.00014323437,0.00009845095,0.0004697388,0.00010507591],"candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0012753286,0.0006469582,0.00036830653,0.0018151461,0.00034989562,0.0012031829,0.0002368524,0.00038620475,0.0008603455],"category_scores_gemma":[0.0034671118,0.00015219969,0.0005229152,0.0024044218,0.00025995832,0.0013031171,0.00073659496,0.0008633931,0.00056421664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001522222,0.0009724369,0.091265805,0.0015255746,0.00096771884,0.0006553276,0.002636672,0.015919857,0.033911172,0.00876048,0.12079655,0.7210661],"study_design_scores_gemma":[0.0002233815,0.00046897735,0.14940496,0.00050103577,0.00077858346,0.00081860874,0.0029371863,0.7155831,0.021056637,0.018922413,0.0891439,0.00016125895],"about_ca_topic_score_codex":0.007379763,"about_ca_topic_score_gemma":0.015220694,"teacher_disagreement_score":0.9987247,"about_ca_system_score_codex":0.00043818756,"about_ca_system_score_gemma":0.001351016,"threshold_uncertainty_score":0.01467365},"labels":[],"label_agreement":null},{"id":"W3197264270","doi":"10.1016/j.knosys.2021.107449","title":"Enhancing emotion inference in conversations with commonsense knowledge","year":2021,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Conversation; Computer science; Inference; Commonsense knowledge; Utterance; Leverage (statistics); Commonsense reasoning; Feeling; Knowledge graph; Task (project management); Natural language processing; Artificial intelligence; Cognitive psychology; Knowledge base; Psychology; Social psychology; Communication","score_opus":0.029681385024280717,"score_gpt":0.2835319008581277,"score_spread":0.253850515833847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197264270","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35672918,0.0008044121,0.6256353,0.0024657042,0.00034242528,0.0002237522,0.0008105975,0.0018483745,0.011140185],"genre_scores_gemma":[0.916037,0.00021911906,0.081089355,0.00027370482,0.00016884542,0.000061321116,0.0006207192,0.00008931066,0.0014407064],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983063,0.0007709903,0.00006934182,0.0003696314,0.00034518162,0.000138493],"domain_scores_gemma":[0.9914567,0.006903992,0.0003546062,0.00040530873,0.0007009167,0.00017857793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021117355,0.00071847846,0.00060796115,0.00090708723,0.0007260674,0.0025257957,0.0008338093,0.0012268586,0.0028633086],"category_scores_gemma":[0.018055674,0.0003159621,0.0005610072,0.0005762642,0.00042551168,0.0041479906,0.0020414325,0.0018162108,0.00085778424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0038350406,0.0020659687,0.022292763,0.001036526,0.0005150584,0.00093816937,0.007009752,0.061833125,0.113191254,0.018498108,0.01303371,0.75575054],"study_design_scores_gemma":[0.00005896144,0.00021808027,0.007078609,0.000105143605,0.00023777169,0.000114365685,0.0014256006,0.9078291,0.030664967,0.04684194,0.0053666807,0.00005879916],"about_ca_topic_score_codex":0.001968122,"about_ca_topic_score_gemma":0.0027266885,"teacher_disagreement_score":0.0028633086,"about_ca_system_score_codex":0.00061035407,"about_ca_system_score_gemma":0.0005257845,"threshold_uncertainty_score":0.011168063},"labels":[],"label_agreement":null},{"id":"W3198730144","doi":"10.1007/978-3-030-86472-9_18","title":"BERT-Based Multi-Task Learning for Aspect-Based Opinion Mining","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Pooling; Sentiment analysis; SemEval; Task (project management); Artificial intelligence; Machine learning; Artificial neural network; Natural language processing","score_opus":0.03343226142974383,"score_gpt":0.28241887930439863,"score_spread":0.24898661787465481,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198730144","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017493112,0.00093349314,0.9762296,0.000321492,0.0002710815,0.00017700122,0.0005262334,0.0025315415,0.0015164622],"genre_scores_gemma":[0.41739058,0.00085249986,0.568281,0.00066963426,0.0006693767,0.00069168943,0.0049053114,0.000462034,0.006077856],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998423,0.0004649919,0.00014373011,0.00044089547,0.00030349067,0.00022382865],"domain_scores_gemma":[0.9958224,0.0024750654,0.00020522662,0.00033927252,0.0009532449,0.00020469261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033035462,0.0020596618,0.002140246,0.0017650612,0.0010237654,0.001785473,0.003002821,0.0024467702,0.0046939747],"category_scores_gemma":[0.0072716256,0.0007204215,0.0019371307,0.0027466083,0.0005118845,0.0025903992,0.002666281,0.0033144539,0.0032592567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00072106515,0.0006314095,0.0020984665,0.0002610001,0.00031327363,0.00021159304,0.00018953823,0.060962163,0.013994887,0.0030561637,0.01930723,0.89825314],"study_design_scores_gemma":[0.000021287478,0.00007421257,0.0003132695,0.000009901466,0.00003772333,0.000031368516,0.000027727378,0.99261683,0.001778108,0.004050552,0.0010263177,0.000012649562],"about_ca_topic_score_codex":0.004787234,"about_ca_topic_score_gemma":0.005990932,"teacher_disagreement_score":0.004787234,"about_ca_system_score_codex":0.00084401533,"about_ca_system_score_gemma":0.0010497851,"threshold_uncertainty_score":0.017471015},"labels":[],"label_agreement":null},{"id":"W3200693607","doi":"10.1007/978-3-030-87334-9_22","title":"Tolerance-Based Short Text Sentiment Classifier","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Computer science; Sentiment analysis; Classifier (UML); Artificial intelligence; Polarity (international relations); Natural language processing; Pattern recognition (psychology)","score_opus":0.02704774461835764,"score_gpt":0.26115359351646295,"score_spread":0.23410584889810532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200693607","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15296762,0.0024154603,0.8147416,0.00071365474,0.001672799,0.0005239141,0.0029341043,0.0065371357,0.017493691],"genre_scores_gemma":[0.6787774,0.0012759152,0.27187335,0.0005271966,0.0012807893,0.00048814996,0.013484925,0.0005855688,0.031706695],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991233,0.000094099836,0.00010659828,0.0001546398,0.00041599848,0.00010532996],"domain_scores_gemma":[0.9984339,0.00031913823,0.0001322697,0.000119762575,0.0008937939,0.00010117832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009019678,0.0008409246,0.0011703143,0.0019159201,0.0008064228,0.0012632014,0.00097419467,0.0007288376,0.0068134796],"category_scores_gemma":[0.0026790025,0.00017357344,0.0007504434,0.0014431422,0.00021257681,0.0014386846,0.0009292295,0.001003556,0.0056764227],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010041707,0.00032496985,0.0034152141,0.00023410283,0.00011427262,0.00019196597,0.00007378719,0.006324932,0.05257789,0.0026414108,0.029565947,0.90353143],"study_design_scores_gemma":[0.00010935558,0.0006474721,0.007993106,0.00006695146,0.00026017748,0.000607726,0.00021337949,0.9199904,0.04331779,0.006447877,0.020272473,0.000073201336],"about_ca_topic_score_codex":0.0011099306,"about_ca_topic_score_gemma":0.0015973366,"teacher_disagreement_score":0.0068134796,"about_ca_system_score_codex":0.0004379865,"about_ca_system_score_gemma":0.00071518985,"threshold_uncertainty_score":0.022793293},"labels":[],"label_agreement":null},{"id":"W3201284223","doi":"10.1162/coli_a_00433","title":"Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis","year":2022,"lang":"en","type":"article","venue":"Computational Linguistics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":93,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Harm; Emotion recognition; Sentiment analysis; Key (lock); Computer science; Psychology; Social psychology; Artificial intelligence; Computer security","score_opus":0.0701563089762093,"score_gpt":0.3225153650597269,"score_spread":0.2523590560835176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201284223","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034083616,0.0009790461,0.5513681,0.14617851,0.007276906,0.026665676,0.0033364354,0.003416376,0.2266953],"genre_scores_gemma":[0.19641711,0.0012025884,0.57502884,0.057478108,0.0038860405,0.040673193,0.0028484182,0.0010867757,0.12137898],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.85729086,0.09847997,0.011165653,0.0029288905,0.026528703,0.0036059553],"domain_scores_gemma":[0.7320235,0.117730185,0.010906533,0.02744966,0.10710792,0.0047821994],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.105793595,0.00077263435,0.0005910545,0.002721129,0.00462578,0.0074351686,0.0013949628,0.005754403,0.012211549],"category_scores_gemma":[0.15643564,0.0007394137,0.00091605575,0.0013316541,0.00449763,0.0035224361,0.0032487924,0.0062771523,0.012261188],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054907746,0.00054914085,0.004616072,0.0006860491,0.000044797496,0.0010145755,0.008839663,0.002934768,0.011294702,0.32382542,0.44243395,0.20321175],"study_design_scores_gemma":[0.00011542324,0.0003090029,0.0034748525,0.0012762798,0.000030098838,0.00066982856,0.0026354834,0.009259662,0.0087750815,0.06632021,0.9069677,0.00016637723],"about_ca_topic_score_codex":0.0021782492,"about_ca_topic_score_gemma":0.0022014764,"teacher_disagreement_score":0.9942456,"about_ca_system_score_codex":0.005014472,"about_ca_system_score_gemma":0.01603108,"threshold_uncertainty_score":0.5594967},"labels":[],"label_agreement":null},{"id":"W3201943163","doi":"10.3138/9781781793220-014","title":"12 Contrastive analyses of evaluation in text: Key issues in the design of an annotation system for attitude applicable to consumer reviews in English and Spanish","year":2013,"lang":"en","type":"book-chapter","venue":"University of Toronto Press eBooks","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Annotation; Computer science; Key (lock); Linguistics; Natural language processing; Contrastive analysis; Artificial intelligence; Information retrieval; Philosophy","score_opus":0.08577557456558975,"score_gpt":0.30636807022193335,"score_spread":0.2205924956563436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201943163","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02522316,0.0045430507,0.8291992,0.009868412,0.001189868,0.0011051054,0.0013066044,0.0032215454,0.12434305],"genre_scores_gemma":[0.21992557,0.0025248064,0.70522124,0.0018779201,0.00096099626,0.0017582356,0.0019597409,0.0027843602,0.06298716],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9931838,0.003960608,0.0004898141,0.0006831271,0.0015310656,0.0001515541],"domain_scores_gemma":[0.96722543,0.020370739,0.0007763491,0.001072431,0.010334175,0.00022087303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0105670225,0.0007684833,0.0005499235,0.00254716,0.0009910846,0.0042606113,0.0010015669,0.00092895597,0.0070744385],"category_scores_gemma":[0.029184058,0.0004846938,0.0005200754,0.0020568273,0.002195911,0.0045886226,0.001408853,0.002500633,0.004002239],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025015484,0.00013709225,0.0034348797,0.001844595,0.000074098185,0.0005725911,0.03148529,0.0009573699,0.042576585,0.104408465,0.09840213,0.71585673],"study_design_scores_gemma":[0.00007907975,0.0002157104,0.028434794,0.0017317221,0.00016015903,0.0012432345,0.015421519,0.031548727,0.06354118,0.1421701,0.7151747,0.0002790225],"about_ca_topic_score_codex":0.002400969,"about_ca_topic_score_gemma":0.003995315,"teacher_disagreement_score":0.0105670225,"about_ca_system_score_codex":0.002861398,"about_ca_system_score_gemma":0.0015374618,"threshold_uncertainty_score":0.05588442},"labels":[],"label_agreement":null},{"id":"W3203271898","doi":"10.3968/12199","title":"Study on Paratextual Elements in News Transediting: A Case Study on the Transediting Strategies in Reference News","year":2021,"lang":"en","type":"article","venue":"Canadian social science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Popularity; News values; Context (archaeology); China; Ideology; Political science; News media; Publishing; Advertising; History; Politics; Law; Business","score_opus":0.09950345459367124,"score_gpt":0.34342525644156513,"score_spread":0.2439218018478939,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203271898","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9854578,0.00030874845,0.0019606585,0.00067558006,0.00002938941,0.000082509425,0.00006060226,0.000016433254,0.011408295],"genre_scores_gemma":[0.99315816,0.00038909563,0.0020057817,0.00016526283,0.00003201637,0.00004581631,0.00010522392,0.000028601584,0.004070113],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.996198,0.0025146713,0.00017206649,0.00023529447,0.00061701646,0.00026292916],"domain_scores_gemma":[0.9742353,0.018977318,0.0025375232,0.00090159907,0.0025138797,0.00083435513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040908023,0.0003912874,0.0002670728,0.0026446874,0.0032847498,0.0038083545,0.00078275485,0.0011144064,0.0024017366],"category_scores_gemma":[0.017997751,0.00020414552,0.00028171774,0.0034227106,0.0020633384,0.003974032,0.001276573,0.0017263357,0.00044616987],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023784352,0.00052029226,0.055310275,0.00051761925,0.000041653897,0.011749482,0.8289717,0.00026553054,0.0071007744,0.011643607,0.0032178755,0.08042338],"study_design_scores_gemma":[0.000027844046,0.00028716066,0.0568108,0.00024742004,0.00008223933,0.0034023295,0.86831087,0.002415624,0.007211928,0.0025493912,0.058587592,0.00006674173],"about_ca_topic_score_codex":0.0040404024,"about_ca_topic_score_gemma":0.009270329,"teacher_disagreement_score":0.0040908023,"about_ca_system_score_codex":0.0022412082,"about_ca_system_score_gemma":0.001031257,"threshold_uncertainty_score":0.02163446},"labels":[],"label_agreement":null},{"id":"W3204405158","doi":"10.1108/ijius-06-2021-0040","title":"Scale to estimate the aspect-oriented sentiment polarity under anaphors influence (SPAI)","year":2021,"lang":"en","type":"article","venue":"International Journal of Intelligent Unmanned Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":true,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Tellabs (Canada)","funders":"","keywords":"Sentiment analysis; Polarity (international relations); Social media; Computer science; Natural language processing; Scale (ratio); Originality; Anaphora (linguistics); Resolution (logic); Artificial intelligence; Data science; Psychology; World Wide Web; Social psychology; Chemistry","score_opus":0.017281045246704596,"score_gpt":0.32963496680921,"score_spread":0.3123539215625054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204405158","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48388088,0.0024483097,0.46008497,0.0012353224,0.00067121634,0.0024489937,0.005083304,0.0021708193,0.041976105],"genre_scores_gemma":[0.8998679,0.0004911052,0.09284442,0.00014832025,0.00022987826,0.0009213371,0.0023067389,0.00006400282,0.0031263994],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977385,0.0006226309,0.00023892717,0.0003755627,0.0009078245,0.000116514006],"domain_scores_gemma":[0.9946108,0.0022682487,0.00073505094,0.00035807927,0.0018924688,0.00013542696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002780116,0.00087828794,0.00061009324,0.002198304,0.00064405345,0.0018841065,0.0006422566,0.00064799766,0.0048097735],"category_scores_gemma":[0.01738648,0.0002029883,0.00086874765,0.0016626994,0.0003605105,0.0016512296,0.001036728,0.0007954168,0.001749782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001179463,0.00048715228,0.17943035,0.0014033404,0.00057692034,0.00058346434,0.0020878846,0.017902786,0.021898849,0.0080232285,0.016583266,0.7498433],"study_design_scores_gemma":[0.00018602543,0.0013945294,0.24870667,0.00044783155,0.0007347379,0.0010092516,0.0043845824,0.6731859,0.017246302,0.024048548,0.028384702,0.0002709709],"about_ca_topic_score_codex":0.002017293,"about_ca_topic_score_gemma":0.0017939797,"teacher_disagreement_score":0.0048097735,"about_ca_system_score_codex":0.0005374672,"about_ca_system_score_gemma":0.0005311741,"threshold_uncertainty_score":0.016090274},"labels":[],"label_agreement":null},{"id":"W3204648175","doi":"10.1109/iccke54056.2021.9721440","title":"Extracting Major Topics of COVID-19 Related Tweets","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Concordia University","keywords":"Latent Dirichlet allocation; Topic model; Coronavirus disease 2019 (COVID-19); Social media; Focus (optics); Computer science; Telecommuting; Masking (illustration); Misinformation; Sentiment analysis; Data science; Information retrieval; Artificial intelligence; World Wide Web; Medicine; Disease; Computer security; Engineering","score_opus":0.05215145235084765,"score_gpt":0.3291354493518675,"score_spread":0.27698399700101983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204648175","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8497965,0.0022576645,0.08322618,0.0009542369,0.00041688618,0.0008927059,0.045763817,0.0023872037,0.014304787],"genre_scores_gemma":[0.8788335,0.001386656,0.06843386,0.0001154361,0.00052596704,0.00076713716,0.044266716,0.00020334151,0.005467479],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995993,0.000059658338,0.00004926737,0.00011165646,0.00008855704,0.00009163446],"domain_scores_gemma":[0.99916863,0.00035566464,0.00013861222,0.000039174218,0.00023282951,0.00006499704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047300837,0.00073829945,0.00035142037,0.006177063,0.00067272136,0.0008685647,0.00024173173,0.00048775133,0.0016736988],"category_scores_gemma":[0.0017934359,0.00020790352,0.0008047133,0.0035922353,0.00018490935,0.00078282645,0.00058422907,0.00042218095,0.00117842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015376206,0.00053188374,0.21750164,0.0020605582,0.00040015383,0.0026682138,0.008399468,0.010726217,0.12546292,0.008617738,0.077937774,0.54415584],"study_design_scores_gemma":[0.0001415556,0.0004413167,0.54291224,0.00029523717,0.0005880289,0.0020108758,0.010121206,0.26710322,0.04530058,0.013263671,0.117658496,0.0001635243],"about_ca_topic_score_codex":0.0039807702,"about_ca_topic_score_gemma":0.0042497315,"teacher_disagreement_score":0.006177063,"about_ca_system_score_codex":0.0004096309,"about_ca_system_score_gemma":0.0006248504,"threshold_uncertainty_score":0.007915199},"labels":[],"label_agreement":null},{"id":"W3204949062","doi":"10.18280/ria.350405","title":"Leveraging Pre-Trained Contextualized Word Embeddings to Enhance Sentiment Classification of Drug Reviews","year":2021,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Sentiment analysis; Natural language processing; Leverage (statistics); Artificial intelligence; Word (group theory); Word embedding; Word2vec; Context (archaeology); Machine learning; Information retrieval; Embedding; Linguistics","score_opus":0.05561749341251207,"score_gpt":0.34134956854952414,"score_spread":0.28573207513701204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204949062","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5721915,0.006976141,0.40344894,0.0010343114,0.0011735439,0.0003232872,0.002531259,0.0053631486,0.006957786],"genre_scores_gemma":[0.90709287,0.0014631519,0.08105767,0.00038383546,0.00036376354,0.00017428315,0.005099432,0.00017478084,0.004190214],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995425,0.000113311056,0.000055161392,0.00013722242,0.000089465364,0.00006233656],"domain_scores_gemma":[0.99915326,0.000320085,0.00012717284,0.00007579439,0.00028430694,0.00003932319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005840637,0.0014667314,0.00073996355,0.001388729,0.00019906275,0.0006747353,0.0004783474,0.000673591,0.0013330906],"category_scores_gemma":[0.0026129768,0.00022654925,0.0007346684,0.0009920647,0.00028454047,0.0015716972,0.00063685427,0.00092901266,0.0014467804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009817306,0.0006811143,0.017475462,0.000659698,0.00035183338,0.00045110993,0.0004026731,0.0488785,0.047120288,0.0023757352,0.014022995,0.8665988],"study_design_scores_gemma":[0.000039962164,0.00036395979,0.0069319205,0.00006635138,0.00013100778,0.00020109605,0.00019173571,0.97071373,0.011484061,0.0036359138,0.0061961706,0.000044125423],"about_ca_topic_score_codex":0.0018128183,"about_ca_topic_score_gemma":0.0033267066,"teacher_disagreement_score":0.0018128183,"about_ca_system_score_codex":0.00031496483,"about_ca_system_score_gemma":0.00044922635,"threshold_uncertainty_score":0.0044596195},"labels":[],"label_agreement":null},{"id":"W3204983869","doi":"10.2139/ssrn.3759258","title":"Language and Domain Specificity: A Chinese Financial Sentiment Dictionary","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Natural language processing; Domain (mathematical analysis); Computer science; Artificial intelligence; Linguistics; Philosophy; Mathematics","score_opus":0.0063743242374447555,"score_gpt":0.2288125989722031,"score_spread":0.22243827473475836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204983869","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6682912,0.004356586,0.065331265,0.0037675197,0.0014058128,0.0010305912,0.10748122,0.0026945632,0.14564122],"genre_scores_gemma":[0.847001,0.0031251588,0.05004636,0.0005358905,0.00028034626,0.0005950561,0.07181383,0.00059222826,0.026010184],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99984956,0.00002252294,0.00004393805,0.000026453177,0.000037637652,0.000019749525],"domain_scores_gemma":[0.9995851,0.00009885793,0.00003876345,0.000032511558,0.00020228174,0.000042499658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021743514,0.0004181407,0.00024356865,0.0043497314,0.0010308984,0.00067976903,0.00022195095,0.0001842455,0.006231542],"category_scores_gemma":[0.0009914449,0.00014357672,0.00021863863,0.006070639,0.000338946,0.000996433,0.0005734584,0.00046409533,0.0017997485],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063927926,0.000134172,0.031696107,0.0017546429,0.00006226453,0.0030582377,0.0074884864,0.0015836405,0.056107346,0.03689823,0.1930256,0.66755205],"study_design_scores_gemma":[0.00011041137,0.00015933876,0.11465911,0.00042482922,0.00018195478,0.0021257682,0.0040414887,0.014726479,0.012096874,0.0055018235,0.845849,0.00012294416],"about_ca_topic_score_codex":0.017380143,"about_ca_topic_score_gemma":0.018260159,"teacher_disagreement_score":0.017380143,"about_ca_system_score_codex":0.0007913686,"about_ca_system_score_gemma":0.0020661347,"threshold_uncertainty_score":0.034558},"labels":[],"label_agreement":null},{"id":"W3205996524","doi":"10.1142/s0219649222400081","title":"Extracting Feelings of People Regarding COVID-19 by Social Network Mining","year":2022,"lang":"en","type":"article","venue":"Journal of Information & Knowledge Management","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Publication; Lexicon; Subject (documents); Sentiment analysis; Computer science; Coronavirus disease 2019 (COVID-19); Feeling; Point (geometry); Social network (sociolinguistics); Social media; Data science; Advertising; World Wide Web; Artificial intelligence; Psychology; Business","score_opus":0.018152721377652288,"score_gpt":0.2785387382618968,"score_spread":0.2603860168842445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205996524","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8992749,0.0014581956,0.047121976,0.0012923672,0.00033511067,0.00087043695,0.025618088,0.0006331881,0.023395784],"genre_scores_gemma":[0.9479661,0.00079997326,0.03176497,0.0002076098,0.0002343065,0.00054909673,0.014311845,0.00003721174,0.00412908],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916184,0.00020702677,0.00011537652,0.00018120505,0.00024101081,0.00009353505],"domain_scores_gemma":[0.99866533,0.0006376832,0.0003196655,0.00006211234,0.00023091777,0.00008427475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062045717,0.00064557814,0.00038758307,0.004240413,0.0005544707,0.0011309825,0.00029888796,0.00057310105,0.002157121],"category_scores_gemma":[0.0030121312,0.00014180901,0.0005573085,0.00282887,0.00021609977,0.0013943007,0.0006448363,0.0005145955,0.0015848457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012768927,0.00085748965,0.36761746,0.0025570388,0.0006028221,0.0021808888,0.007873391,0.0039642863,0.035844408,0.006335329,0.04796173,0.52292824],"study_design_scores_gemma":[0.00009288993,0.00067668094,0.6879501,0.0005068628,0.0005295635,0.002283673,0.023282882,0.17212167,0.013117502,0.011682976,0.087560795,0.00019438108],"about_ca_topic_score_codex":0.0017579233,"about_ca_topic_score_gemma":0.003061092,"teacher_disagreement_score":0.004240413,"about_ca_system_score_codex":0.00043222995,"about_ca_system_score_gemma":0.0002666408,"threshold_uncertainty_score":0.0072162747},"labels":[],"label_agreement":null},{"id":"W3211769661","doi":"10.1007/978-3-031-37660-3_29","title":"Leveraging Sentiment Analysis Knowledge to Solve Emotion Detection Tasks","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Air Canada; Polytechnique Montréal","funders":"","keywords":"Computer science; Sentiment analysis; Artificial intelligence; Adapter (computing); Transformer; Task (project management); Natural language processing; Key (lock)","score_opus":0.030065510210739315,"score_gpt":0.27669674892741764,"score_spread":0.24663123871667833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211769661","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040566236,0.0015596483,0.9299721,0.00088076905,0.000905804,0.0003528609,0.0017721714,0.0066414294,0.01734895],"genre_scores_gemma":[0.29404512,0.0020095878,0.6758638,0.0006842759,0.00093327864,0.00031303952,0.0079624895,0.0008777453,0.017310627],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994362,0.000097356366,0.00005041258,0.00017855076,0.0001715407,0.00006592293],"domain_scores_gemma":[0.99890983,0.00044010684,0.000099032535,0.0001324407,0.00036573596,0.000052825108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092655787,0.0016781689,0.00089945714,0.0018953867,0.000488683,0.0017142515,0.00085042586,0.00079086993,0.0055992734],"category_scores_gemma":[0.0025485163,0.00043490526,0.0014648066,0.0014731481,0.00020853784,0.0023001889,0.0009637815,0.0015690849,0.006617828],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017251409,0.00044757166,0.0019946203,0.00028221583,0.00017947424,0.00022855694,0.00013778967,0.0045713056,0.094896495,0.0021784594,0.02878307,0.86612797],"study_design_scores_gemma":[0.000060481296,0.00031430487,0.005963651,0.00016775768,0.00044311647,0.00057263055,0.00032758206,0.82725954,0.09588155,0.028715465,0.040199835,0.00009406351],"about_ca_topic_score_codex":0.0011761922,"about_ca_topic_score_gemma":0.0022385877,"teacher_disagreement_score":0.0055992734,"about_ca_system_score_codex":0.00032175786,"about_ca_system_score_gemma":0.00048427633,"threshold_uncertainty_score":0.018731415},"labels":[],"label_agreement":null},{"id":"W3212697960","doi":"10.18653/v1/2021.findings-emnlp.278","title":"SentNoB: A Dataset for Analysing Sentiment on Noisy Bangla Texts","year":2021,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Bengali; Computer science; Benchmark (surveying); Artificial intelligence; Sentiment analysis; Natural language processing; Social media; Polarity (international relations); Artificial neural network; World Wide Web","score_opus":0.03168555132132317,"score_gpt":0.30803718387958934,"score_spread":0.27635163255826617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212697960","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0716285,0.001371807,0.008106934,0.00082664104,0.00060231594,0.00084013253,0.8926316,0.006627565,0.017364496],"genre_scores_gemma":[0.041517805,0.00033747894,0.014529337,0.00020410847,0.00011301735,0.0008164678,0.937367,0.00029099607,0.0048236446],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988789,0.00026710457,0.0002040392,0.00021070411,0.0003351477,0.00010400561],"domain_scores_gemma":[0.9980361,0.00054231024,0.0002749384,0.00027692146,0.0006734163,0.00019634255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006538492,0.0012803101,0.00063019537,0.0035474522,0.0010073944,0.0010656178,0.00097041926,0.0011146617,0.0080149425],"category_scores_gemma":[0.0039224657,0.00023188311,0.00047493688,0.0028930714,0.0003555896,0.0011636408,0.0011296694,0.0007436894,0.0098528005],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009519132,0.00060514093,0.018291654,0.003036678,0.00016618521,0.0009985944,0.0010844629,0.0018120008,0.018943133,0.00262497,0.8323027,0.1191826],"study_design_scores_gemma":[0.00031241818,0.0003176057,0.08507221,0.00051028526,0.000113567556,0.0010917942,0.0021166878,0.024845673,0.016403368,0.0034500656,0.86558807,0.00017824188],"about_ca_topic_score_codex":0.0059662643,"about_ca_topic_score_gemma":0.016058248,"teacher_disagreement_score":0.0080149425,"about_ca_system_score_codex":0.00083962834,"about_ca_system_score_gemma":0.0007886795,"threshold_uncertainty_score":0.026812673},"labels":[],"label_agreement":null},{"id":"W3213868581","doi":"10.26615/978-954-452-072-4_053","title":"Syntax and Themes: How Context Free Grammar Rules and SemanticWord Association Influence Book Success","year":2021,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Natural language processing; Syntax; Artificial intelligence; Context (archaeology); Grammar; Thesaurus; Set (abstract data type); Feature (linguistics); Information retrieval; Association rule learning; Semantic feature; Domain (mathematical analysis); Linguistics; Programming language","score_opus":0.008490743866938572,"score_gpt":0.218347921971438,"score_spread":0.20985717810449944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213868581","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91896635,0.00055493106,0.064902276,0.0009162791,0.00007693276,0.00008466252,0.0013504096,0.00065545563,0.012492714],"genre_scores_gemma":[0.98306954,0.00017766758,0.013436266,0.00009158793,0.000023695406,0.000027346665,0.0010119908,0.0001502832,0.0020117387],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99913603,0.00039545354,0.00003907325,0.00023035845,0.00012145846,0.00007765665],"domain_scores_gemma":[0.9904122,0.0078744795,0.00048502014,0.00040056105,0.000609191,0.00021848209],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002049094,0.00052435714,0.00044426243,0.0016030662,0.00047963407,0.002720689,0.0005876303,0.000711923,0.0028380842],"category_scores_gemma":[0.015275983,0.0003423775,0.00080331846,0.001510471,0.00068161037,0.0037145126,0.00067515817,0.001156239,0.0012156542],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061873434,0.0004378004,0.64696884,0.00034050466,0.00053098885,0.0011609902,0.004846857,0.06819924,0.010954848,0.020998187,0.008971796,0.2359713],"study_design_scores_gemma":[0.00005804915,0.00025226275,0.21081007,0.0001220404,0.00035714157,0.0006578361,0.0024234632,0.70598716,0.008023858,0.061609384,0.009561231,0.00013741027],"about_ca_topic_score_codex":0.011248708,"about_ca_topic_score_gemma":0.017551467,"teacher_disagreement_score":0.011248708,"about_ca_system_score_codex":0.00062306784,"about_ca_system_score_gemma":0.00089643983,"threshold_uncertainty_score":0.022366464},"labels":[],"label_agreement":null},{"id":"W3213919030","doi":"10.1051/e3sconf/202131705013","title":"Prediction of The Level of Public Trust in Government Policies in the 1<sup>st</sup>Quarter of The Covid 19 Pandemic using Sentiment Analysis","year":2021,"lang":"en","type":"article","venue":"E3S Web of Conferences","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Public opinion; Government (linguistics); Sentiment analysis; Pandemic; Coronavirus disease 2019 (COVID-19); Political science; Public policy; Social media; Public relations; Public administration; Politics; Geography; Law; Medicine; Computer science","score_opus":0.16760559025452865,"score_gpt":0.3066568777835184,"score_spread":0.13905128752898976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213919030","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99525696,0.000058780268,0.0005067601,0.0004249858,0.000018572213,0.000024030975,0.00097545417,0.000013145692,0.0027212414],"genre_scores_gemma":[0.9986136,0.000048608923,0.00030876105,0.000020495756,0.000009653786,0.000013013391,0.00063124485,0.000001844706,0.00035287088],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995745,0.00012185352,0.000044761404,0.00005194177,0.00008253809,0.0001243038],"domain_scores_gemma":[0.9960912,0.0015883029,0.0010461452,0.00009197938,0.0008555392,0.00032679355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011295199,0.00019847977,0.00022197294,0.0009355355,0.00034232234,0.0011629947,0.000129254,0.00038701855,0.00155535],"category_scores_gemma":[0.00570287,0.00010615611,0.00032822014,0.00078027375,0.00017050546,0.00075229903,0.0004078623,0.00054364116,0.0005088009],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029447558,0.00014922983,0.97321373,0.0000562747,0.00004822997,0.00014919796,0.0009099668,0.0027274662,0.00089035986,0.000437882,0.0029785659,0.01814472],"study_design_scores_gemma":[0.000012031386,0.0001808808,0.91861105,0.00005388166,0.000044184388,0.00007214235,0.0056998017,0.071241714,0.00111547,0.0005863932,0.002360455,0.000022026987],"about_ca_topic_score_codex":0.01694403,"about_ca_topic_score_gemma":0.014182222,"teacher_disagreement_score":0.01694403,"about_ca_system_score_codex":0.0011829946,"about_ca_system_score_gemma":0.000616759,"threshold_uncertainty_score":0.03369081},"labels":[],"label_agreement":null},{"id":"W3214436470","doi":"10.2308/ajpt-2020-060","title":"Using LIWC to Analyze Participants' Psychological Processing in Accounting JDM Research","year":2021,"lang":"en","type":"article","venue":"Auditing A Journal of Practice & Theory","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Psychology; Applied psychology; Audit; Psychological research; Clinical psychology; Social psychology; Accounting","score_opus":0.19903740233030637,"score_gpt":0.4942095726807556,"score_spread":0.29517217035044924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3214436470","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6595344,0.0002612816,0.3050888,0.0024408635,0.00022465507,0.0075022164,0.0024150854,0.0015741488,0.020958556],"genre_scores_gemma":[0.5770405,0.00017691027,0.3999232,0.0006104059,0.00006461991,0.01743316,0.001143659,0.00044314776,0.0031643894],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9565905,0.030111315,0.004487483,0.00195701,0.006031235,0.0008223729],"domain_scores_gemma":[0.8180931,0.1124035,0.017601652,0.015809827,0.034736224,0.0013556944],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.055612754,0.00046019995,0.0005854989,0.005188069,0.002551854,0.0038190319,0.0011502107,0.0005934663,0.0035865444],"category_scores_gemma":[0.15044585,0.00038364108,0.00039339028,0.005922871,0.0022876314,0.0025603315,0.0025917152,0.001596577,0.0011664451],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007557309,0.00047949483,0.04761792,0.0019411987,0.00008584997,0.00064307207,0.3092147,0.0016648873,0.051117763,0.023983285,0.019485315,0.5430108],"study_design_scores_gemma":[0.0003379627,0.0014836893,0.23876932,0.003151218,0.000196135,0.0009848876,0.32000935,0.053215813,0.112778775,0.08092408,0.18730104,0.00084774947],"about_ca_topic_score_codex":0.0022473307,"about_ca_topic_score_gemma":0.0037890326,"teacher_disagreement_score":0.94438726,"about_ca_system_score_codex":0.0019906464,"about_ca_system_score_gemma":0.0030287623,"threshold_uncertainty_score":0.2941119},"labels":[],"label_agreement":null},{"id":"W3215822937","doi":"10.1109/access.2021.3122025","title":"Urdu Sentiment Analysis via Multimodal Data Mining Based on Deep Learning Algorithms","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Northwestern Polytechnic; University of Windsor","funders":"","keywords":"Computer science; Sentiment analysis; Artificial intelligence; Context (archaeology); Urdu; Task (project management); Feature (linguistics); Feature extraction; Machine learning; Natural language processing","score_opus":0.053390415827058155,"score_gpt":0.3387090779877873,"score_spread":0.28531866216072915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215822937","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2776628,0.0014112836,0.69586897,0.0010526367,0.00039728536,0.00046354393,0.0034904398,0.0049067764,0.0147462115],"genre_scores_gemma":[0.78777367,0.0005582165,0.19906251,0.00031596413,0.0001984372,0.00038137785,0.0053216596,0.00014293919,0.0062452364],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996289,0.000079039964,0.000030656636,0.00008520103,0.00010993599,0.00006624],"domain_scores_gemma":[0.999556,0.000102682956,0.000058753067,0.000035868376,0.00022343139,0.00002327145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004890465,0.001077482,0.00057306036,0.001466362,0.0003831714,0.00068435294,0.0005852258,0.00050567585,0.0023077785],"category_scores_gemma":[0.001839869,0.00019818025,0.0006638938,0.00093750434,0.00017978836,0.0007929138,0.0007803455,0.00081040314,0.0012385225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000537294,0.0004532091,0.010246723,0.00021087703,0.0001660695,0.0004473835,0.0002788551,0.033848442,0.040380396,0.0029184003,0.017654732,0.89285773],"study_design_scores_gemma":[0.000015507274,0.00008276437,0.0034178176,0.000021818478,0.000036061138,0.000056336015,0.00013862445,0.9800061,0.009626016,0.0031834557,0.0033986487,0.000016845643],"about_ca_topic_score_codex":0.0033470178,"about_ca_topic_score_gemma":0.004701872,"teacher_disagreement_score":0.0033470178,"about_ca_system_score_codex":0.00052045204,"about_ca_system_score_gemma":0.0003958621,"threshold_uncertainty_score":0.0077202916},"labels":[],"label_agreement":null},{"id":"W3217501503","doi":"10.1080/08839514.2021.2000688","title":"Multimodal Sentiment Analysis Using Multi-tensor Fusion Network with Cross-modal Modeling","year":2021,"lang":"en","type":"article","venue":"Applied Artificial Intelligence","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Sentiment analysis; Modal; Artificial intelligence; Tensor (intrinsic definition); Modalities; Fusion; Feature (linguistics); Multimodality; Feature extraction; Machine learning; Pattern recognition (psychology); Data mining; Natural language processing","score_opus":0.07494880422815559,"score_gpt":0.327966712087125,"score_spread":0.25301790785896944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217501503","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05059568,0.0004036968,0.94590604,0.00037432037,0.000098376935,0.000094310984,0.00030102566,0.0005601734,0.0016663374],"genre_scores_gemma":[0.7835161,0.0007042067,0.20939036,0.00020798731,0.00019044115,0.00029559602,0.001645471,0.0001291079,0.00392074],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931693,0.00024640025,0.00003869456,0.00017383607,0.00013605035,0.00008808281],"domain_scores_gemma":[0.99923575,0.00018375512,0.000117134834,0.000058242553,0.00035372467,0.000051384268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014699104,0.001438406,0.00064782414,0.0013254004,0.00062846346,0.0010547367,0.0006952543,0.0005640104,0.0017089953],"category_scores_gemma":[0.0024149455,0.0003309437,0.001689717,0.0011277857,0.00046158012,0.0015421538,0.0009343201,0.001185622,0.00050197775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053823175,0.00033337393,0.0160066,0.00027661375,0.0006324217,0.00034229306,0.0007485927,0.46098188,0.030902447,0.025075985,0.010481888,0.45367974],"study_design_scores_gemma":[0.0000021366805,0.000017900824,0.00069818046,0.000004716146,0.000016810269,0.0000112522,0.00002369846,0.9955375,0.0007175381,0.002586147,0.00037560653,0.000008562655],"about_ca_topic_score_codex":0.008493917,"about_ca_topic_score_gemma":0.006473879,"teacher_disagreement_score":0.008493917,"about_ca_system_score_codex":0.0009139072,"about_ca_system_score_gemma":0.0007234711,"threshold_uncertainty_score":0.016888976},"labels":[],"label_agreement":null},{"id":"W32588857","doi":"10.1007/978-3-642-37256-8_12","title":"Using Google n-Grams to Expand Word-Emotion Association Lexicon","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Lexicon; Trigram; Computer science; Natural language processing; Word (group theory); Artificial intelligence; Association (psychology); Word Association; n-gram; Feeling; Speech recognition; Linguistics; Language model; Psychology","score_opus":0.03738423959584312,"score_gpt":0.27995138154211324,"score_spread":0.24256714194627013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W32588857","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14053991,0.0037898305,0.6236742,0.00127215,0.0035269628,0.0011400658,0.07181809,0.08760723,0.06663167],"genre_scores_gemma":[0.30010468,0.0019371777,0.5745167,0.0005480518,0.00049570657,0.00081892696,0.09215634,0.004228087,0.025194326],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949515,0.000067357585,0.00008243321,0.0001244079,0.00017544208,0.000055248227],"domain_scores_gemma":[0.999215,0.00021617544,0.000048435133,0.00008235732,0.00039246024,0.00004546698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043864458,0.0012874594,0.0006099354,0.0048034773,0.0007961983,0.0015098131,0.0004852666,0.0004531764,0.008735453],"category_scores_gemma":[0.002143541,0.0005388452,0.0009831927,0.0043679485,0.0002082783,0.0026632561,0.0015143688,0.0009861384,0.010378368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045978426,0.00018294869,0.0055721328,0.000992002,0.00017702612,0.00093858084,0.00092855503,0.0021576358,0.06386127,0.007267948,0.11925047,0.79821163],"study_design_scores_gemma":[0.00018965793,0.00034353885,0.026798561,0.000575883,0.000663433,0.0029318386,0.0019933945,0.23741789,0.07701383,0.05366336,0.59806836,0.00034022395],"about_ca_topic_score_codex":0.005973168,"about_ca_topic_score_gemma":0.016686676,"teacher_disagreement_score":0.008735453,"about_ca_system_score_codex":0.0005316652,"about_ca_system_score_gemma":0.0009777569,"threshold_uncertainty_score":0.029222965},"labels":[],"label_agreement":null},{"id":"W349662773","doi":"","title":"Adding a Capability to Extract Sentiment from Text Using HanDles","year":2012,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sentiment analysis; Computer science; Set (abstract data type); Information retrieval; Similarity (geometry); Test (biology); Test set; Natural language processing; Artificial intelligence; Product (mathematics); Data science; Image (mathematics); Mathematics","score_opus":0.050989612874940686,"score_gpt":0.3060639335656392,"score_spread":0.2550743206906985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W349662773","genre_codex":"software","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12814677,0.000739696,0.3826574,0.0014641737,0.00077231764,0.0010510534,0.015283732,0.44712162,0.022763107],"genre_scores_gemma":[0.40215236,0.0006056263,0.53452206,0.001122648,0.00074439234,0.0009797089,0.017048908,0.017500935,0.025323363],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989912,0.00014078185,0.00012252857,0.0002596501,0.0004178527,0.00006795811],"domain_scores_gemma":[0.99294925,0.004092044,0.0005530667,0.00094811677,0.0012557134,0.00020194169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018088561,0.0015955832,0.0005767626,0.004506014,0.0004496593,0.0024638004,0.00083802966,0.0006691796,0.014825727],"category_scores_gemma":[0.008127384,0.0005830631,0.00087472564,0.0012247575,0.00042235304,0.0027712923,0.0016946113,0.00088515953,0.0069278465],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012845922,0.00021835287,0.0151737565,0.0009957873,0.00027306372,0.0010615321,0.003085676,0.0034066543,0.076431334,0.0030807378,0.10774917,0.7872393],"study_design_scores_gemma":[0.00041666455,0.00095710054,0.030485436,0.0006086524,0.00047363195,0.0017098698,0.0016906076,0.33204758,0.2238115,0.016337272,0.39092982,0.0005319063],"about_ca_topic_score_codex":0.0016737746,"about_ca_topic_score_gemma":0.0030611064,"teacher_disagreement_score":0.014825727,"about_ca_system_score_codex":0.0004709081,"about_ca_system_score_gemma":0.00057580083,"threshold_uncertainty_score":0.049597025},"labels":[],"label_agreement":null},{"id":"W386759688","doi":"10.17705/1pais.02304","title":"Promote Product Reviews of High Quality on Ecommerce Sites","year":2010,"lang":"en","type":"article","venue":"Pacific Asia journal of the Association for Information Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Leverage (statistics); Quality (philosophy); Product (mathematics); Task (project management); User-generated content; Data science; World Wide Web; Artificial intelligence; Social media; Engineering","score_opus":0.028139175003491355,"score_gpt":0.2899114777481229,"score_spread":0.2617723027446316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W386759688","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8895305,0.008025706,0.042589918,0.0045572515,0.0009441449,0.0019314099,0.0039264443,0.008013648,0.04048083],"genre_scores_gemma":[0.90166783,0.0021781034,0.0731262,0.001005543,0.0013131798,0.00022049762,0.0030374022,0.000991165,0.016460186],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9954267,0.0014848936,0.000292412,0.00061045866,0.0019327945,0.0002528323],"domain_scores_gemma":[0.9358232,0.025568355,0.009743556,0.0035365417,0.022330258,0.0029980983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004832216,0.0009488667,0.0011178576,0.0066981693,0.0011303234,0.0047630575,0.00074968435,0.00125748,0.0050722486],"category_scores_gemma":[0.04148432,0.0005414986,0.0006616615,0.003981971,0.00046033773,0.003185842,0.0009238287,0.0010998128,0.004614907],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012719543,0.0013772917,0.20312515,0.0029152636,0.0006290463,0.0012452481,0.003070703,0.0036465046,0.040213857,0.0015070305,0.06486535,0.67613256],"study_design_scores_gemma":[0.0003200389,0.0026590165,0.76227605,0.00070786773,0.0009774953,0.0028449788,0.002737844,0.06506834,0.038341787,0.0037554991,0.119981706,0.00032938563],"about_ca_topic_score_codex":0.002574796,"about_ca_topic_score_gemma":0.008796795,"teacher_disagreement_score":0.0066981693,"about_ca_system_score_codex":0.00080470025,"about_ca_system_score_gemma":0.0008560361,"threshold_uncertainty_score":0.025555551},"labels":[],"label_agreement":null},{"id":"W4200496591","doi":"10.3233/jifs-219247","title":"A unified deep neuro-fuzzy approach for COVID-19 twitter sentiment classification","year":2021,"lang":"en","type":"article","venue":"Journal of Intelligent & Fuzzy Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University of Edmonton","funders":"","keywords":"Sentiment analysis; Computer science; Social media; Artificial intelligence; Exploit; Fuzzy logic; Coronavirus disease 2019 (COVID-19); Spelling; Natural language processing; Machine learning; World Wide Web; Linguistics; Computer security","score_opus":0.09427035429546292,"score_gpt":0.32311602457546873,"score_spread":0.2288456702800058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200496591","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10600601,0.0008793071,0.8862794,0.0008568178,0.00020275431,0.00015157918,0.00031578075,0.0008370387,0.0044712173],"genre_scores_gemma":[0.80993515,0.00043057493,0.18322152,0.00038610588,0.00015948557,0.00017452137,0.00066190097,0.00003406568,0.0049966937],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997235,0.000041591087,0.000027992126,0.00006550977,0.00008683543,0.00005449394],"domain_scores_gemma":[0.99968684,0.00007015318,0.000023442371,0.000017109374,0.00017805389,0.000024413945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078320515,0.00062942825,0.00067662494,0.0009663413,0.0006723416,0.0009877133,0.0009296971,0.0009726208,0.0014259701],"category_scores_gemma":[0.0011605788,0.00029594637,0.00079903443,0.0005621998,0.00023041708,0.00081314106,0.0006020249,0.0010659726,0.00055374263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034994492,0.000390941,0.0059398147,0.00012994141,0.00021763312,0.00020116627,0.00022541285,0.29052588,0.022808217,0.0053615826,0.0057326667,0.66811687],"study_design_scores_gemma":[0.000002984766,0.000030487265,0.00035993062,0.0000068828485,0.000013658748,0.000008727237,0.000023221131,0.9970438,0.0010813943,0.001095655,0.00032812555,0.000005115601],"about_ca_topic_score_codex":0.008669808,"about_ca_topic_score_gemma":0.010402795,"teacher_disagreement_score":0.008669808,"about_ca_system_score_codex":0.00084726664,"about_ca_system_score_gemma":0.0010176819,"threshold_uncertainty_score":0.017238677},"labels":[],"label_agreement":null},{"id":"W4205231996","doi":"10.2139/ssrn.4001976","title":"A Cross-Country Analysis of Macroeconomic Responses to COVID-19 Pandemic Using Twitter Sentiments","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada); York University","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Pandemic; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Sentiment analysis; Business; Virology; Computer science; Medicine; Outbreak; Infectious disease (medical specialty); Artificial intelligence","score_opus":0.0416779350523182,"score_gpt":0.3558782409164926,"score_spread":0.3142003058641744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205231996","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9917082,0.00012130004,0.000424071,0.00022878988,0.000042009502,0.000017160646,0.0054506166,0.00001979746,0.0019880868],"genre_scores_gemma":[0.99221087,0.0001192438,0.0003196548,0.00005743258,0.00004296188,0.0000178743,0.0060637095,0.0000087228855,0.0011594611],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99970275,0.00010143896,0.000017705606,0.000055208504,0.00003521879,0.00008758481],"domain_scores_gemma":[0.9984017,0.00060958526,0.0004323058,0.000079679565,0.00030311194,0.00017365716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072255987,0.0002982748,0.00029496386,0.0011976332,0.00029181736,0.0010878926,0.00020807028,0.00050210144,0.0023332238],"category_scores_gemma":[0.0017844932,0.00016219451,0.0005706961,0.0014617661,0.00022163373,0.0008144307,0.0006812266,0.00067412015,0.0008376274],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011364967,0.00038733103,0.94208705,0.00023039378,0.001339515,0.00090399117,0.0011473219,0.012598247,0.0042822026,0.0017592503,0.015730534,0.018397689],"study_design_scores_gemma":[0.000021425183,0.00032964896,0.9694243,0.00003821248,0.00024761911,0.00012394821,0.0035503882,0.020757165,0.00096098916,0.00023530322,0.0042752894,0.000035693538],"about_ca_topic_score_codex":0.014393716,"about_ca_topic_score_gemma":0.013348584,"teacher_disagreement_score":0.014393716,"about_ca_system_score_codex":0.0003085139,"about_ca_system_score_gemma":0.00022734467,"threshold_uncertainty_score":0.028619885},"labels":[],"label_agreement":null},{"id":"W4205402178","doi":"10.18280/ts.380630","title":"Evaluation of Logistics Service Quality: Sentiment Analysis of Comment Text Based on Multi-Level Graph Neural Network","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Education of the People's Republic of China","keywords":"Computer science; Graph; Sentiment analysis; Pillar; Service quality; Quality of service; Artificial neural network; Service (business); Artificial intelligence; Data mining; Natural language processing; Theoretical computer science; Computer network; Engineering","score_opus":0.19035983852261115,"score_gpt":0.36010736634932233,"score_spread":0.16974752782671118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205402178","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8908814,0.00028993143,0.10187568,0.00045733704,0.00017106347,0.00015404605,0.0007486163,0.00084202236,0.0045798873],"genre_scores_gemma":[0.9897295,0.00008553223,0.008505698,0.000036638256,0.000032474094,0.000030995066,0.00052137487,0.000013202092,0.0010446245],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996456,0.00009097518,0.000028375245,0.0000864476,0.00010096354,0.00004766938],"domain_scores_gemma":[0.9991967,0.00026092076,0.000108634216,0.00002898243,0.00036366621,0.00004107575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052118977,0.0006033484,0.00033931536,0.0012496213,0.0002163385,0.00045144442,0.00033550267,0.00044816092,0.001031974],"category_scores_gemma":[0.0019293929,0.000089539026,0.0004454109,0.00071774353,0.00022250417,0.0006662028,0.0002800574,0.00039160773,0.00034301946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023645796,0.00072332914,0.09445889,0.0005653901,0.0004253865,0.000837294,0.0010576607,0.14984852,0.080257356,0.0017511405,0.01063768,0.6570727],"study_design_scores_gemma":[0.000007096563,0.00009433837,0.011789885,0.000007068404,0.00003823108,0.000026925718,0.00014237553,0.98218566,0.0049973675,0.0003347716,0.0003658845,0.000010396919],"about_ca_topic_score_codex":0.006259913,"about_ca_topic_score_gemma":0.0063781436,"teacher_disagreement_score":0.006259913,"about_ca_system_score_codex":0.00070182217,"about_ca_system_score_gemma":0.00023216628,"threshold_uncertainty_score":0.01244694},"labels":[],"label_agreement":null},{"id":"W4205611454","doi":"10.1109/bigdata52589.2021.9671692","title":"Towards Multi-class Sentiment Analysis With Limited Labeled Data","year":2021,"lang":"en","type":"article","venue":"2021 IEEE International Conference on Big Data (Big Data)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Sentiment analysis; Computer science; Artificial intelligence; Machine learning; Class (philosophy); Transformer; Enhanced Data Rates for GSM Evolution; Labeled data; Baseline (sea); Data mining; Engineering","score_opus":0.3978350710327186,"score_gpt":0.377999222092047,"score_spread":0.019835848940671585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205611454","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05175516,0.00036063534,0.93610644,0.0007119487,0.00022506104,0.00030204165,0.0014078434,0.0045312364,0.004599721],"genre_scores_gemma":[0.39148632,0.00032844755,0.5940831,0.00053797883,0.0003477007,0.00043739515,0.00818417,0.00045378247,0.004141133],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978163,0.00075837236,0.00014957639,0.0005617136,0.00055402576,0.00016002945],"domain_scores_gemma":[0.9951015,0.0016966711,0.00057233166,0.0007952702,0.0016457337,0.00018852325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034850817,0.0015991781,0.0011489574,0.002621638,0.00096590596,0.001906172,0.0013344632,0.001227936,0.0018070155],"category_scores_gemma":[0.0075496095,0.00050670304,0.0012563935,0.0017045633,0.0006504745,0.003434193,0.0015837053,0.0023860831,0.0027909572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010151693,0.0008970593,0.012594121,0.00037205964,0.00025992657,0.00035008314,0.000715267,0.024780912,0.059169456,0.008404841,0.040491324,0.8509497],"study_design_scores_gemma":[0.00006710494,0.0001540857,0.0028310365,0.00003576375,0.000054930053,0.0001417794,0.00046470787,0.94988334,0.018785845,0.018891422,0.008658432,0.00003147429],"about_ca_topic_score_codex":0.0019821613,"about_ca_topic_score_gemma":0.0043754284,"teacher_disagreement_score":0.0034850817,"about_ca_system_score_codex":0.0007989739,"about_ca_system_score_gemma":0.001198226,"threshold_uncertainty_score":0.018431127},"labels":[],"label_agreement":null},{"id":"W4205619838","doi":"10.1109/icdmw53433.2021.00020","title":"Sentiment Analysis Using Part-of-Speech-Based Feature Extraction and Game-Theoretic Rough Sets","year":2021,"lang":"en","type":"article","venue":"2021 International Conference on Data Mining Workshops (ICDMW)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada; University of Regina","keywords":"Computer science; Sentiment analysis; Artificial intelligence; Naive Bayes classifier; Rough set; Support vector machine; Ambiguity; Decision tree; Probabilistic logic; Feature extraction; Natural language processing; Data mining; Machine learning","score_opus":0.09865835572894807,"score_gpt":0.3584949497619248,"score_spread":0.25983659403297676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205619838","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043385662,0.00037261273,0.9533162,0.0004996005,0.00007804884,0.00019452989,0.00030190294,0.00037184774,0.001479465],"genre_scores_gemma":[0.71593356,0.0004349501,0.28095743,0.00019326128,0.00012923157,0.0003627418,0.00074155245,0.00003508661,0.0012121978],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99792933,0.00080186425,0.00021000973,0.00032530728,0.0006085266,0.00012501184],"domain_scores_gemma":[0.9974486,0.0015310891,0.0003439496,0.00013982525,0.00047898781,0.000057497014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026262589,0.0010283701,0.0015135068,0.0023073754,0.00047730756,0.001569815,0.0009987896,0.0006487104,0.00073578244],"category_scores_gemma":[0.007257732,0.0003871802,0.0022873473,0.001273389,0.0005342586,0.0020626974,0.0006671609,0.0012048348,0.00030868396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006010846,0.00043015505,0.010859789,0.00057394354,0.00072534167,0.00050091627,0.0006985149,0.50519496,0.015848935,0.036840748,0.006657256,0.42106846],"study_design_scores_gemma":[0.000011564338,0.000054443175,0.0010312904,0.000010773803,0.000039729654,0.000034612098,0.00003092511,0.9881458,0.0011744036,0.008965041,0.00048278464,0.000018635339],"about_ca_topic_score_codex":0.0035644225,"about_ca_topic_score_gemma":0.0023704672,"teacher_disagreement_score":0.0035644225,"about_ca_system_score_codex":0.0011092674,"about_ca_system_score_gemma":0.00081140845,"threshold_uncertainty_score":0.013889134},"labels":[],"label_agreement":null},{"id":"W4205689905","doi":"10.1109/smc52423.2021.9658689","title":"ONSET: Opinion and Aspect Extraction System from Unlabelled Data","year":2021,"lang":"en","type":"article","venue":"2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Sentiment analysis; Artificial intelligence; Language model; Natural language processing; Quality (philosophy); Machine learning; State (computer science); Information extraction; Training set; Data modeling; Database","score_opus":0.10452784261160454,"score_gpt":0.3315195991100384,"score_spread":0.2269917564984339,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205689905","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1288663,0.0013049863,0.73617667,0.0012313684,0.000738347,0.0014795867,0.01737221,0.0984905,0.0143401],"genre_scores_gemma":[0.29357824,0.0007709904,0.64480036,0.0007890315,0.0005093603,0.0010512447,0.042630404,0.0011203453,0.014750064],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993412,0.00011139591,0.000067212975,0.00023424893,0.00019864774,0.00004742663],"domain_scores_gemma":[0.9986524,0.00037820067,0.00016972942,0.00013510526,0.0005957671,0.00006889415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001003035,0.0011724617,0.0006959341,0.0018443221,0.00039213581,0.00086625706,0.0008821939,0.00074691855,0.0032564895],"category_scores_gemma":[0.0030811087,0.0002957825,0.00074882753,0.000868991,0.00016404806,0.001746427,0.0007728979,0.0007826743,0.004290817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011871812,0.0004946746,0.008253515,0.00072560617,0.00017712741,0.00074684666,0.0005652994,0.0029392252,0.114973046,0.0018627796,0.09384286,0.7742318],"study_design_scores_gemma":[0.0003017945,0.00084233546,0.016345685,0.00013400558,0.00030447057,0.0008831701,0.0006990442,0.7493964,0.14741588,0.008759551,0.07475839,0.0001593886],"about_ca_topic_score_codex":0.0013614972,"about_ca_topic_score_gemma":0.0036162797,"teacher_disagreement_score":0.0032564895,"about_ca_system_score_codex":0.00043603315,"about_ca_system_score_gemma":0.0005312797,"threshold_uncertainty_score":0.01089406},"labels":[],"label_agreement":null},{"id":"W4205895159","doi":"10.1007/978-981-16-3945-6_79","title":"Twitter Sentiment Analysis of the 2019 Indian Election","year":2022,"lang":"en","type":"book-chapter","venue":"Smart innovation, systems and technologies","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Sentiment analysis; Politics; Political science; General election; Advertising; Public relations; Media studies; Law; Computer science; Business; Sociology; Artificial intelligence","score_opus":0.020422917837155465,"score_gpt":0.238456962948832,"score_spread":0.21803404511167654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205895159","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90964174,0.00054694276,0.0024653992,0.00288756,0.0010300352,0.00005429408,0.017562592,0.00042018117,0.06539121],"genre_scores_gemma":[0.9636785,0.00033919697,0.0013786122,0.000237259,0.00053559104,0.000038608552,0.011614683,0.0000999856,0.022077685],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99979717,0.000038034425,0.000011615264,0.00001907178,0.000079685226,0.00005445343],"domain_scores_gemma":[0.9994221,0.0001864678,0.00006562612,0.000021642683,0.0002554933,0.00004869948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031954006,0.00016776772,0.00015578141,0.0010085155,0.00056828116,0.00082424015,0.00017529538,0.00023122189,0.0037109638],"category_scores_gemma":[0.0013526452,0.00006633297,0.0002204346,0.001362241,0.00013992423,0.00044070752,0.00028623093,0.0004166575,0.0018183136],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017910033,0.00036065868,0.25684267,0.0008023271,0.0002511355,0.0017163437,0.0045463596,0.009884471,0.032943632,0.011997875,0.36780736,0.3110562],"study_design_scores_gemma":[0.000020639058,0.00019288542,0.71847564,0.00009559271,0.00014896659,0.00044605864,0.0090408875,0.07206023,0.0127310995,0.002017363,0.1846825,0.000088237815],"about_ca_topic_score_codex":0.015101183,"about_ca_topic_score_gemma":0.038105726,"teacher_disagreement_score":0.015101183,"about_ca_system_score_codex":0.0005030171,"about_ca_system_score_gemma":0.00029589926,"threshold_uncertainty_score":0.030026615},"labels":[],"label_agreement":null},{"id":"W4206384487","doi":"10.18280/ts.380625","title":"Classification of Image and Text Data Using Deep Learning-Based LSTM Model","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Sentence; Sentiment analysis; Artificial intelligence; Deep learning; Recurrent neural network; Task (project management); Reputation; Support vector machine; Natural language processing; Machine learning; Product (mathematics); Artificial neural network","score_opus":0.07928456933552505,"score_gpt":0.30311914790654737,"score_spread":0.22383457857102232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206384487","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41034824,0.0040568365,0.5598878,0.0019411045,0.001157679,0.00038681616,0.006209135,0.009992078,0.006020299],"genre_scores_gemma":[0.85209095,0.0012068829,0.13122006,0.0005386399,0.00027170862,0.00031419442,0.006867842,0.00012006108,0.007369595],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963844,0.00003359105,0.0000349654,0.00012102627,0.00008840052,0.00008353978],"domain_scores_gemma":[0.99938345,0.00022852168,0.00007594269,0.000060049493,0.00021775432,0.00003423322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047607502,0.0010750721,0.0006644394,0.0015025238,0.000247769,0.0008297912,0.0010080434,0.0016359736,0.0028056377],"category_scores_gemma":[0.0019961095,0.00031559213,0.0010871104,0.0015786887,0.00039171358,0.0012574747,0.00059830456,0.0014494205,0.0018441299],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007913351,0.0007256261,0.0058899694,0.00047478292,0.00014038043,0.0008990506,0.00018808855,0.16242145,0.055773385,0.001961824,0.011445205,0.75928885],"study_design_scores_gemma":[0.0000071835257,0.0000848617,0.0013429633,0.000018620109,0.000012730961,0.000066517234,0.000037903017,0.98959774,0.0068036816,0.0011010361,0.0009133613,0.000013441215],"about_ca_topic_score_codex":0.006325672,"about_ca_topic_score_gemma":0.0054635894,"teacher_disagreement_score":0.006325672,"about_ca_system_score_codex":0.00076125906,"about_ca_system_score_gemma":0.00052577,"threshold_uncertainty_score":0.012577713},"labels":[],"label_agreement":null},{"id":"W4210261633","doi":"10.1109/icmla52953.2021.00037","title":"Emotion Recognition and Sentiment Classification using BERT with Data Augmentation and Emotion Lexicon Enrichment","year":2021,"lang":"en","type":"article","venue":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Lexicon; Computer science; Sentiment analysis; Emotion detection; Emotion recognition; Emotion classification; Social media; Artificial intelligence; Natural language processing; World Wide Web","score_opus":0.13639950268741094,"score_gpt":0.35362016570033294,"score_spread":0.217220663012922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210261633","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15716109,0.0012795731,0.8180725,0.0014547663,0.00056613685,0.00052702916,0.0024344495,0.010782462,0.0077220104],"genre_scores_gemma":[0.713796,0.00047516442,0.26749918,0.0006424347,0.00032988153,0.00060829485,0.007276789,0.0002651866,0.009107058],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993844,0.00016771287,0.00004757909,0.00015510623,0.0001576008,0.00008758824],"domain_scores_gemma":[0.9986014,0.0005615172,0.000090588546,0.0002226066,0.00045623665,0.00006763243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012978263,0.0015007413,0.00092445826,0.0010421278,0.0005613693,0.0013228353,0.0016525735,0.0010747863,0.0028771055],"category_scores_gemma":[0.0037879848,0.00057096325,0.00097172614,0.00109215,0.00048675228,0.0024580609,0.0014343666,0.0017015436,0.0026349614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020425091,0.0012567267,0.010743265,0.000300761,0.00023488732,0.00045988525,0.0002998976,0.1605866,0.04765588,0.0061845304,0.019724643,0.75051033],"study_design_scores_gemma":[0.000016845172,0.00009067175,0.0007618035,0.0000066736916,0.000017556295,0.000036730587,0.000033467768,0.99046904,0.0043606968,0.0024188773,0.0017716998,0.000015938698],"about_ca_topic_score_codex":0.00507612,"about_ca_topic_score_gemma":0.0069815717,"teacher_disagreement_score":0.00507612,"about_ca_system_score_codex":0.0007807546,"about_ca_system_score_gemma":0.00085583254,"threshold_uncertainty_score":0.0100931525},"labels":[],"label_agreement":null},{"id":"W4210730697","doi":"10.1109/icmla52953.2021.00283","title":"Evaluating Sentiments in Social Media Comments on Tax Transformation in India using Deep Learning","year":2021,"lang":"en","type":"article","venue":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Key (lock); Government (linguistics); Quarter (Canadian coin); Social media; Computer science; Goods and services; Corporate governance; Deep learning; Artificial intelligence; Variation (astronomy); Transformation (genetics); Period (music); Natural language processing; Political science; Advertising; Data science; Business; Economics; World Wide Web; Economy; Computer security; Linguistics; Geography; Finance","score_opus":0.08807877699985492,"score_gpt":0.3898915717117282,"score_spread":0.3018127947118733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210730697","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9973911,0.000035160243,0.0003603779,0.00019046299,0.000031344665,0.00001037493,0.00050807855,0.000029070607,0.001443991],"genre_scores_gemma":[0.99791723,0.000051379342,0.0004319201,0.000045408273,0.000029800325,0.000008080842,0.00072933425,0.00000530567,0.0007814044],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997502,0.00007144939,0.000016705699,0.00003393287,0.00007132242,0.000056413304],"domain_scores_gemma":[0.99868745,0.00060925086,0.00024256173,0.000045950976,0.00031865473,0.00009608156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039118854,0.00024846237,0.00014969175,0.0006513905,0.00034331062,0.00068463245,0.00019959892,0.00029524777,0.00083332916],"category_scores_gemma":[0.0020594862,0.000075628974,0.0001873194,0.0006686255,0.00023392652,0.00047859715,0.0003759081,0.0005246456,0.0004373513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015983346,0.00086144125,0.77238667,0.00051298755,0.0002575059,0.0016902204,0.011742385,0.01187371,0.037774216,0.0012919796,0.018077036,0.14193344],"study_design_scores_gemma":[0.000016439019,0.00038336456,0.8466979,0.0000690276,0.00012543,0.00024583965,0.018924756,0.11417618,0.0116195455,0.00064803485,0.0070214095,0.00007203635],"about_ca_topic_score_codex":0.009027699,"about_ca_topic_score_gemma":0.017363433,"teacher_disagreement_score":0.009027699,"about_ca_system_score_codex":0.00048120136,"about_ca_system_score_gemma":0.00023548045,"threshold_uncertainty_score":0.017950296},"labels":[],"label_agreement":null},{"id":"W4211107845","doi":"10.2200/s00999ed3v01y202003hlt046","title":"Natural Language Processing for Social Media, Third Edition","year":2020,"lang":"en","type":"article","venue":"Synthesis lectures on human language technologies","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Social media; Interpersonal communication; Natural (archaeology); Computer science; Psychology; Sociology; World Wide Web; Communication; History","score_opus":0.038861291742286454,"score_gpt":0.3072510998608941,"score_spread":0.26838980811860763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4211107845","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030972697,0.07958679,0.75138414,0.007998838,0.02218891,0.00046749468,0.033753004,0.046986375,0.05453711],"genre_scores_gemma":[0.019978156,0.045202278,0.5624397,0.0024102214,0.008892822,0.0011871058,0.07240054,0.009545055,0.2779441],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998769,0.00015748391,0.00020136299,0.00025199674,0.0005627772,0.000057392495],"domain_scores_gemma":[0.9956216,0.0017217075,0.00017210803,0.000696995,0.0016604122,0.00012718608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019237065,0.0019081107,0.0015531013,0.0036553666,0.0007113788,0.00552362,0.0019057865,0.001188446,0.055785377],"category_scores_gemma":[0.0072749443,0.0015235607,0.0014267483,0.0027516452,0.0009402363,0.0060580787,0.0014934295,0.002118755,0.041906815],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006337187,0.00006119187,0.00020863026,0.0011278401,0.00006434467,0.000117316704,0.00011865285,0.0013234309,0.004356781,0.008200429,0.61955136,0.36480653],"study_design_scores_gemma":[0.000037094796,0.000058201294,0.002019629,0.0005415319,0.00007941075,0.0006428754,0.00018516806,0.019813938,0.00592804,0.032057036,0.93856525,0.000071843235],"about_ca_topic_score_codex":0.009690083,"about_ca_topic_score_gemma":0.013451624,"teacher_disagreement_score":0.055785377,"about_ca_system_score_codex":0.0012090943,"about_ca_system_score_gemma":0.0020395012,"threshold_uncertainty_score":0.18662065},"labels":[],"label_agreement":null},{"id":"W4220904073","doi":"10.18280/ts.390133","title":"Emoji-Integrated Polyseme Probabilistic Analysis Model: Sentiment Analysis of Short Review Texts on Library Service Quality","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Education of the People's Republic of China","keywords":"Sentiment analysis; Emoji; Computer science; Probabilistic logic; Natural language processing; Context (archaeology); Quality (philosophy); Artificial intelligence; Service (business); Word (group theory); Support vector machine; Service quality; Information retrieval; World Wide Web; Linguistics; Social media","score_opus":0.0445279112455248,"score_gpt":0.3004829424385609,"score_spread":0.2559550311930361,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220904073","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6380842,0.0023002878,0.34244838,0.0016793817,0.00031952997,0.00054283714,0.001953299,0.0008332736,0.011838858],"genre_scores_gemma":[0.9740777,0.00043573018,0.022218844,0.000087379885,0.00013190547,0.00019129073,0.0006076865,0.000017330984,0.0022320228],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989089,0.0003892259,0.0001239095,0.00022183494,0.00029008574,0.00006603894],"domain_scores_gemma":[0.9975051,0.0012249096,0.00041433412,0.00006928968,0.00073407433,0.000052382056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017610757,0.00057269476,0.00038615064,0.0020650998,0.0002890925,0.0010075547,0.00038406282,0.00040155376,0.0012002256],"category_scores_gemma":[0.0062424485,0.00014848722,0.0006723336,0.0015515189,0.00026422524,0.0011152502,0.0003225143,0.00050471583,0.00043637474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017356535,0.0007403206,0.0936002,0.001567343,0.0010753263,0.0010100263,0.0032187698,0.07480029,0.045073353,0.012207622,0.015052155,0.749919],"study_design_scores_gemma":[0.000026186268,0.0003699248,0.06262331,0.00008371482,0.00025322635,0.00023752598,0.00046792856,0.9232339,0.004747406,0.0040676175,0.0038332904,0.000055988734],"about_ca_topic_score_codex":0.0034928953,"about_ca_topic_score_gemma":0.0042617666,"teacher_disagreement_score":0.0034928953,"about_ca_system_score_codex":0.0006788187,"about_ca_system_score_gemma":0.00049962266,"threshold_uncertainty_score":0.009313583},"labels":[],"label_agreement":null},{"id":"W4221020671","doi":"10.18280/isi.270111","title":"Fine-Tuning BERT Based Approach for Multi-Class Sentiment Analysis on Twitter Emotion Data","year":2022,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Microblogging; Natural language processing; Sentiment analysis; Encoder; Social media; Machine learning; Transformer; Slang; World Wide Web; Linguistics","score_opus":0.0696460497840425,"score_gpt":0.28874683735230655,"score_spread":0.21910078756826407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221020671","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27210814,0.0009606245,0.6789836,0.0013498713,0.0005441821,0.0005719155,0.0036135556,0.032923505,0.00894454],"genre_scores_gemma":[0.80089456,0.00031703187,0.17375506,0.0005050395,0.00017499365,0.0003605591,0.009749822,0.0009412274,0.0133017115],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944645,0.0001207036,0.000040542804,0.00016519228,0.00011493807,0.00011219494],"domain_scores_gemma":[0.99941266,0.00020025122,0.000044964985,0.000083847335,0.00021359079,0.00004471337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001143246,0.0014556624,0.00056106626,0.0011275536,0.00059811096,0.0009928345,0.00094400416,0.0008353326,0.0040072985],"category_scores_gemma":[0.0023906184,0.00039231675,0.0009922406,0.00058873836,0.0003003929,0.0017663444,0.0008363374,0.0017516953,0.003661718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011218773,0.00068246573,0.013692065,0.0002130829,0.0001830805,0.0003531634,0.00033277014,0.15998305,0.03828207,0.0038536892,0.027191542,0.7541112],"study_design_scores_gemma":[0.0000143381785,0.00007715965,0.0012519062,0.000009462138,0.000012230117,0.00004419491,0.000083096136,0.988203,0.0064879507,0.0019134107,0.001889082,0.000014113341],"about_ca_topic_score_codex":0.008755919,"about_ca_topic_score_gemma":0.015329759,"teacher_disagreement_score":0.008755919,"about_ca_system_score_codex":0.0011570917,"about_ca_system_score_gemma":0.0010493482,"threshold_uncertainty_score":0.01740992},"labels":[],"label_agreement":null},{"id":"W4225654607","doi":"10.2139/ssrn.4009739","title":"Joint Theme and Event Based Rating Model for Identifying Relevant Influencers on Twitter: COVID-19 Case Study","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Influencer marketing; Event (particle physics); Coronavirus disease 2019 (COVID-19); Theme (computing); Joint (building); Social media; Psychology; Computer science; Medicine; Engineering; Business; World Wide Web; Marketing","score_opus":0.08643912294364758,"score_gpt":0.35210441522693486,"score_spread":0.2656652922832873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225654607","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.831837,0.0014250064,0.15080532,0.0017506528,0.00027311215,0.0006502157,0.004087143,0.001041296,0.008130228],"genre_scores_gemma":[0.9447545,0.00024111621,0.047611818,0.000072945375,0.000097960576,0.00012867995,0.0027479082,0.000037263893,0.0043078223],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987496,0.00051568367,0.00009934961,0.00024136326,0.00024270317,0.00015133237],"domain_scores_gemma":[0.9966185,0.0021686144,0.00019106269,0.00021639526,0.0006272488,0.00017814894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034159152,0.0007810873,0.0010119384,0.0014310946,0.00070564024,0.0013354938,0.0012300389,0.0011805765,0.0020230485],"category_scores_gemma":[0.005906789,0.0002290996,0.0009464005,0.0013811213,0.00025505383,0.0014262532,0.0007554016,0.0011854362,0.0010538719],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005392698,0.005139329,0.20070703,0.0011125342,0.0009557473,0.0021749742,0.0014989913,0.2593414,0.0137145575,0.010947644,0.032535315,0.46647984],"study_design_scores_gemma":[0.000032811186,0.00019383438,0.007198769,0.000011328887,0.00007483536,0.00012658908,0.00023393267,0.9889893,0.0009992828,0.0010194303,0.0011007773,0.000019030576],"about_ca_topic_score_codex":0.0144486595,"about_ca_topic_score_gemma":0.020230556,"teacher_disagreement_score":0.0144486595,"about_ca_system_score_codex":0.00087606814,"about_ca_system_score_gemma":0.00082345353,"threshold_uncertainty_score":0.02872914},"labels":[],"label_agreement":null},{"id":"W4225983683","doi":"10.1109/access.2022.3160172","title":"A Study of the Application of Weight Distributing Method Combining Sentiment Dictionary and TF-IDF for Text Sentiment Analysis","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":86,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Social Science Fund of China; National Office for Philosophy and Social Sciences","keywords":"Sentiment analysis; Computer science; Artificial intelligence; Weighting; Sentence; Natural language processing; tf–idf; Bag-of-words model; Information retrieval; Term (time)","score_opus":0.02981395879643067,"score_gpt":0.3434620506057906,"score_spread":0.31364809180935993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225983683","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14582077,0.0023421976,0.84235185,0.00038382597,0.00025780138,0.0002973087,0.00010389609,0.0006360378,0.0078063505],"genre_scores_gemma":[0.6413814,0.0022260214,0.35171714,0.00014306708,0.000205843,0.00019696131,0.00024277187,0.00011590955,0.0037709123],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99820006,0.0005866769,0.00012633119,0.00030300824,0.0006984556,0.00008539635],"domain_scores_gemma":[0.99665797,0.0016786128,0.00015705967,0.00018778285,0.0012414891,0.00007714953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003009366,0.00057712005,0.00059454114,0.0018514039,0.00042930426,0.0008720459,0.00052270613,0.00047137047,0.0012445647],"category_scores_gemma":[0.008878855,0.00024429895,0.00045682405,0.002011326,0.00037813652,0.0023126274,0.00036087478,0.000433099,0.00041741724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026403554,0.00017493291,0.009334788,0.00038872162,0.000102804304,0.00013688528,0.0004567534,0.010786189,0.04190804,0.0059985043,0.0025509258,0.92789745],"study_design_scores_gemma":[0.00006519197,0.0005473548,0.013320392,0.00007078648,0.00015045541,0.0005412909,0.0005694087,0.93213177,0.035932522,0.0043199514,0.012285131,0.00006573317],"about_ca_topic_score_codex":0.0028588793,"about_ca_topic_score_gemma":0.0018242134,"teacher_disagreement_score":0.003009366,"about_ca_system_score_codex":0.00056450034,"about_ca_system_score_gemma":0.00051314366,"threshold_uncertainty_score":0.015915215},"labels":[],"label_agreement":null},{"id":"W4225984337","doi":"10.1109/fg52635.2021.9666994","title":"The Many Faces of Anger: A Multicultural Video Dataset of Negative Emotions in the Wild (MFA-Wild)","year":2021,"lang":"en","type":"article","venue":"2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Anger; Multiculturalism; Psychology; Computer science; Social psychology","score_opus":0.05518946392789806,"score_gpt":0.3148131111698275,"score_spread":0.25962364724192943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225984337","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41871312,0.004046672,0.016079612,0.0016420273,0.0021675278,0.0014053405,0.5150656,0.005487275,0.035392847],"genre_scores_gemma":[0.2833032,0.0010533793,0.03754967,0.0010248605,0.00042113836,0.0012291871,0.6592298,0.0005835911,0.015605138],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99915075,0.00018341263,0.00006156762,0.00021739407,0.00023722323,0.00014953694],"domain_scores_gemma":[0.9989957,0.00015423642,0.0001048475,0.00019854747,0.0003630723,0.00018354092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074398675,0.0013267,0.000523616,0.001387477,0.001076084,0.0008481519,0.00083898724,0.0013502126,0.0036035671],"category_scores_gemma":[0.0020195548,0.00018949632,0.00067704933,0.00088331517,0.0004946591,0.0008872409,0.0014363689,0.0010965529,0.0042438637],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015041253,0.0009493395,0.04983196,0.0022175726,0.0003645764,0.0020710966,0.003915411,0.0019014432,0.049827628,0.002206187,0.70983857,0.17537206],"study_design_scores_gemma":[0.00016760729,0.00070917665,0.3958073,0.0007953692,0.00019613579,0.0047898153,0.010270567,0.02299063,0.028214173,0.002571171,0.53315884,0.0003291599],"about_ca_topic_score_codex":0.016530154,"about_ca_topic_score_gemma":0.061395604,"teacher_disagreement_score":0.016530154,"about_ca_system_score_codex":0.00073874707,"about_ca_system_score_gemma":0.00042230758,"threshold_uncertainty_score":0.03286791},"labels":[],"label_agreement":null},{"id":"W4226143637","doi":"10.1109/eebda53927.2022.9745018","title":"Deep Learning Approaches on Multimodal Sentiment Analysis","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Sentiment analysis; Artificial intelligence; Deep learning; Natural language processing","score_opus":0.09570675983202043,"score_gpt":0.28682947428888506,"score_spread":0.19112271445686463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226143637","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016755762,0.0022462592,0.9709976,0.0013907589,0.00018365715,0.000072128954,0.0002722653,0.00063563616,0.0074459855],"genre_scores_gemma":[0.6220896,0.0048570978,0.35551366,0.0008831886,0.0007968439,0.00028840665,0.0011622356,0.00022370536,0.01418523],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955434,0.00016064549,0.000029689576,0.00008866223,0.000099546356,0.000067154004],"domain_scores_gemma":[0.9994759,0.00020174315,0.00006878664,0.000041835036,0.00017858784,0.000033194436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011790331,0.0009283212,0.0006243563,0.0012950907,0.0004384452,0.001231807,0.00088060746,0.0008349363,0.0031791588],"category_scores_gemma":[0.002183859,0.00032198953,0.0009643431,0.001129282,0.0005218785,0.0015846172,0.0011934737,0.0015615752,0.00084493903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017269539,0.00018878093,0.0040204017,0.00049505365,0.0003797312,0.00020525207,0.0005435758,0.17093872,0.013148195,0.059277385,0.018361084,0.7322692],"study_design_scores_gemma":[0.00000762125,0.00002801388,0.0008936632,0.000038303217,0.00003665369,0.000028121593,0.00008900636,0.9621866,0.001689637,0.029910348,0.005079378,0.000012710887],"about_ca_topic_score_codex":0.003352048,"about_ca_topic_score_gemma":0.0035639259,"teacher_disagreement_score":0.003352048,"about_ca_system_score_codex":0.0009510394,"about_ca_system_score_gemma":0.0005503658,"threshold_uncertainty_score":0.010635376},"labels":[],"label_agreement":null},{"id":"W4229647441","doi":"10.1007/978-1-4939-7131-2_101137","title":"Social Media Analysis","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Social media; Sociology; Computer science; World Wide Web","score_opus":0.04553654995249079,"score_gpt":0.2760890981779233,"score_spread":0.23055254822543253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229647441","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009300189,0.009387247,0.39657447,0.0076952633,0.0035831374,0.00078641,0.0078992555,0.007972272,0.5568018],"genre_scores_gemma":[0.12856713,0.015857816,0.2883798,0.0028504513,0.005941236,0.0009648845,0.018678516,0.004315718,0.53444445],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99886227,0.00019865218,0.000054870678,0.00021952375,0.0005910368,0.000073597264],"domain_scores_gemma":[0.9986656,0.0004907542,0.00006819067,0.00022083665,0.00050269306,0.000051837553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012950439,0.0012805022,0.0005397782,0.0067055053,0.0012969348,0.004027381,0.0009619449,0.0008271088,0.032319],"category_scores_gemma":[0.0036508439,0.00037270624,0.00092029176,0.0045333332,0.00062958425,0.0038916883,0.0019059252,0.0011172013,0.02913752],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025630134,0.00006049877,0.00093262596,0.00027777546,0.00006232415,0.000076195436,0.0003738194,0.00068734016,0.0034433524,0.055814058,0.18927152,0.7489749],"study_design_scores_gemma":[0.000006215311,0.000021110112,0.0024965538,0.00028003065,0.000056483026,0.00030613423,0.000604641,0.01315577,0.007514266,0.09722516,0.8782948,0.00003877467],"about_ca_topic_score_codex":0.0015022518,"about_ca_topic_score_gemma":0.0025000076,"teacher_disagreement_score":0.032319,"about_ca_system_score_codex":0.0008906713,"about_ca_system_score_gemma":0.0009280616,"threshold_uncertainty_score":0.10811788},"labels":[],"label_agreement":null},{"id":"W4230648055","doi":"10.32920/ryerson.14644251.v1","title":"The impact of sentiment analysis on decision outcomes - an empirical investigation","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sentiment analysis; Computer science; Product (mathematics); Service (business); Quality (philosophy); Filter (signal processing); Star (game theory); Marketing; Artificial intelligence; Business; Mathematics","score_opus":0.05895634017010145,"score_gpt":0.39300296649493077,"score_spread":0.33404662632482934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230648055","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9968184,0.000110474386,0.0008679346,0.00008194537,0.000015803345,0.00018342093,0.00012405615,0.000007152403,0.0017907213],"genre_scores_gemma":[0.99711895,0.0001310828,0.0016110871,0.00006111504,0.000022998931,0.00032877427,0.0002086059,0.000011489793,0.0005058458],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9893757,0.0060504037,0.00072401,0.0010369174,0.0023350555,0.00047785553],"domain_scores_gemma":[0.72618544,0.24272974,0.017213322,0.0039859316,0.0074253054,0.0024602087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014672966,0.0005228016,0.00075586035,0.0009312868,0.0005853385,0.0019899237,0.00069562317,0.0007686591,0.0033669104],"category_scores_gemma":[0.08629455,0.0002671273,0.0010185087,0.0008428186,0.0012827794,0.0014594455,0.0010303942,0.0023299372,0.0006402322],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.01370184,0.020395711,0.73220855,0.003246877,0.0013730506,0.0011680332,0.016069911,0.01329662,0.020419985,0.004119737,0.0038989861,0.17010069],"study_design_scores_gemma":[0.00049777067,0.017436415,0.9072526,0.00037074139,0.00091464847,0.0003579107,0.00845387,0.046486124,0.00970848,0.003738582,0.0045917486,0.00019109025],"about_ca_topic_score_codex":0.0010102383,"about_ca_topic_score_gemma":0.000904053,"teacher_disagreement_score":0.014672966,"about_ca_system_score_codex":0.0011284202,"about_ca_system_score_gemma":0.00068218686,"threshold_uncertainty_score":0.07759899},"labels":[],"label_agreement":null},{"id":"W4230724048","doi":"10.1007/978-1-4939-7131-2_351","title":"Multi-classifier System for Sentiment Analysis and Opinion Mining","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Sentiment analysis; Classifier (UML); Computer science; Artificial intelligence","score_opus":0.05001574669295378,"score_gpt":0.2822478646491177,"score_spread":0.23223211795616394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230724048","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011189599,0.002958438,0.9385155,0.0006723831,0.0015312574,0.00047540257,0.0022204828,0.019562323,0.022874711],"genre_scores_gemma":[0.067927465,0.0019199346,0.8580391,0.00063675956,0.00070259714,0.0005157753,0.006300356,0.0009112716,0.063046835],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999244,0.00007183376,0.00007166188,0.00020342102,0.00034683725,0.000062159466],"domain_scores_gemma":[0.9990915,0.00017081886,0.000035595236,0.000084314815,0.00056385895,0.000053883672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009127107,0.00092755683,0.0011130165,0.001970719,0.00097302726,0.0016561247,0.0013080854,0.0011337283,0.020111904],"category_scores_gemma":[0.0015825198,0.0003898067,0.000999947,0.0019353542,0.00015117507,0.0018568176,0.00091496407,0.0013336213,0.018101644],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001269935,0.00014447646,0.00073652324,0.00023565404,0.00007408949,0.00015772766,0.00007863023,0.0013876911,0.027738808,0.0034030061,0.06491293,0.9010035],"study_design_scores_gemma":[0.00008094947,0.00028574246,0.0052217185,0.00021990525,0.0003553522,0.0012846529,0.00019816562,0.5938289,0.10309565,0.01985025,0.2754303,0.00014831251],"about_ca_topic_score_codex":0.0020153143,"about_ca_topic_score_gemma":0.0037643318,"teacher_disagreement_score":0.020111904,"about_ca_system_score_codex":0.0006335748,"about_ca_system_score_gemma":0.0008007593,"threshold_uncertainty_score":0.06728095},"labels":[],"label_agreement":null},{"id":"W4231185195","doi":"10.1007/978-1-4939-7131-2_101058","title":"Sentiment Classification","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Artificial intelligence; Computer science","score_opus":0.052260458300062096,"score_gpt":0.27068508943774594,"score_spread":0.21842463113768384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231185195","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011381205,0.0044766716,0.3227763,0.0034436455,0.0041005937,0.0012259169,0.008072581,0.012300133,0.63222295],"genre_scores_gemma":[0.06870454,0.0068136463,0.22837658,0.0020132712,0.0023392905,0.00090569817,0.025919192,0.0023638976,0.6625639],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950993,0.00004692594,0.000034261295,0.00014017169,0.00022089208,0.00004773169],"domain_scores_gemma":[0.9994241,0.000089634865,0.00003074735,0.00009125094,0.0003243904,0.00003992451],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007022708,0.0012718502,0.000683086,0.00220031,0.0009651838,0.0025601194,0.0010591863,0.0006323614,0.079276726],"category_scores_gemma":[0.0020017666,0.00035077432,0.0009856415,0.0020149548,0.00023767403,0.0024070712,0.0012298836,0.0012601375,0.08666377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052826905,0.00008184754,0.00036476317,0.00021986624,0.000021331038,0.00004462536,0.00006788746,0.00044031997,0.009273635,0.010565296,0.20906892,0.76979864],"study_design_scores_gemma":[0.000020980164,0.00007566777,0.0019781115,0.00026309237,0.00007060859,0.00044297767,0.00015363174,0.018717952,0.019402048,0.024449851,0.93438315,0.000041898646],"about_ca_topic_score_codex":0.00085267384,"about_ca_topic_score_gemma":0.0015055815,"teacher_disagreement_score":0.079276726,"about_ca_system_score_codex":0.0006517656,"about_ca_system_score_gemma":0.0007790594,"threshold_uncertainty_score":0.26520705},"labels":[],"label_agreement":null},{"id":"W4231331946","doi":"10.1007/978-1-4939-7131-2_101390","title":"Twitter Opinion Mining","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Sentiment analysis; Computer science; Data science; World Wide Web; Artificial intelligence","score_opus":0.05100208249066389,"score_gpt":0.2780008784946445,"score_spread":0.2269987960039806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231331946","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012151048,0.016246622,0.34579912,0.013352686,0.0074129393,0.0009499821,0.01430972,0.010647084,0.57913077],"genre_scores_gemma":[0.09280931,0.02468037,0.22251701,0.004038282,0.0076575126,0.0009708538,0.03502862,0.0026666305,0.6096314],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949217,0.00006462727,0.00002886115,0.0001041063,0.00026293434,0.000047304464],"domain_scores_gemma":[0.9993569,0.0001789679,0.000039936218,0.000088698005,0.00029567917,0.000039842602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007489695,0.0011228529,0.00059072225,0.0031365335,0.0010277991,0.0026292934,0.00092347886,0.00077997043,0.02945191],"category_scores_gemma":[0.002811808,0.0004143754,0.0007823488,0.0036319692,0.00026240115,0.0035527102,0.0014016251,0.0010745842,0.039724134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003060872,0.000045708835,0.0006755686,0.00023332614,0.000027730766,0.000053911564,0.00012594991,0.00055312866,0.002506229,0.01564356,0.30195907,0.67814523],"study_design_scores_gemma":[0.000011013457,0.000026621157,0.0018571744,0.00020701275,0.000045700912,0.00029404153,0.00024083297,0.01737803,0.0061148605,0.043900494,0.92988884,0.00003532409],"about_ca_topic_score_codex":0.0013448182,"about_ca_topic_score_gemma":0.00302635,"teacher_disagreement_score":0.02945191,"about_ca_system_score_codex":0.0006997299,"about_ca_system_score_gemma":0.00067174144,"threshold_uncertainty_score":0.09852642},"labels":[],"label_agreement":null},{"id":"W4231638995","doi":"10.1007/978-1-4939-7131-2_100822","title":"Opinion Mining","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science","score_opus":0.04051981448005819,"score_gpt":0.26715581555881257,"score_spread":0.22663600107875437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231638995","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005237883,0.007954948,0.5129045,0.0040922053,0.0024997802,0.00071780384,0.0037428855,0.006417233,0.45643276],"genre_scores_gemma":[0.06855343,0.016630186,0.3929948,0.0026608692,0.0029050026,0.00074475654,0.015052464,0.0016813034,0.49877724],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994098,0.00008115222,0.000038176393,0.00016097036,0.00026970138,0.000040293013],"domain_scores_gemma":[0.99917334,0.00025729492,0.000039275103,0.00013518128,0.00035330042,0.000041531708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008121739,0.0010643192,0.0006043294,0.0022695356,0.0008053458,0.0026305616,0.0012861664,0.00071521784,0.050626237],"category_scores_gemma":[0.003140036,0.00036218777,0.0009791614,0.002254157,0.00032916648,0.0032605615,0.0013452824,0.0012649664,0.051898725],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029126479,0.000064649685,0.00022239499,0.0003093635,0.00002407537,0.000056709396,0.00012755222,0.0005602354,0.0035906413,0.028225552,0.14577794,0.8210118],"study_design_scores_gemma":[0.000015979898,0.000046735728,0.0008470098,0.00028810144,0.000055188957,0.0005048994,0.0001936346,0.01547857,0.009796197,0.082835115,0.8899063,0.000032312684],"about_ca_topic_score_codex":0.00058484136,"about_ca_topic_score_gemma":0.0009837338,"teacher_disagreement_score":0.050626237,"about_ca_system_score_codex":0.0006126927,"about_ca_system_score_gemma":0.0006660837,"threshold_uncertainty_score":0.16936165},"labels":[],"label_agreement":null},{"id":"W4232428191","doi":"10.1007/978-1-4939-7131-2_100961","title":"Recommender Engine","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Recommender system; Information retrieval; World Wide Web","score_opus":0.03600037641108487,"score_gpt":0.24853407369000877,"score_spread":0.2125336972789239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232428191","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010210247,0.0063322214,0.37944648,0.002831545,0.0022137729,0.0011384544,0.008729695,0.037744872,0.5513527],"genre_scores_gemma":[0.039743774,0.0062499396,0.23480694,0.0013802208,0.0007010388,0.00028560177,0.01655867,0.0021717069,0.6981022],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994752,0.000047573656,0.000028547855,0.00012928716,0.000283443,0.000035956775],"domain_scores_gemma":[0.99945694,0.00007638206,0.000015785105,0.00020350471,0.00020805986,0.000039413433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068577327,0.0010234265,0.00096486794,0.0015923309,0.0007237873,0.0020839483,0.0017778719,0.0013685517,0.09381519],"category_scores_gemma":[0.0019697167,0.0005710046,0.0009131104,0.0018729308,0.00014723832,0.002867599,0.0009842302,0.0012917584,0.106294975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011093418,0.00021158806,0.00061356026,0.00044662156,0.00010373469,0.000120012635,0.00007207048,0.0019177299,0.0093673235,0.035711177,0.3546314,0.5966939],"study_design_scores_gemma":[0.000045012664,0.000072369854,0.00061618706,0.00010152142,0.000097779695,0.00052610156,0.000039818813,0.032706898,0.0093674725,0.014556141,0.9418183,0.00005240208],"about_ca_topic_score_codex":0.0029369644,"about_ca_topic_score_gemma":0.006207958,"teacher_disagreement_score":0.09381519,"about_ca_system_score_codex":0.00044825554,"about_ca_system_score_gemma":0.00071354373,"threshold_uncertainty_score":0.31384307},"labels":[],"label_agreement":null},{"id":"W4233158516","doi":"10.4018/978-1-4666-8614-4.ch014","title":"Analytics and Performance Measurement Frameworks for Social Customer Relationship Management","year":2015,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Analytics; Big data; Computer science; Data science; Knowledge management; Customer relationship management; Process management; Business; Data mining","score_opus":0.09766581246738561,"score_gpt":0.2959052575165106,"score_spread":0.198239445049125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233158516","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005763836,0.06564178,0.6977374,0.026772162,0.0015465961,0.0006943648,0.0014701638,0.0015925325,0.19878116],"genre_scores_gemma":[0.22199662,0.09053076,0.6315072,0.0043820967,0.00444981,0.0021912686,0.0028081306,0.0007147367,0.041419353],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9931425,0.0024933177,0.00045854689,0.00068944646,0.002877769,0.00033844952],"domain_scores_gemma":[0.99290985,0.0040875487,0.0007282119,0.0005214276,0.001537289,0.00021565745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00784097,0.002395761,0.0009859862,0.008134575,0.0016576665,0.009081543,0.0023252545,0.0026190646,0.0077201338],"category_scores_gemma":[0.012982613,0.000636479,0.0012483716,0.009501904,0.003430085,0.013055772,0.0036855761,0.004592015,0.0033617525],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001010963,0.00006497658,0.00090849324,0.0005722333,0.000035604673,0.00005972648,0.00089314644,0.004227994,0.00031859515,0.84419733,0.022767125,0.12594464],"study_design_scores_gemma":[0.0000061455094,0.000056474157,0.002156811,0.0016754336,0.000034261706,0.0002414064,0.0014537371,0.029574355,0.00064590824,0.6677168,0.2963517,0.0000870225],"about_ca_topic_score_codex":0.004220126,"about_ca_topic_score_gemma":0.0026658208,"teacher_disagreement_score":0.009081543,"about_ca_system_score_codex":0.0061096996,"about_ca_system_score_gemma":0.0031378695,"threshold_uncertainty_score":0.044329166},"labels":[],"label_agreement":null},{"id":"W4233489102","doi":"10.1007/978-1-4614-6170-8_100780","title":"Sentiment Detection and Analysis","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Sentiment analysis; Computer science; Artificial intelligence; Natural language processing; Pattern recognition (psychology)","score_opus":0.014917228796105735,"score_gpt":0.22781560548431362,"score_spread":0.21289837668820788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233489102","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005487727,0.004084179,0.80892676,0.0017319612,0.00157661,0.00067030685,0.0023240265,0.0080551095,0.1671433],"genre_scores_gemma":[0.047656734,0.007958946,0.54741013,0.0013221991,0.0014518374,0.00070813374,0.0094349375,0.002658967,0.3813981],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993018,0.00007649587,0.000042050644,0.00016178013,0.00036501305,0.000052817824],"domain_scores_gemma":[0.9992912,0.0001676427,0.000032119508,0.00010403674,0.00037309434,0.00003184418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091722567,0.0012468648,0.0008179289,0.0025884619,0.000783714,0.0027235136,0.0010748595,0.00074891816,0.029741973],"category_scores_gemma":[0.0021143428,0.0005349523,0.0009739514,0.0018731733,0.00040944238,0.0022257778,0.0014215418,0.0012292606,0.044426795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003562731,0.00005704238,0.00031285963,0.00024998223,0.00002815934,0.00006106963,0.00013893652,0.0006592458,0.016398221,0.017487854,0.106831215,0.85773975],"study_design_scores_gemma":[0.000015546382,0.00007166227,0.0021514576,0.00026253873,0.00007613996,0.0007261727,0.00024135761,0.0286209,0.052020982,0.0653716,0.850377,0.000064691034],"about_ca_topic_score_codex":0.0005711404,"about_ca_topic_score_gemma":0.0009088312,"teacher_disagreement_score":0.029741973,"about_ca_system_score_codex":0.0005204461,"about_ca_system_score_gemma":0.000753709,"threshold_uncertainty_score":0.09949678},"labels":[],"label_agreement":null},{"id":"W4234412303","doi":"10.1007/978-1-4939-7131-2_101199","title":"Social Recommender System","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Recommender system; Computer science; World Wide Web; Internet privacy","score_opus":0.05517995274480055,"score_gpt":0.27521532510210683,"score_spread":0.22003537235730627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234412303","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027831554,0.016351484,0.4776331,0.0060762223,0.00431163,0.0007709098,0.0025251717,0.008404737,0.4560951],"genre_scores_gemma":[0.1693741,0.014391657,0.26889816,0.0015985309,0.0021161237,0.00035590745,0.0048909867,0.00060190435,0.53777266],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955624,0.0000778432,0.000023232762,0.00010264833,0.00020829616,0.000031781386],"domain_scores_gemma":[0.999522,0.000094132,0.000018756758,0.00010920857,0.00021389342,0.0000420636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007473165,0.0005914284,0.00065415434,0.0011454157,0.00095422973,0.0015296377,0.00091625954,0.0009799037,0.021169946],"category_scores_gemma":[0.0014881493,0.00024707476,0.0005544141,0.0014149009,0.00020435701,0.0023287195,0.0009028801,0.00087607396,0.019778155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000062563704,0.0001761206,0.0009246321,0.00041757396,0.00009238648,0.00011610715,0.00014920002,0.0018284798,0.0059499494,0.04394158,0.17123213,0.7751093],"study_design_scores_gemma":[0.00003695983,0.00015096065,0.0019079556,0.00018145674,0.00016757104,0.00070411986,0.0001771269,0.0794671,0.007530062,0.038048435,0.87154466,0.0000836165],"about_ca_topic_score_codex":0.0024146193,"about_ca_topic_score_gemma":0.0052644294,"teacher_disagreement_score":0.021169946,"about_ca_system_score_codex":0.00052478665,"about_ca_system_score_gemma":0.00064468477,"threshold_uncertainty_score":0.07082045},"labels":[],"label_agreement":null},{"id":"W4234955752","doi":"10.32920/ryerson.14644251","title":"The impact of sentiment analysis on decision outcomes - an empirical investigation","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sentiment analysis; Computer science; Product (mathematics); Service (business); Quality (philosophy); Filter (signal processing); Star (game theory); Artificial intelligence; Marketing; Business; Mathematics","score_opus":0.05895634017010145,"score_gpt":0.39300296649493077,"score_spread":0.33404662632482934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234955752","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9968184,0.000110474386,0.0008679346,0.00008194537,0.000015803345,0.00018342093,0.00012405615,0.000007152403,0.0017907213],"genre_scores_gemma":[0.99711895,0.0001310828,0.0016110871,0.00006111504,0.000022998931,0.00032877427,0.0002086059,0.000011489793,0.0005058458],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9893757,0.0060504037,0.00072401,0.0010369174,0.0023350555,0.00047785553],"domain_scores_gemma":[0.72618544,0.24272974,0.017213322,0.0039859316,0.0074253054,0.0024602087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014672966,0.0005228016,0.00075586035,0.0009312868,0.0005853385,0.0019899237,0.00069562317,0.0007686591,0.0033669104],"category_scores_gemma":[0.08629455,0.0002671273,0.0010185087,0.0008428186,0.0012827794,0.0014594455,0.0010303942,0.0023299372,0.0006402322],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.01370184,0.020395711,0.73220855,0.003246877,0.0013730506,0.0011680332,0.016069911,0.01329662,0.020419985,0.004119737,0.0038989861,0.17010069],"study_design_scores_gemma":[0.00049777067,0.017436415,0.9072526,0.00037074139,0.00091464847,0.0003579107,0.00845387,0.046486124,0.00970848,0.003738582,0.0045917486,0.00019109025],"about_ca_topic_score_codex":0.0010102383,"about_ca_topic_score_gemma":0.000904053,"teacher_disagreement_score":0.014672966,"about_ca_system_score_codex":0.0011284202,"about_ca_system_score_gemma":0.00068218686,"threshold_uncertainty_score":0.07759899},"labels":[],"label_agreement":null},{"id":"W4236542667","doi":"10.4018/978-1-7998-0414-7.ch077","title":"Sentiment Recognition in Customer Reviews Using Deep Learning","year":2019,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Deep learning; Artificial intelligence; Sentiment analysis; Computer science; Machine learning; Artificial neural network; Support vector machine; Convolutional neural network; Natural language processing","score_opus":0.05057857399141688,"score_gpt":0.2861576284545343,"score_spread":0.23557905446311742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236542667","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33006486,0.0065807975,0.61297303,0.0022366017,0.0008387576,0.00032177605,0.0034514405,0.0056743794,0.037858356],"genre_scores_gemma":[0.8037301,0.0029152036,0.16896716,0.00050562015,0.0002954529,0.00011707604,0.0034262692,0.00015317183,0.019889938],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997862,0.000044875615,0.000014556687,0.000042797335,0.0000801673,0.00003142443],"domain_scores_gemma":[0.99964035,0.000096433156,0.000054717806,0.000019592275,0.00017376652,0.000015217049],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003829476,0.0006076423,0.00042868918,0.00067788875,0.0001495468,0.00082243694,0.00031044116,0.00040016143,0.002530803],"category_scores_gemma":[0.00094432617,0.00019722451,0.000505372,0.000673852,0.00011918349,0.0006591399,0.00033083677,0.00064906635,0.0022466923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002915769,0.00021134324,0.008450187,0.0005052116,0.00015266926,0.0002758247,0.00021976554,0.019062677,0.061968528,0.0022352363,0.025017066,0.88161],"study_design_scores_gemma":[0.000015915652,0.00014060021,0.011636897,0.000086349755,0.00007593194,0.00020917348,0.00017375291,0.94225806,0.028912172,0.0042866906,0.012172333,0.000032050146],"about_ca_topic_score_codex":0.00181911,"about_ca_topic_score_gemma":0.003297369,"teacher_disagreement_score":0.002530803,"about_ca_system_score_codex":0.00038479816,"about_ca_system_score_gemma":0.00023889587,"threshold_uncertainty_score":0.008466423},"labels":[],"label_agreement":null},{"id":"W4237211318","doi":"10.1109/wi-iat.2012.122","title":"Verb Oriented Sentiment Classification","year":2012,"lang":"en","type":"article","venue":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Sentiment analysis; Computer science; Natural language processing; Verb; Sentence; Artificial intelligence; Noun; Feature (linguistics); Computational linguistics; Linguistics","score_opus":0.07566559340921215,"score_gpt":0.3233779520571548,"score_spread":0.24771235864794267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237211318","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27252576,0.0039324802,0.580386,0.0020020497,0.0031266443,0.006102402,0.021493094,0.006972908,0.10345868],"genre_scores_gemma":[0.5790048,0.0022264812,0.35040715,0.0009283944,0.0012333249,0.002421357,0.03412649,0.0003852123,0.029266795],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988533,0.00021807078,0.00013979641,0.00017974847,0.00048688223,0.0001221196],"domain_scores_gemma":[0.99867815,0.0002748213,0.00014784129,0.000079519785,0.00076920853,0.000050467672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012173965,0.0009206144,0.00067167333,0.0032576972,0.00071859796,0.0018505977,0.0006010683,0.0006879675,0.007905092],"category_scores_gemma":[0.0031612332,0.0001565423,0.00097324746,0.0022994592,0.00023838527,0.0011513403,0.00059435697,0.0006679106,0.005254241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004351453,0.00056040287,0.01348524,0.0006935442,0.00017964609,0.00031642916,0.00037372755,0.0021275922,0.042716045,0.007517741,0.049521092,0.8820733],"study_design_scores_gemma":[0.00038200253,0.0014360357,0.09746773,0.0007234231,0.000777726,0.0019962012,0.002472221,0.4631942,0.085017905,0.0528658,0.2934316,0.00023512755],"about_ca_topic_score_codex":0.0010177759,"about_ca_topic_score_gemma":0.001125295,"teacher_disagreement_score":0.007905092,"about_ca_system_score_codex":0.0005847563,"about_ca_system_score_gemma":0.0005770279,"threshold_uncertainty_score":0.02644515},"labels":[],"label_agreement":null},{"id":"W4237338686","doi":"10.1007/978-1-4939-7131-2_101054","title":"Sentiment Analysis","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Sentiment analysis; Computer science; Natural language processing","score_opus":0.024614225672764976,"score_gpt":0.25476112696417136,"score_spread":0.23014690129140639,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237338686","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006353313,0.004337422,0.37983337,0.0026509198,0.0026557506,0.0007489496,0.005071502,0.008647715,0.58970106],"genre_scores_gemma":[0.05973107,0.0075742365,0.22083032,0.0018637134,0.0019659044,0.0007125716,0.015223544,0.003330018,0.6887687],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994605,0.00006261505,0.00003438923,0.00012946356,0.00027289128,0.00004018093],"domain_scores_gemma":[0.9994168,0.000118791,0.000029738252,0.00008162728,0.0003248428,0.000028047554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076539436,0.0011349758,0.0005539585,0.0026222465,0.0008498479,0.0025912689,0.00081703305,0.00055575534,0.06513904],"category_scores_gemma":[0.002146321,0.0003264694,0.00092808594,0.0019584657,0.00031233547,0.002130707,0.00126705,0.0011329008,0.06706145],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003856587,0.000050851588,0.000340856,0.00031048525,0.000036536316,0.00006619634,0.0001810038,0.00057824457,0.011631739,0.025625542,0.2212678,0.7398723],"study_design_scores_gemma":[0.000011395006,0.000035086374,0.001446514,0.00020168335,0.000056321725,0.000373044,0.00020128867,0.0082861865,0.013738096,0.033783093,0.94183546,0.0000317715],"about_ca_topic_score_codex":0.00061954686,"about_ca_topic_score_gemma":0.0010725607,"teacher_disagreement_score":0.06513904,"about_ca_system_score_codex":0.00057621894,"about_ca_system_score_gemma":0.0006545887,"threshold_uncertainty_score":0.21791178},"labels":[],"label_agreement":null},{"id":"W4238203830","doi":"10.32920/ryerson.14653809.v1","title":"Automatic classification of the emotional content of web documents","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Bigram; Computer science; Natural language processing; Artificial intelligence; Word (group theory); Web page; Web content; Information retrieval; World Wide Web; Trigram; Linguistics","score_opus":0.07545543630957381,"score_gpt":0.2937467731545012,"score_spread":0.2182913368449274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238203830","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60266733,0.0013936928,0.3815606,0.00049655134,0.00020987033,0.00032837442,0.0012672258,0.0039142435,0.008162113],"genre_scores_gemma":[0.840363,0.0005386255,0.15262221,0.000111093585,0.00019021257,0.00016990617,0.002477805,0.0001652285,0.0033618507],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991898,0.0002452292,0.00006337307,0.00015926991,0.00025580643,0.000086512744],"domain_scores_gemma":[0.99825686,0.0007522496,0.00019634255,0.00011843471,0.0006250011,0.00005106674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006820864,0.0005015965,0.0005482838,0.0026887513,0.0003965474,0.0010832781,0.00037066138,0.00052996824,0.0012996218],"category_scores_gemma":[0.0030308275,0.00015716988,0.00044730733,0.0010289885,0.00024111483,0.0011269582,0.00044092897,0.00048016175,0.0012285408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047595892,0.00032307094,0.013479601,0.000363854,0.00009511602,0.0002982628,0.000348651,0.005330632,0.13417995,0.0018940421,0.0054869936,0.83772385],"study_design_scores_gemma":[0.000059868362,0.00028082126,0.07093678,0.000095239775,0.00014110794,0.00060651667,0.00071088655,0.8123421,0.09690959,0.008269322,0.009571636,0.00007610533],"about_ca_topic_score_codex":0.0005611441,"about_ca_topic_score_gemma":0.00061784656,"teacher_disagreement_score":0.0026887513,"about_ca_system_score_codex":0.00033266898,"about_ca_system_score_gemma":0.000248551,"threshold_uncertainty_score":0.004347682},"labels":[],"label_agreement":null},{"id":"W4239948474","doi":"10.1007/978-1-4939-7131-2_101060","title":"Sentiment Detection and Analysis","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Sentiment analysis; Computer science; Artificial intelligence","score_opus":0.01865475474095229,"score_gpt":0.23990602908560002,"score_spread":0.22125127434464772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239948474","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059429766,0.004411966,0.7909523,0.0021931245,0.0021464038,0.0006994832,0.002731334,0.008944586,0.18197773],"genre_scores_gemma":[0.05164491,0.008685656,0.529454,0.0015947814,0.0020256084,0.00075650134,0.011005582,0.0031617475,0.39167124],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992982,0.00007636556,0.00004389401,0.00017118691,0.00035701526,0.000053415926],"domain_scores_gemma":[0.9992194,0.00017985277,0.00003520028,0.000116993266,0.0004111027,0.000037452715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009494074,0.0012467193,0.0007772001,0.0026457743,0.000791248,0.0028455995,0.0010516584,0.00076901953,0.032456916],"category_scores_gemma":[0.002327779,0.0005222191,0.0010142728,0.0018810852,0.00040093335,0.0023383407,0.0014686595,0.0013280679,0.04883282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038666687,0.00006460653,0.00032960848,0.000270599,0.000029603465,0.00006400773,0.00014388683,0.0006418503,0.016873017,0.018861707,0.12764275,0.83503973],"study_design_scores_gemma":[0.00001477285,0.00006541832,0.0019248758,0.00025135803,0.00007133597,0.00063722045,0.0002089439,0.024735078,0.0442384,0.061653286,0.8661423,0.000056990615],"about_ca_topic_score_codex":0.0005005863,"about_ca_topic_score_gemma":0.00077547226,"teacher_disagreement_score":0.032456916,"about_ca_system_score_codex":0.0005176863,"about_ca_system_score_gemma":0.0007482344,"threshold_uncertainty_score":0.10857916},"labels":[],"label_agreement":null},{"id":"W4240118341","doi":"10.1162/coli_r_00161","title":"Publications Received","year":2013,"lang":"en","type":"article","venue":"Computational Linguistics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science","score_opus":0.027100620379252753,"score_gpt":0.27570986432487815,"score_spread":0.2486092439456254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240118341","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004646721,0.008873792,0.0013551482,0.0062841037,0.022974748,0.00022638582,0.007474007,0.0026445675,0.94970256],"genre_scores_gemma":[0.0012836859,0.0050272765,0.00084659754,0.002372196,0.0027457909,0.00007974408,0.005400378,0.0009410529,0.9813033],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99672854,0.00024385391,0.0002707038,0.00064487517,0.0018537253,0.00025839807],"domain_scores_gemma":[0.99386966,0.00047612286,0.00029472102,0.0008166378,0.003432193,0.0011105665],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0014396989,0.0017503684,0.0018910266,0.004460081,0.0020912318,0.016135521,0.0023543513,0.0032262574,0.80928415],"category_scores_gemma":[0.00840755,0.00078706833,0.0012810248,0.0057807285,0.00086390047,0.007423947,0.004201639,0.003481594,0.84746933],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021851052,0.000028880933,0.000108418266,0.00036250002,0.0000067398055,0.00006994638,0.000044934415,0.00004498029,0.00021512869,0.0036175048,0.898413,0.09706603],"study_design_scores_gemma":[0.0000034847944,0.000008435983,0.000112616945,0.000116187795,0.0000022069626,0.0000707027,0.000026043354,0.000012871768,0.000048002486,0.0006502685,0.998944,0.0000051470843],"about_ca_topic_score_codex":0.0014542429,"about_ca_topic_score_gemma":0.0024559211,"teacher_disagreement_score":0.19071585,"about_ca_system_score_codex":0.002003154,"about_ca_system_score_gemma":0.003626665,"threshold_uncertainty_score":0.27203304},"labels":[],"label_agreement":null},{"id":"W4241800255","doi":"10.1007/978-1-4614-6170-8_110111","title":"Sentiment Analysis","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Sentiment analysis; Computer science; Artificial intelligence; Natural language processing","score_opus":0.018998062458324878,"score_gpt":0.23999229475423065,"score_spread":0.22099423229590576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241800255","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0060231956,0.0042537074,0.40340868,0.002297323,0.0021938975,0.0007504364,0.004517112,0.008168217,0.5683875],"genre_scores_gemma":[0.057158545,0.0073074116,0.23297705,0.001649711,0.0015934808,0.00068325957,0.013635894,0.0029295897,0.682065],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994666,0.0000613089,0.00003259798,0.0001236601,0.00027605705,0.000039685114],"domain_scores_gemma":[0.9994789,0.000107780084,0.000026526166,0.000072892544,0.00028981286,0.000024089686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007423084,0.0011468743,0.0005731895,0.002551536,0.00083333196,0.002494228,0.0008423854,0.0005467609,0.059863843],"category_scores_gemma":[0.0019471211,0.00033574537,0.00089827395,0.0019173741,0.0003212463,0.0020742815,0.0012588071,0.0010817674,0.06228123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036282756,0.000046302204,0.00032420672,0.00028606475,0.000035101686,0.00006263451,0.00017026847,0.0005806385,0.011224129,0.02484881,0.19661406,0.7657715],"study_design_scores_gemma":[0.000012134348,0.000037831433,0.0015518691,0.00020778853,0.000060096565,0.0004158593,0.00021942302,0.009432651,0.015736723,0.038285915,0.93400407,0.000035500387],"about_ca_topic_score_codex":0.00067079917,"about_ca_topic_score_gemma":0.0011751524,"teacher_disagreement_score":0.059863843,"about_ca_system_score_codex":0.00057241647,"about_ca_system_score_gemma":0.00064465805,"threshold_uncertainty_score":0.20026445},"labels":[],"label_agreement":null},{"id":"W4243978387","doi":"10.1007/978-1-4614-6170-8_110104","title":"Social Recommender System","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Recommender system; Computer science; World Wide Web; Information retrieval","score_opus":0.043022907946831904,"score_gpt":0.2598264340325602,"score_spread":0.21680352608572834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243978387","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025673565,0.016145157,0.5102267,0.005191564,0.0035036977,0.000753808,0.0022270721,0.007582118,0.42869633],"genre_scores_gemma":[0.15416855,0.013893179,0.28435066,0.0013930347,0.001707021,0.00034206675,0.0042150016,0.0005432121,0.5393872],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995407,0.000079319696,0.000023221657,0.00010176035,0.0002227652,0.00003215113],"domain_scores_gemma":[0.99955684,0.000088606925,0.000017641236,0.00009936193,0.00020050982,0.000036967485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072989304,0.0006020697,0.0006853368,0.0011374663,0.00094223116,0.0014925223,0.0009431312,0.00096687087,0.01990502],"category_scores_gemma":[0.001391865,0.00025937206,0.0005262518,0.0014251432,0.00021491294,0.002290963,0.0008929772,0.0008532473,0.018566785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005530319,0.00015992837,0.00081857364,0.00038564744,0.00008727056,0.000108474254,0.0001462971,0.0018698537,0.00545447,0.04249898,0.15122083,0.7971944],"study_design_scores_gemma":[0.00003577164,0.0001524048,0.0019355735,0.00018907702,0.00016338538,0.00076549547,0.00018592193,0.08160301,0.008158696,0.04131109,0.8654086,0.00009090479],"about_ca_topic_score_codex":0.002570856,"about_ca_topic_score_gemma":0.0057181427,"teacher_disagreement_score":0.01990502,"about_ca_system_score_codex":0.00052717054,"about_ca_system_score_gemma":0.00063569454,"threshold_uncertainty_score":0.06658894},"labels":[],"label_agreement":null},{"id":"W4244495650","doi":"10.32920/ryerson.14653809","title":"Automatic classification of the emotional content of web documents","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Bigram; Computer science; Natural language processing; Artificial intelligence; Word (group theory); Web page; Web content; Information retrieval; World Wide Web; Trigram; Linguistics","score_opus":0.07545543630957381,"score_gpt":0.2937467731545012,"score_spread":0.2182913368449274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244495650","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60266733,0.0013936928,0.3815606,0.00049655134,0.00020987033,0.00032837442,0.0012672258,0.0039142435,0.008162113],"genre_scores_gemma":[0.840363,0.0005386255,0.15262221,0.000111093585,0.00019021257,0.00016990617,0.002477805,0.0001652285,0.0033618507],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991898,0.0002452292,0.00006337307,0.00015926991,0.00025580643,0.000086512744],"domain_scores_gemma":[0.99825686,0.0007522496,0.00019634255,0.00011843471,0.0006250011,0.00005106674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006820864,0.0005015965,0.0005482838,0.0026887513,0.0003965474,0.0010832781,0.00037066138,0.00052996824,0.0012996218],"category_scores_gemma":[0.0030308275,0.00015716988,0.00044730733,0.0010289885,0.00024111483,0.0011269582,0.00044092897,0.00048016175,0.0012285408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047595892,0.00032307094,0.013479601,0.000363854,0.00009511602,0.0002982628,0.000348651,0.005330632,0.13417995,0.0018940421,0.0054869936,0.83772385],"study_design_scores_gemma":[0.000059868362,0.00028082126,0.07093678,0.000095239775,0.00014110794,0.00060651667,0.00071088655,0.8123421,0.09690959,0.008269322,0.009571636,0.00007610533],"about_ca_topic_score_codex":0.0005611441,"about_ca_topic_score_gemma":0.00061784656,"teacher_disagreement_score":0.0026887513,"about_ca_system_score_codex":0.00033266898,"about_ca_system_score_gemma":0.000248551,"threshold_uncertainty_score":0.004347682},"labels":[],"label_agreement":null},{"id":"W4246094123","doi":"10.4018/978-1-7998-0951-7.ch056","title":"From Citizens to Decision-Makers","year":2019,"lang":"en","type":"book-chapter","venue":"Natural Language Processing","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Social media; Order (exchange); Democracy; Computer science; Process (computing); Data science; Public relations; Political science; Internet privacy; World Wide Web; Business","score_opus":0.00909554556771086,"score_gpt":0.26729444006459874,"score_spread":0.2581988944968879,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246094123","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019951295,0.016319767,0.038359165,0.106375806,0.0034742753,0.00016807974,0.000506927,0.0003570499,0.81448776],"genre_scores_gemma":[0.3972021,0.017904185,0.025241662,0.01531201,0.0019957975,0.0002764645,0.00068775535,0.00027856044,0.5411014],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990532,0.0004389378,0.0000325494,0.0001275686,0.00027634104,0.00007152831],"domain_scores_gemma":[0.99902534,0.0006221798,0.000047482143,0.00006148151,0.00015610378,0.00008740418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011075753,0.000353614,0.0001954159,0.00056822755,0.0016455373,0.0068913936,0.0006238403,0.0015718484,0.011964858],"category_scores_gemma":[0.002835454,0.0002416771,0.00015528162,0.0008624327,0.0038283821,0.0054024043,0.0023830284,0.0026185827,0.004181636],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016399701,0.000030477058,0.00028455612,0.00018131458,0.000004815383,0.00015710291,0.011118639,0.0003223235,0.00065825455,0.7923127,0.1147277,0.080185756],"study_design_scores_gemma":[0.000004466144,0.000007319144,0.00021951465,0.00019557377,0.000002401699,0.000051988754,0.004903807,0.0004373697,0.0004293456,0.21806766,0.77567494,0.0000056580834],"about_ca_topic_score_codex":0.0012717237,"about_ca_topic_score_gemma":0.0017037651,"teacher_disagreement_score":0.011964858,"about_ca_system_score_codex":0.0021469113,"about_ca_system_score_gemma":0.0024000485,"threshold_uncertainty_score":0.040026426},"labels":[],"label_agreement":null},{"id":"W4247573097","doi":"10.1007/978-1-4614-6170-8_100882","title":"Sentiment Classification","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.04114905748384107,"score_gpt":0.2559855830704277,"score_spread":0.2148365255865866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247573097","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010769785,0.0045100916,0.35619193,0.0030492425,0.0034832081,0.0012109615,0.0068962397,0.011350281,0.6025382],"genre_scores_gemma":[0.06657714,0.00690551,0.24352126,0.0018442029,0.001970837,0.00085670175,0.022619333,0.002061451,0.65364355],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952555,0.000044745044,0.000031940286,0.00013305548,0.00021810913,0.000046620666],"domain_scores_gemma":[0.9994949,0.00007996102,0.000026633716,0.00008022959,0.00028487243,0.000033318633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068793964,0.0012695234,0.00069845165,0.0020966101,0.00095436245,0.0024523747,0.0011051408,0.0006214467,0.06935937],"category_scores_gemma":[0.0017937544,0.00035388247,0.00094925915,0.0019649558,0.00024992952,0.002328642,0.0012247826,0.0011948347,0.0769607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047287696,0.00007174707,0.0003299427,0.00019827334,0.000020235684,0.000041087947,0.00006382881,0.0004308718,0.0087026255,0.010373108,0.17997298,0.799748],"study_design_scores_gemma":[0.000021461185,0.000079129975,0.0020611011,0.00026850938,0.00007464675,0.0004962481,0.00016917144,0.020740055,0.02200953,0.028464252,0.92556983,0.000046138146],"about_ca_topic_score_codex":0.0009224254,"about_ca_topic_score_gemma":0.001630357,"teacher_disagreement_score":0.06935937,"about_ca_system_score_codex":0.0006479245,"about_ca_system_score_gemma":0.0007591159,"threshold_uncertainty_score":0.23203015},"labels":[],"label_agreement":null},{"id":"W4249142469","doi":"10.1109/wi-iat.2012.170","title":"Unsupervised Emotion Detection from Text Using Semantic and Syntactic Relations","year":2012,"lang":"en","type":"article","venue":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":137,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Natural language processing; Artificial intelligence; Sentence; Emotion detection; Word (group theory); Affect (linguistics); Context (archaeology); Emotion recognition; Linguistics","score_opus":0.06826441657676245,"score_gpt":0.30505265263647646,"score_spread":0.236788236059714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249142469","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20328891,0.0010828587,0.7817784,0.0005098345,0.00019267587,0.0004508678,0.0015322577,0.003148099,0.008016231],"genre_scores_gemma":[0.73292476,0.0005468011,0.25787568,0.00020378403,0.0004329816,0.0005012707,0.0035225404,0.00022715461,0.0037649968],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992059,0.00020215256,0.00006440832,0.00025223827,0.00021309456,0.00006220869],"domain_scores_gemma":[0.99830735,0.0008342493,0.00026343513,0.00009886772,0.00044622945,0.000049784212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058170647,0.0008716388,0.00070024986,0.0025755065,0.0004135433,0.0010518413,0.00058722193,0.0005987304,0.0014301069],"category_scores_gemma":[0.0028391867,0.00019815075,0.0008196739,0.0010994509,0.00041862484,0.0019152354,0.00066009536,0.0008275348,0.0013288467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059724355,0.0004563916,0.0153637715,0.00053799234,0.00017981551,0.0003813333,0.0008158355,0.008512782,0.22286685,0.006142085,0.008146752,0.73599917],"study_design_scores_gemma":[0.000058627294,0.00057396345,0.0553073,0.00010713445,0.00024140567,0.0007921323,0.00105353,0.824524,0.07617557,0.028839944,0.01221585,0.00011052725],"about_ca_topic_score_codex":0.00064893917,"about_ca_topic_score_gemma":0.0011704214,"teacher_disagreement_score":0.0025755065,"about_ca_system_score_codex":0.00032187445,"about_ca_system_score_gemma":0.0003512855,"threshold_uncertainty_score":0.004784167},"labels":[],"label_agreement":null},{"id":"W4250344253","doi":"10.1075/bct.87.07tab","title":"Loving and hating the movies in English, German and Spanish","year":2016,"lang":"en","type":"book-chapter","venue":"Benjamins current topics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Simon Fraser University","funders":"","keywords":"Argumentative; German; Style (visual arts); Linguistics; Sociocultural evolution; Psychology; Graduation (instrument); Polarity (international relations); White (mutation); Sociology; Literature; Art; Mathematics; Anthropology","score_opus":0.02793063734501796,"score_gpt":0.2687378681451503,"score_spread":0.24080723080013236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250344253","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9792099,0.0012916905,0.00050017453,0.00014741879,0.00002042097,0.000026110352,0.0018836918,0.000009687827,0.016910959],"genre_scores_gemma":[0.98871535,0.0011953937,0.00090786384,0.00009706982,0.000049462516,0.00007612749,0.0027746502,0.000024112614,0.006159927],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99905986,0.00040374763,0.00006829231,0.00008843998,0.00029669725,0.000083031526],"domain_scores_gemma":[0.9920392,0.0054590283,0.0009975911,0.00009566749,0.0012492462,0.00015917259],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010700913,0.00018516836,0.00019425037,0.0023158214,0.00028707844,0.0011343451,0.00015945727,0.000165655,0.0023364006],"category_scores_gemma":[0.0053078835,0.00006391076,0.000110267436,0.0030476176,0.0003769274,0.0006714343,0.0003722019,0.00023712355,0.00044733204],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068499154,0.00037960368,0.6444423,0.0025553922,0.00017399044,0.0013505049,0.12815477,0.00057140033,0.0128534315,0.0065226383,0.022550255,0.17976084],"study_design_scores_gemma":[0.0000045560773,0.000098914425,0.95188797,0.00012297285,0.000019349325,0.0004588856,0.027870046,0.0006046169,0.0009870969,0.0003063164,0.017623687,0.000015496224],"about_ca_topic_score_codex":0.0041483273,"about_ca_topic_score_gemma":0.008182754,"teacher_disagreement_score":0.0041483273,"about_ca_system_score_codex":0.00050106965,"about_ca_system_score_gemma":0.00020491934,"threshold_uncertainty_score":0.008248389},"labels":[],"label_agreement":null},{"id":"W4251868616","doi":"10.1007/978-1-4939-7131-2_100659","title":"Microblog Sentiment Analysis","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Microblogging; Sentiment analysis; Social media; Computer science; Natural language processing; World Wide Web","score_opus":0.020645338149472038,"score_gpt":0.2453499360273448,"score_spread":0.22470459787787278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251868616","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019754203,0.018756485,0.55880564,0.0052832905,0.0058400915,0.00076923054,0.010147174,0.015760457,0.36488342],"genre_scores_gemma":[0.15187469,0.021835564,0.34301752,0.0025915126,0.0060275164,0.00070386456,0.024858,0.0039443295,0.4451469],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999572,0.000046561647,0.000026333135,0.000101850535,0.00021603664,0.00003729531],"domain_scores_gemma":[0.9994628,0.00013489857,0.00003565135,0.000055283967,0.0002772118,0.000034223238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000609343,0.0011365283,0.000577862,0.00429088,0.0007279758,0.0024453541,0.0006589894,0.00060883356,0.020581387],"category_scores_gemma":[0.0018668959,0.000329169,0.0007726041,0.0032306968,0.00026045274,0.002241974,0.0010066954,0.0009882494,0.027673947],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030344727,0.000064319975,0.00081658835,0.00029949716,0.00004078799,0.000055796212,0.00012034937,0.00047239938,0.0074637556,0.008587192,0.19303061,0.78901845],"study_design_scores_gemma":[0.000012942491,0.000057966943,0.007092874,0.00033050202,0.00011700426,0.00072823185,0.0003468848,0.030951802,0.020506766,0.041684035,0.898113,0.0000580372],"about_ca_topic_score_codex":0.0009006693,"about_ca_topic_score_gemma":0.0018091076,"teacher_disagreement_score":0.020581387,"about_ca_system_score_codex":0.00053821615,"about_ca_system_score_gemma":0.000515305,"threshold_uncertainty_score":0.06885165},"labels":[],"label_agreement":null},{"id":"W4253518962","doi":"10.20431/2347-3134.0610004","title":"Trump Tweets","year":2018,"lang":"en","type":"article","venue":"International Journal on Studies in English Language and Literature","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Computer science; Natural language processing; Internet privacy; Political science; History; Linguistics; Philosophy","score_opus":0.018022281430827486,"score_gpt":0.33460769943430274,"score_spread":0.3165854180034753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253518962","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06997193,0.0024987971,0.004538402,0.01620451,0.010148294,0.0011684155,0.44959122,0.009188748,0.43668956],"genre_scores_gemma":[0.17501238,0.0026327637,0.0112549355,0.0079227295,0.004907054,0.0016579252,0.348801,0.004115265,0.4436959],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99878675,0.00013994449,0.000090373906,0.00017725462,0.0006039237,0.00020178853],"domain_scores_gemma":[0.9966967,0.0008631018,0.00048611787,0.00037104977,0.0011855784,0.00039748388],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008255446,0.00069570274,0.0004064272,0.0039965617,0.0022184364,0.0025257007,0.00064159767,0.0010350316,0.0978938],"category_scores_gemma":[0.007972894,0.00040629317,0.0003665983,0.003884065,0.0003027649,0.0030487175,0.0024538822,0.0016993515,0.0657073],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020179538,0.000041113624,0.0050806184,0.00037297027,0.00001701471,0.00023901167,0.0012231079,0.00008679989,0.0018972983,0.0029303362,0.9347256,0.053184222],"study_design_scores_gemma":[0.000019246718,0.00004111425,0.009276538,0.00010089562,0.0000126006935,0.00019634368,0.000808327,0.00046726997,0.0013564371,0.0006631119,0.9870269,0.000031185795],"about_ca_topic_score_codex":0.0052275867,"about_ca_topic_score_gemma":0.012796522,"teacher_disagreement_score":0.0978938,"about_ca_system_score_codex":0.0010001285,"about_ca_system_score_gemma":0.0007336232,"threshold_uncertainty_score":0.3274874},"labels":[],"label_agreement":null},{"id":"W4253949701","doi":"10.1007/978-1-4614-6170-8_100591","title":"Twitter Opinion Mining","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Sentiment analysis; Computer science; Artificial intelligence","score_opus":0.04124181079557006,"score_gpt":0.26483424030754205,"score_spread":0.223592429511972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253949701","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012955514,0.01410456,0.34135026,0.011416407,0.005792017,0.0010275691,0.015877636,0.011067959,0.586408],"genre_scores_gemma":[0.08863853,0.020559449,0.22116888,0.0032772108,0.0055467514,0.00096459495,0.03504333,0.0024491744,0.6223521],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995017,0.000062632076,0.000027343254,0.00009410837,0.00026702118,0.000047202484],"domain_scores_gemma":[0.9994294,0.00015148536,0.00003635156,0.00007775691,0.000270109,0.000034842196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069697684,0.0011264102,0.000589527,0.0032465805,0.0010581072,0.002485598,0.00090935966,0.0007428352,0.029185547],"category_scores_gemma":[0.0024610348,0.00042517876,0.0007371166,0.0036098883,0.0002552801,0.0033646019,0.0013723484,0.00096380437,0.03888788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002962971,0.000043215692,0.00071793335,0.00021817502,0.00002644891,0.000054018226,0.00012595682,0.00056321576,0.0025409812,0.013762473,0.29100958,0.6909084],"study_design_scores_gemma":[0.000011883982,0.000028923565,0.0021520979,0.0002065073,0.000049284703,0.00032206313,0.00027493836,0.019562038,0.0071045854,0.040837403,0.92941076,0.000039472416],"about_ca_topic_score_codex":0.0015584372,"about_ca_topic_score_gemma":0.003738012,"teacher_disagreement_score":0.029185547,"about_ca_system_score_codex":0.0006900573,"about_ca_system_score_gemma":0.0006691416,"threshold_uncertainty_score":0.09763533},"labels":[],"label_agreement":null},{"id":"W4254018890","doi":"10.1007/978-1-4939-7131-2_100828","title":"Opinion Mining of Microblogging Data","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Microblogging; Social media; Data science; Computer science; Internet privacy; World Wide Web","score_opus":0.09089128680008007,"score_gpt":0.2981875888431776,"score_spread":0.2072963020430975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254018890","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03760441,0.020666964,0.87625086,0.0046715904,0.0024176217,0.00041606586,0.009072586,0.0048413803,0.044058397],"genre_scores_gemma":[0.23470642,0.025624312,0.64823616,0.0013747384,0.0040654247,0.0005855445,0.02817668,0.0010739422,0.056156777],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993129,0.0001406503,0.00006000999,0.00013931439,0.00028795566,0.000059162612],"domain_scores_gemma":[0.9984055,0.00092070265,0.00009131926,0.00014659493,0.00038518666,0.000050710965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012222292,0.0008153582,0.00065307075,0.0025444515,0.00039467416,0.0020825318,0.0008662492,0.0005412813,0.0049137413],"category_scores_gemma":[0.005361854,0.00032272312,0.0011074466,0.0039364314,0.00022592997,0.0025638859,0.00072841335,0.0012354737,0.005984371],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005889948,0.000105547835,0.0020022586,0.0005662804,0.00008254655,0.00010830968,0.0001998611,0.0015737303,0.0068572517,0.011284697,0.06679003,0.9103707],"study_design_scores_gemma":[0.00003460912,0.00024169286,0.018301481,0.000721849,0.0002852324,0.001987444,0.0007055239,0.3393982,0.044702157,0.14480548,0.44866198,0.0001543175],"about_ca_topic_score_codex":0.000935959,"about_ca_topic_score_gemma":0.0014754082,"teacher_disagreement_score":0.0049137413,"about_ca_system_score_codex":0.00047907184,"about_ca_system_score_gemma":0.00042638223,"threshold_uncertainty_score":0.016438127},"labels":[],"label_agreement":null},{"id":"W4255984321","doi":"10.4018/978-1-4666-5194-4.ch006","title":"Analytics and Performance Measurement Frameworks for Social Customer Relationship Management","year":2014,"lang":"en","type":"book-chapter","venue":"Advances in social networking and online communities book series","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Analytics; Big data; Computer science; Data science; Knowledge management; Customer relationship management; Process management; Business analytics; Business; Business model; Business analysis; Data mining; Marketing; Database","score_opus":0.06516600749590135,"score_gpt":0.2961318256576833,"score_spread":0.23096581816178197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255984321","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045879562,0.06737144,0.695022,0.024438081,0.0018078716,0.00067518733,0.001384993,0.002024445,0.20268808],"genre_scores_gemma":[0.17191224,0.09215278,0.6634029,0.004450946,0.004805356,0.0021215258,0.0031229232,0.0008413518,0.05718995],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99280965,0.002268325,0.00041959214,0.0006893961,0.003497495,0.00031549582],"domain_scores_gemma":[0.9934069,0.003599779,0.0006191208,0.0004981987,0.0016616841,0.00021432777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00729974,0.002416689,0.0010080078,0.0077533284,0.0015684039,0.00909068,0.0023502638,0.0024512887,0.008082961],"category_scores_gemma":[0.013548199,0.00068534276,0.0011363438,0.008867168,0.0030615053,0.012920856,0.003613317,0.004514644,0.00389385],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010178714,0.00007300896,0.0007663743,0.0004852111,0.00003464615,0.00005458974,0.0007065189,0.004822598,0.00030682274,0.81435126,0.03461567,0.14377308],"study_design_scores_gemma":[0.0000064064,0.00005777258,0.0017291617,0.0014036797,0.00003023505,0.00023938752,0.0011016285,0.032855857,0.00065828033,0.6276493,0.3341794,0.00008892714],"about_ca_topic_score_codex":0.0043179705,"about_ca_topic_score_gemma":0.002963136,"teacher_disagreement_score":0.00909068,"about_ca_system_score_codex":0.005575897,"about_ca_system_score_gemma":0.0030463848,"threshold_uncertainty_score":0.040456116},"labels":[],"label_agreement":null},{"id":"W4283574889","doi":"10.1080/08839514.2022.2083794","title":"GAN-BElectra: Enhanced Multi-class Sentiment Analysis with Limited Labeled Data","year":2022,"lang":"en","type":"article","venue":"Applied Artificial Intelligence","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Sentiment analysis; Artificial intelligence; Class (philosophy); Machine learning; Baseline (sea); Training set; Data mining; Labeled data","score_opus":0.07802130697619565,"score_gpt":0.3026750444686736,"score_spread":0.22465373749247797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283574889","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034522038,0.0005136412,0.9524963,0.00041618082,0.00024654676,0.0001575585,0.0004508923,0.006223135,0.0049736723],"genre_scores_gemma":[0.5027655,0.0004378739,0.4826162,0.0010257798,0.00027651727,0.00031332558,0.0026836179,0.00051909825,0.009362073],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942905,0.00022769673,0.000020566164,0.00012498771,0.00013412512,0.00006349643],"domain_scores_gemma":[0.99908733,0.00033162662,0.000073460185,0.0001641533,0.00029887285,0.000044624452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014846595,0.0009901611,0.0006920488,0.0005791045,0.00027188775,0.00057585357,0.0011588676,0.0005623759,0.002585193],"category_scores_gemma":[0.0023911234,0.0002534283,0.00079027703,0.0004428483,0.0003204461,0.001067166,0.00064597855,0.0013627614,0.001655899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063062203,0.00038614662,0.0042260247,0.00024391327,0.0003172662,0.00020053735,0.00018166729,0.10955609,0.049918316,0.006329043,0.03551117,0.7924992],"study_design_scores_gemma":[0.000024902405,0.000087167464,0.0007297701,0.000010505437,0.000026092868,0.00006564252,0.000023345305,0.9859357,0.006981176,0.0032529759,0.002849723,0.000013053646],"about_ca_topic_score_codex":0.001699397,"about_ca_topic_score_gemma":0.004184741,"teacher_disagreement_score":0.002585193,"about_ca_system_score_codex":0.0004146947,"about_ca_system_score_gemma":0.0005417418,"threshold_uncertainty_score":0.008648336},"labels":[],"label_agreement":null},{"id":"W4284879922","doi":"10.1093/ijnp/pyac032.081","title":"EMOTIONAL BEHAVIOR ANALYSIS OF NOVEL CHARACTERS BASED ON COMPLEX NETWORK AND WLDA ALGORITHM — TAKING HARUKI MURAKAMI'S NORWEGIAN FOREST AS AN EXAMPLE","year":2022,"lang":"en","type":"article","venue":"The International Journal of Neuropsychopharmacology","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Norwegian; Mood; Computer science; The Internet; Key (lock); Algorithm; Data mining; Artificial intelligence; Natural language processing; Psychology; Social psychology; World Wide Web; Linguistics","score_opus":0.06403842990052513,"score_gpt":0.3408580066286067,"score_spread":0.27681957672808155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4284879922","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7492292,0.00039584425,0.24518512,0.00037511592,0.00007473123,0.00014119607,0.0003498521,0.0004190931,0.0038298054],"genre_scores_gemma":[0.93561643,0.00018677121,0.060731694,0.000028616656,0.000025070774,0.000107551234,0.00041900232,0.000025617432,0.0028591699],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997366,0.00005505148,0.000017979002,0.00009533703,0.000059741127,0.000035315297],"domain_scores_gemma":[0.999526,0.00022516737,0.000054625107,0.00002394382,0.0001353863,0.000034982146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039188427,0.00051198574,0.0003591332,0.0012872068,0.00047902387,0.00063006446,0.0003355908,0.000364206,0.0014685855],"category_scores_gemma":[0.0016576105,0.00014928848,0.0005330993,0.00085263327,0.0002651006,0.0007523958,0.0003149759,0.0003090273,0.0001881776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010398678,0.00048383966,0.09541876,0.0003673315,0.00030729393,0.0017541444,0.0022789293,0.25636497,0.03693519,0.0061478596,0.0055733123,0.5933284],"study_design_scores_gemma":[0.000010694549,0.00006477179,0.02325725,0.000009453968,0.000033820594,0.00010384753,0.00042455376,0.97116303,0.0026620408,0.0012549384,0.0009942071,0.0000214481],"about_ca_topic_score_codex":0.008607724,"about_ca_topic_score_gemma":0.007996709,"teacher_disagreement_score":0.008607724,"about_ca_system_score_codex":0.00052895513,"about_ca_system_score_gemma":0.0002239628,"threshold_uncertainty_score":0.017115235},"labels":[],"label_agreement":null},{"id":"W4284882013","doi":"10.1109/cniot55862.2022.00036","title":"The Constrained Interaction Network for Aspect-level Sentiment Classification Task","year":2022,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"123 Certification (Canada)","funders":"","keywords":"Computer science; Sentiment analysis; Layer (electronics); Sentence; Constraint (computer-aided design); SemEval; Context (archaeology); Task (project management); Artificial intelligence; Polarity (international relations); Mechanism (biology); Natural language processing; Exploit; Simple (philosophy)","score_opus":0.06285196772945054,"score_gpt":0.2991875188400403,"score_spread":0.23633555111058976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4284882013","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17669563,0.0013679118,0.80312663,0.0010426571,0.0002843282,0.0002947902,0.0021987103,0.0033842372,0.011605188],"genre_scores_gemma":[0.8582592,0.0006065777,0.12952219,0.00035431812,0.00022328565,0.000399945,0.004088869,0.00016971664,0.0063758683],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971443,0.00006753009,0.000017456565,0.00009837215,0.000051561485,0.000050622675],"domain_scores_gemma":[0.99964154,0.0001729886,0.000045512727,0.000033978045,0.00008250711,0.000023462377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048395197,0.0010411935,0.00038974837,0.00072770985,0.0004505672,0.000555723,0.0006928731,0.00083698105,0.0038142002],"category_scores_gemma":[0.0019858037,0.00026151625,0.0006454902,0.00083879346,0.0003052558,0.001624213,0.0007555226,0.0010830774,0.0008922347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012727379,0.0003164698,0.009212203,0.0004438777,0.00024344197,0.0005543761,0.00039530706,0.119058214,0.07226748,0.012122605,0.025135297,0.75897807],"study_design_scores_gemma":[0.000017717759,0.00006337904,0.0024879333,0.00001436712,0.000042716238,0.00007371074,0.000039946975,0.98013353,0.0051893257,0.009052294,0.0028696188,0.000015457801],"about_ca_topic_score_codex":0.004066374,"about_ca_topic_score_gemma":0.006069149,"teacher_disagreement_score":0.004066374,"about_ca_system_score_codex":0.00057932426,"about_ca_system_score_gemma":0.0005862561,"threshold_uncertainty_score":0.012759745},"labels":[],"label_agreement":null},{"id":"W4285115467","doi":"10.18653/v1/2022.wassa-1.13","title":"Assessment of Massively Multilingual Sentiment Classifiers","year":2022,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Department of Artificial Intelligence, Korea University; European Regional Development Fund; Politechnika Wrocławska","keywords":"Sentiment analysis; Subjectivity; Computer science; Social media; Artificial intelligence; Data science; Natural language processing; World Wide Web; Epistemology","score_opus":0.02674224008336883,"score_gpt":0.3108617894804158,"score_spread":0.284119549397047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285115467","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75143397,0.00926666,0.19332294,0.0026627346,0.002710347,0.0005333464,0.005342184,0.0066089868,0.028118795],"genre_scores_gemma":[0.9435271,0.0007458569,0.04226996,0.00025515517,0.0005750076,0.00017521328,0.0088631455,0.00031344747,0.0032751246],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969602,0.001306539,0.00018711694,0.0005662223,0.0007182283,0.0002617635],"domain_scores_gemma":[0.9925902,0.0035163986,0.0002503153,0.0005194772,0.0027484903,0.00037524672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006451106,0.0020056656,0.0015011672,0.0019953952,0.0010590799,0.0021762548,0.0011504303,0.0016476804,0.003368293],"category_scores_gemma":[0.015801089,0.00038321305,0.00078470295,0.0010819425,0.00033121262,0.0025783547,0.00225764,0.0011228406,0.0030921323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0044946447,0.0013845226,0.029409913,0.00091270276,0.0017773138,0.000475784,0.00040341617,0.07602202,0.01980388,0.0025956286,0.054298684,0.8084215],"study_design_scores_gemma":[0.00018470113,0.0008311999,0.008865706,0.00012234211,0.0006330278,0.00021469068,0.00040832424,0.96360177,0.013016874,0.004538763,0.007532982,0.0000496206],"about_ca_topic_score_codex":0.0024498387,"about_ca_topic_score_gemma":0.0027452176,"teacher_disagreement_score":0.006451106,"about_ca_system_score_codex":0.0008418823,"about_ca_system_score_gemma":0.0010163046,"threshold_uncertainty_score":0.034117103},"labels":[],"label_agreement":null},{"id":"W4285115534","doi":"10.1007/978-3-031-10464-0_47","title":"Sentiment Analysis on Citizenship Amendment Act of India 2019 Using Twitter Data","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Citizenship; Amendment; Sentiment analysis; Political science; Internet privacy; Law; Computer science; Artificial intelligence","score_opus":0.05868410942169873,"score_gpt":0.28677963476682233,"score_spread":0.22809552534512362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285115534","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9622203,0.0002269415,0.0006245795,0.0011127157,0.00021396253,0.00004243496,0.016094834,0.0000812039,0.019383041],"genre_scores_gemma":[0.97892225,0.00020573156,0.00066092523,0.00010979017,0.00011789543,0.000036075846,0.013580221,0.0000173376,0.0063497853],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999681,0.00006500879,0.000025865418,0.000030118572,0.00012033316,0.00007771524],"domain_scores_gemma":[0.9985013,0.00062201836,0.000278802,0.000047735484,0.00045400264,0.00009616439],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000412794,0.0001541895,0.00013774169,0.0014343774,0.0004812346,0.00079047855,0.00018665775,0.00029996646,0.0017885274],"category_scores_gemma":[0.0017257573,0.000063089166,0.00025022635,0.0016856061,0.00017975648,0.00047418548,0.00031929705,0.0005145565,0.0009912655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007877951,0.0003520309,0.7047136,0.0004504365,0.00022067255,0.0016732077,0.003443085,0.007236799,0.008608636,0.006643324,0.14117481,0.12469563],"study_design_scores_gemma":[0.000010499845,0.00010483122,0.9011045,0.000085171036,0.00010162422,0.00019547917,0.01147282,0.034241702,0.004561232,0.0007546646,0.047316138,0.000051348958],"about_ca_topic_score_codex":0.02794247,"about_ca_topic_score_gemma":0.039672334,"teacher_disagreement_score":0.02794247,"about_ca_system_score_codex":0.00060404465,"about_ca_system_score_gemma":0.000504241,"threshold_uncertainty_score":0.055559695},"labels":[],"label_agreement":null},{"id":"W4285464558","doi":"10.32920/ryerson.14664513.v1","title":"Applying supervised learning algorithms on information derived from Social Network to enhance recommender systems","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Support vector machine; Computer science; Recommender system; Pairwise comparison; Machine learning; Rank (graph theory); Feature (linguistics); Artificial intelligence; Social network (sociolinguistics); Learning to rank; Social network analysis; Data mining; Social media; World Wide Web; Ranking (information retrieval); Mathematics","score_opus":0.03167334953480581,"score_gpt":0.28718926974411363,"score_spread":0.2555159202093078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285464558","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040532943,0.0006729371,0.9542737,0.00034900964,0.00014227316,0.0002018895,0.0002120458,0.0009222804,0.0026928268],"genre_scores_gemma":[0.526006,0.0005791619,0.46887398,0.00022076552,0.00029151054,0.0003195355,0.0008320178,0.000097358155,0.0027796058],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984647,0.0006724988,0.00011427665,0.0002783664,0.0003975951,0.00007259641],"domain_scores_gemma":[0.9939266,0.0037941476,0.00038790444,0.0004990805,0.0013101081,0.00008222779],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029203934,0.00086604006,0.0011979258,0.0021280877,0.0004680079,0.0011518751,0.0009471108,0.0010110876,0.001449607],"category_scores_gemma":[0.010870889,0.0003444653,0.0006852637,0.0015135687,0.00031877783,0.001583676,0.00074402394,0.0009642021,0.00097294705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002868374,0.00065566134,0.007110047,0.0003620809,0.00040923824,0.00011399043,0.00024085307,0.23007786,0.0060939584,0.0066302638,0.006025668,0.7419934],"study_design_scores_gemma":[0.000016590475,0.00007262832,0.0008658443,0.000017451555,0.000026920627,0.000025523668,0.000033306067,0.9922545,0.0012894197,0.004319419,0.0010688672,0.000009379144],"about_ca_topic_score_codex":0.0027106253,"about_ca_topic_score_gemma":0.0042193606,"teacher_disagreement_score":0.0029203934,"about_ca_system_score_codex":0.00053879747,"about_ca_system_score_gemma":0.0005605721,"threshold_uncertainty_score":0.015444696},"labels":[],"label_agreement":null},{"id":"W4286436770","doi":"10.18280/isi.270318","title":"Arabic Sentiment Analysis of Eateries’ Reviews Using Deep Learning","year":2022,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Random forest; Sentiment analysis; Artificial intelligence; Support vector machine; Computer science; Naive Bayes classifier; Arabic; Machine learning; Deep learning; Term (time); k-nearest neighbors algorithm; Quality (philosophy); Natural language processing; Linguistics","score_opus":0.026115937257924847,"score_gpt":0.2615287979493374,"score_spread":0.23541286069141257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286436770","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9318868,0.0011546469,0.053085465,0.0007297805,0.0002979808,0.00012678915,0.0026647716,0.0011973104,0.008856642],"genre_scores_gemma":[0.9693685,0.00033651464,0.022485273,0.00007768307,0.000078465455,0.00004345325,0.002908123,0.000033416105,0.004668514],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997799,0.000056479203,0.000019058612,0.00003643137,0.000074582844,0.000033501332],"domain_scores_gemma":[0.99954563,0.00010268738,0.000057356876,0.000019858011,0.00025277783,0.000021771419],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032477442,0.00056940754,0.000240323,0.0010060669,0.00017232096,0.0003836623,0.00017862947,0.00024663017,0.0013979052],"category_scores_gemma":[0.0009909327,0.00009221092,0.00037234472,0.0004910736,0.00007382378,0.0003091552,0.00020020388,0.00035702426,0.00085209444],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012062388,0.00074223336,0.05611194,0.00052471,0.00040743826,0.0010484431,0.0007453573,0.045283463,0.08628074,0.0010766633,0.031505678,0.7750672],"study_design_scores_gemma":[0.000020952124,0.00020538068,0.038878463,0.00002801085,0.00006399359,0.00013477083,0.00041658402,0.9325001,0.021530235,0.0005123323,0.0056840354,0.00002517446],"about_ca_topic_score_codex":0.004186818,"about_ca_topic_score_gemma":0.006064422,"teacher_disagreement_score":0.004186818,"about_ca_system_score_codex":0.00033326956,"about_ca_system_score_gemma":0.00017969136,"threshold_uncertainty_score":0.0083248615},"labels":[],"label_agreement":null},{"id":"W4286517722","doi":"10.18280/ria.360305","title":"An Integrated Single Framework for Text, Image and Voice for Sentiment Mining of Social Media Posts","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Electronics and Information technology","keywords":"Computer science; Sentiment analysis; Social media; Image (mathematics); Semantics (computer science); Block (permutation group theory); Negation; Domain (mathematical analysis); Commit; Artificial intelligence; Categorization; Natural language processing; World Wide Web; Database; Programming language","score_opus":0.04805299952005135,"score_gpt":0.3090902958958592,"score_spread":0.26103729637580786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286517722","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025921842,0.0004028483,0.96535134,0.00047334022,0.000116462455,0.00030659346,0.0008535756,0.0040585687,0.0025153456],"genre_scores_gemma":[0.485931,0.00050391094,0.5008938,0.00036710192,0.00021047685,0.0005987961,0.0037479014,0.00028294922,0.0074640666],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996636,0.00006189636,0.000027453043,0.00011631985,0.0000781667,0.000052640295],"domain_scores_gemma":[0.9998375,0.000044246328,0.000019951978,0.000019511384,0.00006393755,0.00001491922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058979174,0.00077197194,0.0004173482,0.0012725221,0.00033987287,0.0010257878,0.00082335086,0.0008222974,0.0023115175],"category_scores_gemma":[0.0008560654,0.00022629472,0.0013398897,0.0006209293,0.00039220197,0.0010989173,0.00090757565,0.0007358557,0.0014944837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032934028,0.0004810242,0.0075317575,0.00042501645,0.0003445192,0.00060797797,0.0006084829,0.059234664,0.08047167,0.019443758,0.013038998,0.8174828],"study_design_scores_gemma":[0.000016388289,0.0001188386,0.0032418952,0.000027663535,0.000075205025,0.00013257188,0.00017951214,0.9703508,0.008693726,0.010016442,0.0071175033,0.000029435383],"about_ca_topic_score_codex":0.0055278386,"about_ca_topic_score_gemma":0.011661735,"teacher_disagreement_score":0.0055278386,"about_ca_system_score_codex":0.0005821924,"about_ca_system_score_gemma":0.00088695186,"threshold_uncertainty_score":0.010991275},"labels":[],"label_agreement":null},{"id":"W4286593227","doi":"10.4018/978-1-6684-6303-1.ch073","title":"Assessing Public Opinions of Products Through Sentiment Analysis","year":2022,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Sentiment analysis; Product (mathematics); Computer science; Process (computing); Social media; Word (group theory); Data science; Artificial intelligence; World Wide Web; Linguistics; Mathematics","score_opus":0.0605661954565672,"score_gpt":0.30783385955815307,"score_spread":0.24726766410158588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286593227","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6080594,0.0022745614,0.2809062,0.0020787278,0.00030964595,0.0007406651,0.0026931746,0.0014555826,0.10148208],"genre_scores_gemma":[0.8925114,0.0013616428,0.09487634,0.00020413581,0.00021734746,0.00023051944,0.0014277238,0.00011482333,0.009056098],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99840647,0.0004519138,0.00010917328,0.00013810354,0.0008032445,0.000091111535],"domain_scores_gemma":[0.9977314,0.00094409887,0.00037243703,0.00009253837,0.00081057864,0.00004901303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023208682,0.00053043244,0.00051014364,0.0033114136,0.00054388604,0.0025405297,0.0003610813,0.0005096462,0.0022295965],"category_scores_gemma":[0.0043197693,0.00017124614,0.0004374272,0.002390829,0.00035535797,0.0017723452,0.0006765498,0.0005301802,0.0016596587],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034946308,0.00026724773,0.03697442,0.0007689218,0.00014376239,0.00036712008,0.0028206226,0.0048763724,0.046317738,0.008811451,0.017041808,0.8812611],"study_design_scores_gemma":[0.00006415059,0.0009290421,0.2510134,0.0008658861,0.00055392244,0.0015558922,0.019542161,0.4796807,0.06464307,0.048601013,0.13224143,0.00030932593],"about_ca_topic_score_codex":0.0010108539,"about_ca_topic_score_gemma":0.0015601113,"teacher_disagreement_score":0.0033114136,"about_ca_system_score_codex":0.0007364781,"about_ca_system_score_gemma":0.0004505146,"threshold_uncertainty_score":0.0122740865},"labels":[],"label_agreement":null},{"id":"W4288080293","doi":"10.1145/3385186","title":"OutdoorSent","year":2020,"lang":"en","type":"article","venue":"ACM Transactions on Information Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Computer science; Sentiment analysis; Generalization; Class (philosophy); Artificial intelligence; Context (archaeology); Information retrieval; Machine learning; Data science; Geography","score_opus":0.03513477824408741,"score_gpt":0.24907013153895155,"score_spread":0.21393535329486413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288080293","genre_codex":"dataset","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07698869,0.002548857,0.031193126,0.0013663386,0.0017975577,0.001065855,0.64780074,0.10619442,0.13104445],"genre_scores_gemma":[0.08711065,0.00065542106,0.031552654,0.00060103874,0.0002675717,0.00047412072,0.833451,0.0036932747,0.042194206],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948585,0.00006084509,0.00003040826,0.00019252658,0.00014531007,0.00008506276],"domain_scores_gemma":[0.99944586,0.000078566874,0.000057649184,0.00020257864,0.0001474449,0.00006787295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059432955,0.0015129491,0.000601338,0.0014375384,0.00056038005,0.0014887609,0.0011671243,0.0008974693,0.043757293],"category_scores_gemma":[0.0018361099,0.0003556335,0.0008170568,0.0013468337,0.00028016075,0.0015907246,0.001668335,0.00073980726,0.03565536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00060036615,0.00020135577,0.007123306,0.0010317968,0.00010788965,0.00025673362,0.0002623461,0.001974337,0.007858192,0.0025233391,0.83308214,0.14497814],"study_design_scores_gemma":[0.00023554258,0.00037632114,0.023751635,0.00022032655,0.00009147051,0.0008526375,0.0004851372,0.039337233,0.015678208,0.0053111203,0.9135667,0.00009363806],"about_ca_topic_score_codex":0.009200886,"about_ca_topic_score_gemma":0.027934562,"teacher_disagreement_score":0.043757293,"about_ca_system_score_codex":0.0005851979,"about_ca_system_score_gemma":0.000553981,"threshold_uncertainty_score":0.14638269},"labels":[],"label_agreement":null},{"id":"W4289101974","doi":"10.3390/a15080267","title":"Short Text Classification with Tolerance-Based Soft Computing Method","year":2022,"lang":"en","type":"article","venue":"Algorithms","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"Natural Sciences and Engineering Research Council of Canada; University of Winnipeg","keywords":"Computer science; Artificial intelligence; Categorization; Pattern recognition (psychology); Support vector machine; Precision and recall; F1 score; Data mining; Machine learning","score_opus":0.03455356625980585,"score_gpt":0.29744387198076366,"score_spread":0.26289030572095784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289101974","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030782608,0.00026882614,0.9651429,0.00016342071,0.00012249759,0.00014180999,0.00016715228,0.0014588273,0.0017519663],"genre_scores_gemma":[0.516669,0.00035847924,0.4761588,0.00025706436,0.00017979988,0.00045335,0.001147696,0.00019340866,0.004582508],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983689,0.00020108662,0.0002329993,0.00039693606,0.0006659477,0.00013409174],"domain_scores_gemma":[0.99804807,0.00062153034,0.00029637953,0.00021463877,0.000735372,0.00008398941],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012064857,0.00080511067,0.0010314083,0.0031224433,0.0005273047,0.0014619959,0.0010410274,0.00083984033,0.0031258825],"category_scores_gemma":[0.004804691,0.00024330987,0.0012211028,0.0024492363,0.0007160392,0.0018538251,0.0010797422,0.0012557608,0.0013129702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047585974,0.00018601955,0.0022733144,0.00025321345,0.00010471716,0.00014815561,0.00019349097,0.11869264,0.025704658,0.01043402,0.0035479998,0.83798593],"study_design_scores_gemma":[0.000015649688,0.00014859116,0.00092713325,0.000018423878,0.000027472395,0.000100399135,0.000064785105,0.97551876,0.014281262,0.0072163013,0.0016576424,0.00002349973],"about_ca_topic_score_codex":0.0012810535,"about_ca_topic_score_gemma":0.00088574877,"teacher_disagreement_score":0.0031258825,"about_ca_system_score_codex":0.0007489238,"about_ca_system_score_gemma":0.0009514739,"threshold_uncertainty_score":0.0104570985},"labels":[],"label_agreement":null},{"id":"W4290928156","doi":"10.1016/j.procs.2022.07.023","title":"Live Sentiment Analysis Using Multiple Machine Learning and Text Processing Algorithms","year":2022,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trent University; Thompson Rivers University","funders":"","keywords":"Computer science; Sentiment analysis; Naive Bayes classifier; Lexicon; Machine learning; Artificial intelligence; Support vector machine; Algorithm; Data stream mining; Data mining","score_opus":0.019950047655876543,"score_gpt":0.26987339386171944,"score_spread":0.2499233462058429,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4290928156","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20238623,0.00072273967,0.77709556,0.0006082321,0.00061216217,0.0005937689,0.002027743,0.009299195,0.00665447],"genre_scores_gemma":[0.6045346,0.00034631157,0.38562727,0.00020194935,0.00034853411,0.0005064733,0.003220934,0.00028513643,0.0049289246],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982651,0.0002636768,0.00015917579,0.00039061255,0.000768426,0.00015308223],"domain_scores_gemma":[0.99823064,0.00040068582,0.00018439125,0.00017510635,0.0009415481,0.00006759075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014092937,0.0013743935,0.0012518377,0.0027722416,0.00074274564,0.0013894433,0.0009881755,0.0008470656,0.0027872848],"category_scores_gemma":[0.0033835196,0.00035499045,0.00096620497,0.0020576105,0.00025994593,0.0024558029,0.00096735265,0.00090758037,0.0022945942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066393794,0.0005741963,0.020062545,0.00028726165,0.00038564808,0.00037418312,0.00024888036,0.030633477,0.054988276,0.0016449266,0.010257463,0.8798792],"study_design_scores_gemma":[0.000029465878,0.00025383657,0.008223341,0.000023646264,0.00006315136,0.00017111565,0.00020796045,0.9535137,0.029772146,0.0028410677,0.0048632273,0.00003737618],"about_ca_topic_score_codex":0.0017020662,"about_ca_topic_score_gemma":0.0022039518,"teacher_disagreement_score":0.0027872848,"about_ca_system_score_codex":0.00057608954,"about_ca_system_score_gemma":0.00048549703,"threshold_uncertainty_score":0.009324431},"labels":[],"label_agreement":null},{"id":"W4291517590","doi":"10.3156/jsoft.34.3_592","title":"An Analysis of People’s Emotional Change Toward Vaccines and Its Factors in the Corona Disaster","year":2022,"lang":"en","type":"article","venue":"Journal of Japan Society for Fuzzy Theory and Intelligent Informatics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Japan Society for the Promotion of Science","keywords":"Corona (planetary geology); Psychology; Physics; Astrobiology","score_opus":0.04335897229548283,"score_gpt":0.29502882678795583,"score_spread":0.25166985449247303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4291517590","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99832684,0.00005633267,0.00028480336,0.00017963094,0.000017068738,0.000011962343,0.00018541314,0.0000040148084,0.00093402894],"genre_scores_gemma":[0.999218,0.000046668054,0.00017110902,0.00003542127,0.00001311131,0.000016939439,0.00015803822,0.0000016723808,0.00033914743],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99976844,0.00007718319,0.000017525565,0.000036216592,0.000042155185,0.00005847948],"domain_scores_gemma":[0.99902403,0.00035544703,0.00027046053,0.000032322176,0.00020492657,0.00011278418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003834334,0.0001451331,0.00015603933,0.0005029399,0.00045408972,0.0005042434,0.000109952554,0.00026639912,0.00096329994],"category_scores_gemma":[0.0018832074,0.00007434303,0.0003180329,0.00051800837,0.00022600192,0.00045686064,0.0004076067,0.00039971824,0.00016821527],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009819081,0.00021060114,0.9193678,0.00022501459,0.00017413884,0.00097622274,0.020840708,0.0011174686,0.008200734,0.00065917097,0.0046308753,0.042615376],"study_design_scores_gemma":[0.000004487341,0.00008275974,0.98125494,0.000015635838,0.000032775202,0.000096450996,0.013990783,0.0021205202,0.0005138525,0.0001468632,0.0017274912,0.000013558688],"about_ca_topic_score_codex":0.0031071363,"about_ca_topic_score_gemma":0.0029283827,"teacher_disagreement_score":0.0031071363,"about_ca_system_score_codex":0.00046696357,"about_ca_system_score_gemma":0.00013791687,"threshold_uncertainty_score":0.006178081},"labels":[],"label_agreement":null},{"id":"W4292846260","doi":"10.2196/preprints.41953","title":"Evaluating the Applicability of Existing Lexicon-Based Sentiment Analysis Techniques on Family Medicine Resident Feedback Field Notes: Retrospective Cohort Study (Preprint)","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Lexicon; Sentiment analysis; Field (mathematics); Computer science; Natural language processing; Artificial intelligence; Information retrieval; Mathematics","score_opus":0.13070425074339637,"score_gpt":0.42536652452126716,"score_spread":0.2946622737778708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292846260","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99525,0.00018155389,0.0013163572,0.000079020945,0.00002856746,0.0008626268,0.0018476889,0.00001668351,0.00041733324],"genre_scores_gemma":[0.99077094,0.00023482551,0.002806908,0.00021352943,0.000053628715,0.002301796,0.0028698284,0.000035313275,0.00071320665],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99507916,0.0016804286,0.0007733791,0.001155418,0.0009230088,0.000388548],"domain_scores_gemma":[0.9695528,0.011928534,0.0068024094,0.0031010183,0.007460786,0.001154425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013826032,0.00051136804,0.0005400888,0.0020589475,0.0010681322,0.0010563502,0.00064659544,0.0005697352,0.002193091],"category_scores_gemma":[0.04419041,0.0005550322,0.0010581118,0.001590448,0.00069210463,0.0012689837,0.0010715415,0.0006248778,0.0011261024],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010322825,0.0003277491,0.9807928,0.0001865669,0.00016923965,0.00013364079,0.003798504,0.00008360539,0.0006040184,0.00007999898,0.0017580485,0.011033544],"study_design_scores_gemma":[0.00011789138,0.0027763073,0.98806596,0.00012811861,0.0001831375,0.0002713321,0.0041952343,0.0007143613,0.0006802353,0.000096443,0.0027230699,0.000047877576],"about_ca_topic_score_codex":0.014367884,"about_ca_topic_score_gemma":0.01708632,"teacher_disagreement_score":0.014367884,"about_ca_system_score_codex":0.0015280247,"about_ca_system_score_gemma":0.0020810459,"threshold_uncertainty_score":0.07311988},"labels":[],"label_agreement":null},{"id":"W4293193791","doi":"10.1016/j.procs.2022.03.050","title":"IPARS: An Image-based Personalized Advertisement Recommendation System on Social Networks","year":2022,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Bipartite graph; Social media; Set (abstract data type); Graph; Information retrieval; Recommender system; Online advertising; Social network (sociolinguistics); Social graph; Rank (graph theory); World Wide Web; Machine learning; Artificial intelligence; The Internet; Theoretical computer science","score_opus":0.029236057362075313,"score_gpt":0.28003083154594755,"score_spread":0.25079477418387225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293193791","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27821,0.0034477296,0.5832258,0.0013558626,0.0005538195,0.0024193532,0.015216914,0.089058094,0.026512325],"genre_scores_gemma":[0.4939275,0.0011430915,0.4600396,0.0007473863,0.00027399723,0.000590633,0.016066551,0.00047987088,0.026731344],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996916,0.000044089167,0.00002406893,0.00008804779,0.000109932924,0.00004228054],"domain_scores_gemma":[0.9997508,0.000045872075,0.000035322435,0.000046252804,0.000091107104,0.000030722807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032133516,0.0012242473,0.0007988065,0.0019083876,0.0004933105,0.00048547014,0.0010237474,0.0007636153,0.0029658617],"category_scores_gemma":[0.0007286456,0.0003449183,0.0007930789,0.0011894403,0.00015740925,0.0013093001,0.0006044057,0.0006012993,0.0026724811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016365304,0.001116778,0.009729324,0.00045751926,0.00047213255,0.0008080163,0.00023315407,0.01605955,0.040534135,0.0019781736,0.079047434,0.84792733],"study_design_scores_gemma":[0.0002826388,0.0007257765,0.014146275,0.00003763004,0.0003283029,0.0008159595,0.00023511086,0.91655326,0.029580083,0.0028902686,0.03426691,0.00013780955],"about_ca_topic_score_codex":0.0157994,"about_ca_topic_score_gemma":0.028106846,"teacher_disagreement_score":0.0157994,"about_ca_system_score_codex":0.00044239152,"about_ca_system_score_gemma":0.00040508885,"threshold_uncertainty_score":0.031414866},"labels":[],"label_agreement":null},{"id":"W4293574685","doi":"10.2196/37862","title":"Search Term Identification Methods for Computational Health Communication: Word Embedding and Network Approach for Health Content on YouTube","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Cancer Institute; National Institutes of Health","keywords":"Computer science; Information retrieval; Social media; Word embedding; Relevance (law); Health communication; Misinformation; Identification (biology); Natural language processing; Artificial intelligence; World Wide Web; Embedding","score_opus":0.12665661287269095,"score_gpt":0.4513652831696563,"score_spread":0.3247086702969654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293574685","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28183982,0.0069634514,0.68299395,0.0032669408,0.00038421937,0.0013947604,0.011677937,0.0038620187,0.007616931],"genre_scores_gemma":[0.6119502,0.0016616929,0.3635291,0.00032769408,0.0003268772,0.001396935,0.013063653,0.00021770605,0.0075260997],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998804,0.0005105009,0.00015745807,0.00023157397,0.00020199898,0.00009447758],"domain_scores_gemma":[0.9948251,0.0040357383,0.000303803,0.00021580675,0.00053574884,0.00008375253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001662727,0.0011349979,0.00088825804,0.0076274513,0.00072520855,0.0011721546,0.0011120262,0.0013439294,0.0032409655],"category_scores_gemma":[0.010929743,0.00023906605,0.0010126183,0.0048581436,0.00046179112,0.0026245322,0.0011089172,0.001093708,0.0013066622],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013218594,0.0007491422,0.016471608,0.0019163651,0.0003453132,0.0009136622,0.001425269,0.10381724,0.010563152,0.021508634,0.029202942,0.81176484],"study_design_scores_gemma":[0.000040916613,0.00009611457,0.0023113147,0.000051092044,0.00004245433,0.00014788611,0.00036967246,0.9839877,0.0016170631,0.008101218,0.0032100277,0.000024517036],"about_ca_topic_score_codex":0.018906936,"about_ca_topic_score_gemma":0.019453669,"teacher_disagreement_score":0.018906936,"about_ca_system_score_codex":0.0017100282,"about_ca_system_score_gemma":0.0009704632,"threshold_uncertainty_score":0.037593782},"labels":[],"label_agreement":null},{"id":"W4293863214","doi":"10.1109/siu55565.2022.9864897","title":"Natural Language Processing-Based Product Category Classification Model for E-Commerce","year":2022,"lang":"en","type":"article","venue":"2022 30th Signal Processing and Communications Applications Conference (SIU)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Stantec (Canada)","funders":"","keywords":"Computer science; Product (mathematics); Scope (computer science); Turkish; E-commerce; Dynamism; Natural language; World Wide Web; Data science; Artificial intelligence","score_opus":0.05933879780773499,"score_gpt":0.31467989876187163,"score_spread":0.25534110095413665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293863214","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19927712,0.0030873858,0.7714874,0.0032055806,0.0006345219,0.00036591582,0.004141406,0.0034293213,0.014371359],"genre_scores_gemma":[0.8611571,0.0010902704,0.115267284,0.0006765712,0.00018649072,0.00054846925,0.004608815,0.00008558238,0.016379416],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998115,0.000041606607,0.000018134888,0.00005677786,0.000040463194,0.00003166336],"domain_scores_gemma":[0.99960345,0.00018168252,0.00003621504,0.00001946287,0.00014503932,0.000014174037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006077222,0.00050319,0.00042773705,0.00094541005,0.00025648111,0.00071144424,0.0010179058,0.0008468485,0.0034860123],"category_scores_gemma":[0.0012708761,0.00020783555,0.0008928198,0.00096237526,0.00024320086,0.00095686986,0.00029031,0.001337322,0.00184407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055796927,0.00092132215,0.011318834,0.0002936185,0.00022740202,0.00043168117,0.00020789607,0.4986919,0.0067643537,0.015663804,0.024067843,0.4408534],"study_design_scores_gemma":[0.0000041463873,0.000022238772,0.0005094175,0.0000063016214,0.000009241599,0.000014363456,0.000008472145,0.9964238,0.00019117644,0.0022146094,0.0005922734,0.000004002148],"about_ca_topic_score_codex":0.016492862,"about_ca_topic_score_gemma":0.012441045,"teacher_disagreement_score":0.016492862,"about_ca_system_score_codex":0.0009765248,"about_ca_system_score_gemma":0.0007674229,"threshold_uncertainty_score":0.03279376},"labels":[],"label_agreement":null},{"id":"W4296397317","doi":"10.1007/s10936-022-09906-3","title":"Validation of Affective Sentences: Extending Beyond Basic Emotion Categories","year":2022,"lang":"en","type":"article","venue":"Journal of Psycholinguistic Research","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mount Saint Vincent University","funders":"Central Queensland University","keywords":"Psycholinguistics; Natural language processing; Psychology; Linguistics; Cognitive psychology; Computer science; Cognitive science; Cognition; Neuroscience; Philosophy","score_opus":0.0849849398771498,"score_gpt":0.40881953841255897,"score_spread":0.3238345985354092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296397317","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9186216,0.0006496664,0.066572525,0.00027135928,0.00010840844,0.0009463085,0.0013929268,0.00019138359,0.011245803],"genre_scores_gemma":[0.9583626,0.0002922089,0.037573624,0.0002647105,0.000097065196,0.0010339448,0.0016874482,0.00008023776,0.00060814567],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9899295,0.005902136,0.001001318,0.0010221214,0.0018980957,0.00024687639],"domain_scores_gemma":[0.9164201,0.052564505,0.007961486,0.006235588,0.016233355,0.0005849023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012205338,0.00057301257,0.00041992782,0.0017850837,0.00065033353,0.0018431742,0.00069007144,0.0008156543,0.0018463773],"category_scores_gemma":[0.09183521,0.00021086233,0.0005380587,0.0009168626,0.0009352022,0.0020492256,0.0015594972,0.0006788342,0.0007942398],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002604167,0.0009809318,0.22877698,0.004942637,0.00051756494,0.0013685591,0.06909977,0.0023001733,0.26677278,0.005443381,0.0039002649,0.41329277],"study_design_scores_gemma":[0.00023131663,0.0029467875,0.7902509,0.0017933424,0.0005681214,0.0024556492,0.020926543,0.028040726,0.107085146,0.016761249,0.028534323,0.00040587463],"about_ca_topic_score_codex":0.0005808015,"about_ca_topic_score_gemma":0.0006441296,"teacher_disagreement_score":0.012205338,"about_ca_system_score_codex":0.00028634857,"about_ca_system_score_gemma":0.0004421072,"threshold_uncertainty_score":0.06454873},"labels":[],"label_agreement":null},{"id":"W4297448030","doi":"10.21203/rs.3.rs-2104488/v1","title":"Location-based Sentiment Analysis of 2019 Nigeria Presidential Election Using A Voting Ensemble Approach","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Sentiment analysis; Presidential election; Social media; Computer science; Voting; Viewpoints; Polarity (international relations); Artificial intelligence; Microblogging; Geopolitics; Sentence; Natural language processing; Political science; Politics; World Wide Web","score_opus":0.07356168515506219,"score_gpt":0.39286026744807623,"score_spread":0.31929858229301405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297448030","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9725424,0.0002490653,0.020568976,0.00023208556,0.00021390572,0.00007271386,0.0018097342,0.00045795226,0.0038532845],"genre_scores_gemma":[0.9854307,0.00008636637,0.010276146,0.00002284519,0.000066899476,0.000033356482,0.0025943604,0.000019567511,0.0014698536],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996506,0.00007172291,0.000039291695,0.000085283194,0.000092803864,0.000060217648],"domain_scores_gemma":[0.999423,0.00012933665,0.000069711896,0.000048132984,0.00029271172,0.000037087106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007310389,0.00045315374,0.00054774963,0.0012661502,0.00037297053,0.000584422,0.00028094155,0.0002954612,0.0010514992],"category_scores_gemma":[0.001333276,0.00012917034,0.00062914274,0.00070992234,0.000095398995,0.00041406404,0.0003129307,0.00036826878,0.00057240523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002171651,0.0007068941,0.32730985,0.000399965,0.0007196371,0.001233878,0.0010696752,0.0824973,0.08944013,0.0011561743,0.017181976,0.47611293],"study_design_scores_gemma":[0.00002844296,0.0003501944,0.19197643,0.000042134146,0.00028052207,0.00022358364,0.0013144413,0.778652,0.02011471,0.0006087251,0.0063472185,0.00006166702],"about_ca_topic_score_codex":0.0045112954,"about_ca_topic_score_gemma":0.009683564,"teacher_disagreement_score":0.0045112954,"about_ca_system_score_codex":0.0002988207,"about_ca_system_score_gemma":0.00022413624,"threshold_uncertainty_score":0.008970082},"labels":[],"label_agreement":null},{"id":"W4297990466","doi":"10.18280/ria.360402","title":"A Hybrid Model Integrating Adaboost Approach for Sentimental Analysis of Airline Tweets","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"AdaBoost; Sentiment analysis; Computer science; Classifier (UML); Artificial intelligence; Social media; Support vector machine; Machine learning; Christian ministry; Natural language processing; World Wide Web","score_opus":0.05114010390139881,"score_gpt":0.2903764827707751,"score_spread":0.2392363788693763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297990466","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.119720325,0.00081180217,0.87296957,0.0005664424,0.0004090511,0.00025531044,0.00022007941,0.0015738382,0.0034735422],"genre_scores_gemma":[0.80984086,0.00044840778,0.17992957,0.00033977657,0.00020644754,0.00033565972,0.0005158814,0.000085510444,0.0082978755],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994641,0.00012198423,0.000043340908,0.00014672813,0.00013644256,0.00008742555],"domain_scores_gemma":[0.99945766,0.00013525753,0.000040159826,0.000019885789,0.00031940118,0.000027640614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012298287,0.0008351038,0.0011169125,0.0010707419,0.0005181044,0.0010764105,0.0012360623,0.001013502,0.0013669508],"category_scores_gemma":[0.0011188404,0.00040606994,0.0010711285,0.0007920571,0.00023549974,0.0009629109,0.00037380794,0.0010106681,0.00081381976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006584689,0.0009336302,0.012227309,0.00029606555,0.00051440217,0.00018481427,0.00027130134,0.3559946,0.022268333,0.0025441705,0.006276561,0.59783036],"study_design_scores_gemma":[0.000006405089,0.00008115682,0.00061132223,0.0000062350277,0.000021705036,0.000017893022,0.00002132653,0.9970968,0.0012511851,0.00033071602,0.00054838514,0.0000068753748],"about_ca_topic_score_codex":0.009930594,"about_ca_topic_score_gemma":0.009500886,"teacher_disagreement_score":0.009930594,"about_ca_system_score_codex":0.0007259927,"about_ca_system_score_gemma":0.0009747034,"threshold_uncertainty_score":0.019745529},"labels":[],"label_agreement":null},{"id":"W4300927300","doi":"10.1007/978-3-031-02167-1_4","title":"Applications of Social Media Text Analysis","year":2018,"lang":"en","type":"book-chapter","venue":"Synthesis lectures on human language technologies","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Social media; Computer science; World Wide Web","score_opus":0.023506218752693507,"score_gpt":0.28690396626457326,"score_spread":0.2633977475118798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4300927300","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019174822,0.03325499,0.7893919,0.0073010977,0.004850152,0.00051690737,0.005274326,0.008579576,0.13165615],"genre_scores_gemma":[0.20905511,0.0278466,0.66229534,0.0012828537,0.0066414434,0.00070981536,0.007314372,0.0027425375,0.08211193],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99835485,0.00048804635,0.00010307809,0.00021362942,0.0007768305,0.00006352062],"domain_scores_gemma":[0.9946537,0.0034951137,0.00023858859,0.00040862622,0.0010686916,0.00013533804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001965924,0.001246096,0.00058542413,0.0064419406,0.0005887451,0.0037252128,0.0007290801,0.000815568,0.015204191],"category_scores_gemma":[0.007223853,0.0004313004,0.0007096728,0.004995295,0.0005242212,0.0029165626,0.0014465118,0.00094836735,0.009081882],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050542247,0.00007212194,0.0016676611,0.00045826932,0.000094459385,0.00013279027,0.00031012518,0.0016998256,0.0074990136,0.0184141,0.048746776,0.9208542],"study_design_scores_gemma":[0.000038108094,0.000110390225,0.007754374,0.00069543236,0.00015684318,0.00095243804,0.0011485957,0.14148544,0.033955604,0.24003972,0.5735366,0.00012645358],"about_ca_topic_score_codex":0.00059051224,"about_ca_topic_score_gemma":0.0009811074,"teacher_disagreement_score":0.015204191,"about_ca_system_score_codex":0.0004767326,"about_ca_system_score_gemma":0.00037900588,"threshold_uncertainty_score":0.050863087},"labels":[],"label_agreement":null},{"id":"W4301431394","doi":"10.1007/978-981-19-3575-6_19","title":"A Rule-Based Sentiment Analysis of WhatsApp Reviews in Telugu Language","year":2022,"lang":"en","type":"book-chapter","venue":"Smart innovation, systems and technologies","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Sentiment analysis; Artificial intelligence; Support vector machine; Machine learning; Computer science; Popularity; Natural language processing; Telugu; Field (mathematics); Mean squared error; Data mining; Statistics; Mathematics; Psychology","score_opus":0.030274652127790392,"score_gpt":0.2673129483426058,"score_spread":0.23703829621481542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4301431394","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7735478,0.0022580822,0.17624879,0.0014231146,0.0008100207,0.00092922215,0.012801901,0.0044873166,0.027493712],"genre_scores_gemma":[0.7915008,0.0009253157,0.1755017,0.00019044419,0.00030652573,0.00036342005,0.016543861,0.0002204868,0.01444739],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991007,0.00025974002,0.00011991172,0.00016846361,0.00028574833,0.00006550618],"domain_scores_gemma":[0.99814165,0.0006505237,0.00013191284,0.000049585586,0.0009709502,0.000055394477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011198987,0.00044413135,0.0004511872,0.002052362,0.0006041969,0.0013798762,0.0004072562,0.0003689018,0.0028652414],"category_scores_gemma":[0.0036629844,0.00015852008,0.00076607853,0.0015453935,0.00014745539,0.0009876527,0.0003544818,0.00049471617,0.0024268483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00084099104,0.0004645585,0.05194769,0.00077554595,0.00031338623,0.0010762964,0.0018662453,0.0053602923,0.0658482,0.0014771059,0.038277786,0.8317519],"study_design_scores_gemma":[0.000089907764,0.00080963114,0.21182413,0.00030867444,0.00063362287,0.001786866,0.0052900594,0.6681685,0.058652174,0.002454805,0.049794924,0.00018676942],"about_ca_topic_score_codex":0.008591462,"about_ca_topic_score_gemma":0.013334023,"teacher_disagreement_score":0.008591462,"about_ca_system_score_codex":0.0004813872,"about_ca_system_score_gemma":0.0006105586,"threshold_uncertainty_score":0.01708293},"labels":[],"label_agreement":null},{"id":"W4304806684","doi":"10.1007/s11634-022-00522-6","title":"On smoothing and scaling language model for sentiment based information retrieval","year":2022,"lang":"en","type":"article","venue":"Advances in Data Analysis and Classification","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Sentiment analysis; Latent Dirichlet allocation; Relevance (law); Social media; Smoothing; Probabilistic logic; Field (mathematics); Information retrieval; Language model; Artificial intelligence; Dirichlet distribution; Topic model; Data mining; World Wide Web; Mathematics","score_opus":0.034786673591868544,"score_gpt":0.32444836377708386,"score_spread":0.2896616901852153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4304806684","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01557853,0.0007476521,0.9815026,0.00032932305,0.00015099566,0.00005953692,0.00013562183,0.0007500749,0.0007457561],"genre_scores_gemma":[0.4817678,0.001965324,0.50051624,0.0006039741,0.00069547794,0.0003693293,0.0015714302,0.00047366385,0.012036732],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987638,0.0004388864,0.0001227122,0.0002308648,0.0003317134,0.00011203685],"domain_scores_gemma":[0.9968335,0.0018153935,0.00015371996,0.00035743418,0.0007620864,0.00007781758],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027822529,0.00078084465,0.0014312268,0.001347361,0.0008017309,0.0012688483,0.0012898798,0.00097157055,0.0023698502],"category_scores_gemma":[0.0085250195,0.00041006773,0.0013585706,0.00180163,0.0006150722,0.0032501207,0.00093989016,0.0017338771,0.0013973202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056616333,0.0003990508,0.0021938377,0.00036642782,0.00028729724,0.00024299009,0.00047416388,0.27143213,0.026503608,0.09194465,0.014917541,0.5906722],"study_design_scores_gemma":[0.000008687254,0.000035127257,0.00020654454,0.0000056427134,0.000021882097,0.000022006134,0.0000146693155,0.9823437,0.0010539254,0.015276932,0.000997135,0.00001383472],"about_ca_topic_score_codex":0.0066877245,"about_ca_topic_score_gemma":0.0049644797,"teacher_disagreement_score":0.0066877245,"about_ca_system_score_codex":0.00076439796,"about_ca_system_score_gemma":0.0009994721,"threshold_uncertainty_score":0.014714122},"labels":[],"label_agreement":null},{"id":"W4306160603","doi":"10.1177/00222437221134802","title":"Beyond Sentiment: The Value and Measurement of Consumer Certainty in Language","year":2022,"lang":"en","type":"article","venue":"Journal of Marketing Research","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Certainty; Lexicon; Sentiment analysis; Valence (chemistry); Computer science; Consumer confidence index; Psychology; Natural language processing; Marketing","score_opus":0.04945216976022157,"score_gpt":0.34000756090629597,"score_spread":0.2905553911460744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306160603","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44523564,0.011753811,0.38331345,0.022616923,0.0015723732,0.0004966137,0.0044154455,0.001125625,0.12947012],"genre_scores_gemma":[0.95853734,0.0015849815,0.034783788,0.0013414655,0.00076330017,0.00013472328,0.0007802032,0.00012427043,0.0019499592],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99239093,0.0037917285,0.00051923626,0.0008770756,0.0022169622,0.00020406627],"domain_scores_gemma":[0.9528353,0.03199128,0.0059204255,0.0037127773,0.0048998008,0.00064043666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0069798687,0.0008600615,0.0008694043,0.004344378,0.00096754346,0.008864381,0.0008701008,0.0014380648,0.0040380047],"category_scores_gemma":[0.080542915,0.00041179312,0.0009169805,0.004812816,0.0033881937,0.014491297,0.002784899,0.0023153883,0.0011369046],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010527031,0.0002715137,0.1736729,0.0015597208,0.0009655307,0.0004126581,0.011591102,0.009154281,0.007821231,0.24138966,0.020646501,0.53146225],"study_design_scores_gemma":[0.000105491585,0.0005708748,0.110588714,0.0013696546,0.0006169344,0.0010204525,0.007796216,0.115106724,0.008862869,0.67819417,0.07526996,0.00049796334],"about_ca_topic_score_codex":0.002564069,"about_ca_topic_score_gemma":0.0016045626,"teacher_disagreement_score":0.008864381,"about_ca_system_score_codex":0.0014271743,"about_ca_system_score_gemma":0.00058505486,"threshold_uncertainty_score":0.036913514},"labels":[],"label_agreement":null},{"id":"W4307423220","doi":"10.5539/mas.v16n4p29","title":"An Online Machine Learning Approach to Sentiment Analysis in Social Media","year":2022,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Sentiment analysis; Machine learning; Task (project management); Artificial intelligence; Fraction (chemistry); Online learning; Social media; Online and offline; Range (aeronautics); Online machine learning; Offline learning; Semi-supervised learning; World Wide Web","score_opus":0.040828658914939815,"score_gpt":0.2797778779990912,"score_spread":0.2389492190841514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307423220","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042945523,0.0011203833,0.94582736,0.0010801756,0.00039865533,0.00029521252,0.0007753871,0.0030573446,0.0044999947],"genre_scores_gemma":[0.5892074,0.0010165663,0.39962447,0.00062694954,0.0009679979,0.0004998723,0.0015445286,0.00030402467,0.00620829],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99845934,0.00060794863,0.00008914845,0.0003692471,0.000375007,0.00009930265],"domain_scores_gemma":[0.9954976,0.002408811,0.00050160335,0.0006329921,0.0008316982,0.00012731926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027698104,0.0014658518,0.0011639018,0.002195816,0.0006549687,0.0015293763,0.0017262946,0.0011908665,0.0033178646],"category_scores_gemma":[0.0076867817,0.00035290275,0.00081202993,0.0017141242,0.0005390319,0.0033299273,0.0010610147,0.0020485062,0.0029078892],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052327226,0.0013695797,0.010706905,0.00036970066,0.00031010204,0.00016955356,0.00026353498,0.10829899,0.011796701,0.0073808315,0.013670058,0.8451408],"study_design_scores_gemma":[0.000019182507,0.00016191918,0.0012257823,0.00001880404,0.000028904427,0.00007778583,0.00005181332,0.9831786,0.0033482304,0.008771967,0.0030981502,0.000018769837],"about_ca_topic_score_codex":0.0020379587,"about_ca_topic_score_gemma":0.0025418375,"teacher_disagreement_score":0.0033178646,"about_ca_system_score_codex":0.00068671146,"about_ca_system_score_gemma":0.00073758146,"threshold_uncertainty_score":0.014648318},"labels":[],"label_agreement":null},{"id":"W4308721738","doi":"10.3390/bdcc6040129","title":"Detecting and Understanding Sentiment Trends and Emotion Patterns of Twitter Users—A Study on the Demise of a Bollywood Celebrity","year":2022,"lang":"en","type":"article","venue":"Big Data and Cognitive Computing","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Timeline; Sentiment analysis; Demise; Social media; Expression (computer science); Task (project management); Psychology; Emotion detection; Computer science; Natural language processing; History; Artificial intelligence; World Wide Web; Emotion recognition; Political science; Engineering","score_opus":0.18424248349473837,"score_gpt":0.3194875530399939,"score_spread":0.1352450695452555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308721738","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9991165,0.000022534907,0.000036686026,0.000080304526,0.000004185452,0.0000041669286,0.00007384438,0.0000013260586,0.0006604697],"genre_scores_gemma":[0.9975752,0.00013459643,0.00017152003,0.000040666797,0.000013797612,0.000011378607,0.00024234867,0.000004152438,0.0018063734],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99988997,0.000031691674,0.0000069096427,0.000020958028,0.000027746983,0.000022683844],"domain_scores_gemma":[0.9994215,0.00017279093,0.0001545332,0.000023904951,0.00011884164,0.00010850808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035006428,0.000110873385,0.00015761155,0.0007461808,0.0008954266,0.0008025835,0.00017496223,0.00030669774,0.0012436961],"category_scores_gemma":[0.0011811953,0.000092868955,0.00014385888,0.0006350569,0.0002876483,0.00089773914,0.00034297322,0.00042576258,0.0003804584],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004304845,0.00037470012,0.77805936,0.00020927674,0.00005803546,0.0022333914,0.14962281,0.00026068444,0.012277111,0.0010606241,0.0050571323,0.0503564],"study_design_scores_gemma":[0.0000025866043,0.00018843809,0.86271125,0.000036383462,0.000016216169,0.00032654885,0.12742355,0.0011681719,0.0011367003,0.00012045267,0.006851869,0.000017895274],"about_ca_topic_score_codex":0.007568647,"about_ca_topic_score_gemma":0.016595567,"teacher_disagreement_score":0.007568647,"about_ca_system_score_codex":0.0003736841,"about_ca_system_score_gemma":0.00013571372,"threshold_uncertainty_score":0.015049219},"labels":[],"label_agreement":null},{"id":"W4309398565","doi":"10.54097/hset.v16i.2224","title":"Model Comparison in Sentiment Analysis: A Case Study of Amazon Product Reviews","year":2022,"lang":"en","type":"article","venue":"Highlights in Science Engineering and Technology","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Sentiment analysis; Artificial intelligence; Computer science; Binary classification; Naive Bayes classifier; Class (philosophy); Convolutional neural network; Support vector machine; Machine learning; Set (abstract data type); Artificial neural network; Binary number; Deep learning; Product (mathematics); Data set; Data mining; Mathematics","score_opus":0.027980801080807884,"score_gpt":0.28894541574953614,"score_spread":0.2609646146687283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309398565","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9282525,0.0020291365,0.05570484,0.0018194234,0.00019862728,0.00031124495,0.001897601,0.0005741552,0.0092123505],"genre_scores_gemma":[0.9555224,0.0004179289,0.040417008,0.00012917862,0.000068326655,0.000102004095,0.0012251931,0.0000672181,0.0020506622],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99853456,0.00087306066,0.000086075095,0.00017481789,0.00026780358,0.00006361839],"domain_scores_gemma":[0.995758,0.0028929967,0.00021298719,0.00019010162,0.0008742898,0.000071712304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026066739,0.00074950216,0.000531142,0.00087845215,0.0004065109,0.00085113605,0.00056111655,0.0008080805,0.0009521957],"category_scores_gemma":[0.006949134,0.00015634896,0.0007443626,0.000727602,0.00023224,0.00073152705,0.00038821928,0.0006208535,0.00037439298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027693142,0.0018389327,0.08965907,0.002103943,0.0008849496,0.0027323042,0.001671416,0.4010145,0.019703623,0.009445509,0.036232114,0.43194434],"study_design_scores_gemma":[0.00004473278,0.00024779022,0.010366857,0.000030020141,0.00006228564,0.00017655094,0.0003964152,0.97922933,0.0043663415,0.0016109954,0.003445232,0.000023427963],"about_ca_topic_score_codex":0.013187244,"about_ca_topic_score_gemma":0.015297187,"teacher_disagreement_score":0.013187244,"about_ca_system_score_codex":0.000977308,"about_ca_system_score_gemma":0.00040107785,"threshold_uncertainty_score":0.026220918},"labels":[],"label_agreement":null},{"id":"W4309776725","doi":"10.1007/s00146-022-01594-w","title":"Natural language processing analysis applied to COVID-19 open-text opinions using a distilBERT model for sentiment categorization","year":2022,"lang":"en","type":"article","venue":"AI & Society","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Universidad de Deusto","keywords":"Sentiment analysis; Categorization; Recall; Computer science; Natural language processing; Coronavirus disease 2019 (COVID-19); Task (project management); Artificial intelligence; Precision and recall; Feeling; F1 score; Data science; Machine learning; Psychology; Cognitive psychology; Social psychology; Infectious disease (medical specialty); Disease; Engineering; Medicine","score_opus":0.04092020058568206,"score_gpt":0.35737817511951697,"score_spread":0.3164579745338349,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309776725","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67303604,0.0005251263,0.31088898,0.0019267912,0.00026077032,0.00045479753,0.003307762,0.0031923582,0.006407422],"genre_scores_gemma":[0.9425476,0.00013207148,0.05223681,0.00013201697,0.00007957888,0.0001036136,0.0022857252,0.000040723327,0.0024418111],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959415,0.00013852997,0.00003966505,0.00009197255,0.00008503881,0.000050577743],"domain_scores_gemma":[0.99841714,0.00091981504,0.00009156791,0.000067521745,0.00045456312,0.00004940138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011114908,0.00060410623,0.00032347874,0.0013918021,0.00036229257,0.0010823244,0.0004951183,0.0006395019,0.0022962443],"category_scores_gemma":[0.0031237379,0.00012753083,0.00079552253,0.00055114744,0.0001814008,0.0008240659,0.0003599713,0.0009059112,0.0012679724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014912535,0.0015282441,0.042832114,0.00053505495,0.0004559238,0.0010825603,0.0013060368,0.17089465,0.059632305,0.0067500644,0.020852966,0.6926389],"study_design_scores_gemma":[0.000008880159,0.00006630302,0.003389222,0.000009575333,0.000025572188,0.00003955636,0.000096670185,0.9907674,0.0035499693,0.0011921474,0.00084545946,0.000009185011],"about_ca_topic_score_codex":0.007875835,"about_ca_topic_score_gemma":0.008257731,"teacher_disagreement_score":0.007875835,"about_ca_system_score_codex":0.0010831837,"about_ca_system_score_gemma":0.00050435495,"threshold_uncertainty_score":0.015659988},"labels":[],"label_agreement":null},{"id":"W4310539674","doi":"10.3389/fpubh.2022.952363","title":"Nowcasting unemployment rate during the COVID-19 pandemic using Twitter data: The case of South Africa","year":2022,"lang":"en","type":"article","venue":"Frontiers in Public Health","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"International Development Research Centre; Styrelsen för Internationellt Utvecklingssamarbete","keywords":"Nowcasting; Unemployment; Coronavirus disease 2019 (COVID-19); Pandemic; Recession; Leverage (statistics); Sentiment analysis; Unemployment rate; Economics; Mean squared error; Econometrics; Demographic economics; Statistics; Computer science; Geography; Medicine; Economic growth; Mathematics; Macroeconomics; Artificial intelligence","score_opus":0.23282256643615565,"score_gpt":0.35732785846405407,"score_spread":0.12450529202789842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310539674","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.989608,0.00040120035,0.0017074,0.0020331836,0.00015523171,0.000037531125,0.0043547107,0.00008046037,0.001622254],"genre_scores_gemma":[0.9926456,0.00029463723,0.002185988,0.00009505279,0.00010099451,0.00002706688,0.003965683,0.00001369923,0.00067129894],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976116,0.00009084403,0.000021018455,0.000044443354,0.000035904417,0.00004671917],"domain_scores_gemma":[0.99936014,0.00030252518,0.00011832298,0.000046316552,0.00011680025,0.000055964465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006972134,0.00043677006,0.0002937843,0.00084000686,0.0003654569,0.0005344218,0.00027659294,0.00074678723,0.00056578644],"category_scores_gemma":[0.0026929267,0.00011668945,0.00037390753,0.00085714774,0.00021804696,0.0007430798,0.00045845273,0.00058697315,0.00024154627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017728197,0.00043446428,0.70377326,0.00089104124,0.00042372217,0.0050215353,0.0044467724,0.108153835,0.015515511,0.0037266454,0.03362738,0.12221298],"study_design_scores_gemma":[0.00006974026,0.00020883494,0.5044538,0.00022708763,0.00016980282,0.0004251535,0.009326718,0.45940018,0.005232668,0.0015566633,0.0188219,0.000107475855],"about_ca_topic_score_codex":0.033831418,"about_ca_topic_score_gemma":0.03515805,"teacher_disagreement_score":0.033831418,"about_ca_system_score_codex":0.00054044515,"about_ca_system_score_gemma":0.00032872998,"threshold_uncertainty_score":0.06726897},"labels":[],"label_agreement":null},{"id":"W4311175910","doi":"10.36227/techrxiv.21699203.v1","title":"Aspect Based Sentiment Analysis - Twitter","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Sentiment analysis; Popularity; Computer science; Categorization; Social media; Service (business); Product (mathematics); Data science; Set (abstract data type); Analytics; World Wide Web; Artificial intelligence; Business; Psychology; Marketing","score_opus":0.03826630628439207,"score_gpt":0.29912388540448287,"score_spread":0.2608575791200908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311175910","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39600766,0.0024513023,0.3028996,0.004082847,0.0017986901,0.0035880655,0.13109672,0.037376933,0.12069813],"genre_scores_gemma":[0.7140298,0.0013597363,0.15601988,0.00079046626,0.00066327886,0.0017673018,0.092698075,0.0012174693,0.031454097],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993217,0.000103144856,0.00007793745,0.00012735014,0.00028747745,0.000082458384],"domain_scores_gemma":[0.999382,0.0000954148,0.00010924827,0.00005211916,0.00032295345,0.000038200025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005989062,0.00076053943,0.00040689742,0.0019980026,0.000470009,0.0013933756,0.00033659066,0.00037317144,0.005566189],"category_scores_gemma":[0.0024205926,0.00019871723,0.00081477885,0.0015326695,0.00015031664,0.0013842125,0.0007306398,0.00055328256,0.0054397318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001096638,0.0004387571,0.08046616,0.0010206867,0.00036052987,0.00089540513,0.001509042,0.009363213,0.06968363,0.008822308,0.17199767,0.654346],"study_design_scores_gemma":[0.00016627724,0.00057678693,0.17848156,0.00029574864,0.00026721033,0.0011829506,0.0037449242,0.40313774,0.060712803,0.026227774,0.32497302,0.00023323752],"about_ca_topic_score_codex":0.0029367942,"about_ca_topic_score_gemma":0.0046220706,"teacher_disagreement_score":0.005566189,"about_ca_system_score_codex":0.00053104607,"about_ca_system_score_gemma":0.00042712057,"threshold_uncertainty_score":0.01862073},"labels":[],"label_agreement":null},{"id":"W4311176110","doi":"10.36227/techrxiv.21699203","title":"Aspect Based Sentiment Analysis - Twitter","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Sentiment analysis; Popularity; Computer science; Categorization; Social media; Service (business); Product (mathematics); Set (abstract data type); Data science; Analytics; World Wide Web; Artificial intelligence; Business; Psychology; Marketing","score_opus":0.03826630628439207,"score_gpt":0.29912388540448287,"score_spread":0.2608575791200908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311176110","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28356954,0.0030057922,0.36607593,0.008192756,0.002697981,0.0034648757,0.15636456,0.06103218,0.11559644],"genre_scores_gemma":[0.63309085,0.0017255115,0.19952922,0.0014497474,0.0008996244,0.0015413337,0.122611865,0.0018566244,0.03729522],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992957,0.00013508018,0.00008204552,0.00013923894,0.00028041095,0.00006750812],"domain_scores_gemma":[0.99927574,0.00013344536,0.00010784578,0.000084584804,0.00035134435,0.000047167516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072463305,0.0008083339,0.00041461442,0.0017171182,0.0004711546,0.0015195467,0.00045602393,0.00049375347,0.0062770755],"category_scores_gemma":[0.0030072439,0.0002527965,0.0008842726,0.0013659468,0.00017161784,0.0018744065,0.0008797806,0.0006825898,0.006595668],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00076419715,0.0003997748,0.051381156,0.0007282645,0.00028967284,0.0006172501,0.0008831242,0.010463843,0.043922737,0.007942859,0.239538,0.64306927],"study_design_scores_gemma":[0.00014382308,0.00043678432,0.090604275,0.000249051,0.00017825414,0.0007525162,0.0020499793,0.5038719,0.046373554,0.026168589,0.32896876,0.00020253022],"about_ca_topic_score_codex":0.0039504245,"about_ca_topic_score_gemma":0.006464071,"teacher_disagreement_score":0.0062770755,"about_ca_system_score_codex":0.0006448864,"about_ca_system_score_gemma":0.00041562392,"threshold_uncertainty_score":0.020998955},"labels":[],"label_agreement":null},{"id":"W4311397192","doi":"10.5539/ies.v16n1p42","title":"A Contrastive Study of Hedges in COVID-19 Reports Selected from China Daily and The New York Times","year":2022,"lang":"en","type":"article","venue":"International Education Studies","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Hebei University","keywords":"Mainstream; Coronavirus disease 2019 (COVID-19); Newspaper; China; Contrastive analysis; 2019-20 coronavirus outbreak; Psychology; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Linguistics; Sociology; Geography; Media studies; Political science; Medicine","score_opus":0.03920419422306908,"score_gpt":0.3542256439616822,"score_spread":0.3150214497386131,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311397192","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99691916,0.00016978747,0.00029989923,0.00007396168,0.00002209241,0.00003052983,0.00058829266,0.0000037182128,0.0018925687],"genre_scores_gemma":[0.99606186,0.00014865953,0.000786848,0.000054709017,0.00005327214,0.00006976846,0.0019883462,0.000010529256,0.0008259386],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.99861896,0.000557452,0.00017689035,0.00018577736,0.0003503346,0.00011056991],"domain_scores_gemma":[0.98512113,0.008657663,0.0027818868,0.0006119133,0.002415596,0.00041170238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016273506,0.00017672678,0.00024082352,0.0041247676,0.0009873849,0.00093184813,0.00024254552,0.0003299677,0.00095442514],"category_scores_gemma":[0.010882215,0.00011382557,0.00016496948,0.004694389,0.000718999,0.0008404548,0.000919736,0.00045463067,0.00015615647],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001991431,0.00044342256,0.66355354,0.0012357167,0.00025895817,0.0037969744,0.18054216,0.0004079211,0.03660423,0.0052264766,0.008803323,0.09713591],"study_design_scores_gemma":[0.000022016746,0.00018423499,0.9296577,0.00009551224,0.00007678182,0.0007291446,0.046032887,0.001161739,0.0046937293,0.0002369729,0.017072495,0.000036756726],"about_ca_topic_score_codex":0.0056325886,"about_ca_topic_score_gemma":0.011173163,"teacher_disagreement_score":0.0056325886,"about_ca_system_score_codex":0.00080362626,"about_ca_system_score_gemma":0.00029177425,"threshold_uncertainty_score":0.011199594},"labels":[],"label_agreement":null},{"id":"W4312420955","doi":"10.4000/books.aaccademia.11017","title":"KERMIT for Sentiment Analysis in Italian Healthcare Reviews","year":2022,"lang":"en","type":"book-chapter","venue":"Accademia University Press eBooks","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"Computer science; Syntax; Sentiment analysis; Domain (mathematical analysis); Health care; Natural language processing; Artificial intelligence; Mathematics; Political science","score_opus":0.06200890571323062,"score_gpt":0.2771779692913871,"score_spread":0.2151690635781565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312420955","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.077249184,0.007237003,0.7689614,0.0054168105,0.0018203326,0.0006679676,0.018136289,0.022632737,0.09787821],"genre_scores_gemma":[0.4778015,0.0035494103,0.4354278,0.0012488991,0.0012447891,0.0007960961,0.024706949,0.0019496101,0.05327497],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992568,0.00030840535,0.000046512265,0.00010971944,0.0002453874,0.000033258795],"domain_scores_gemma":[0.999159,0.0004739339,0.00006604968,0.000111244284,0.0001618422,0.000027844617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010840503,0.0008381096,0.00029405442,0.0014809078,0.00032658642,0.0014530779,0.00041649153,0.0006379125,0.011582505],"category_scores_gemma":[0.004050556,0.00021719685,0.0006829294,0.0010612098,0.00022165147,0.001621367,0.0007829231,0.0009415959,0.007470671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021191307,0.00015714078,0.008613122,0.0008091545,0.00015284226,0.0004992962,0.00077199715,0.014861173,0.0107722925,0.047202628,0.23885113,0.6770974],"study_design_scores_gemma":[0.00003477449,0.00014440276,0.014394643,0.00038475284,0.000088044224,0.0010351621,0.00039743743,0.5620067,0.0076443134,0.043586217,0.37019935,0.00008416572],"about_ca_topic_score_codex":0.0017567237,"about_ca_topic_score_gemma":0.004362535,"teacher_disagreement_score":0.011582505,"about_ca_system_score_codex":0.0007550037,"about_ca_system_score_gemma":0.00041096585,"threshold_uncertainty_score":0.03874731},"labels":[],"label_agreement":null},{"id":"W4312634722","doi":"10.1007/978-3-031-02157-2_4","title":"Applications of Social Media Text Analysis","year":2015,"lang":"en","type":"book-chapter","venue":"Synthesis lectures on human language technologies","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Université de Montréal","funders":"","keywords":"Social media; Computer science; World Wide Web","score_opus":0.038450178203179614,"score_gpt":0.30300424556272315,"score_spread":0.26455406735954357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312634722","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018115064,0.033221014,0.79208666,0.0068671666,0.004270277,0.0004938121,0.004835182,0.008197076,0.13191369],"genre_scores_gemma":[0.20524675,0.027671121,0.67073345,0.0012471606,0.0059279036,0.00066540414,0.0064277076,0.002502502,0.07957797],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99836093,0.0004761246,0.00010212031,0.00020872515,0.0007905351,0.000061628525],"domain_scores_gemma":[0.99474597,0.0034541069,0.00023220028,0.00040407642,0.0010367911,0.00012691386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019832908,0.0012348919,0.0005944499,0.0064024646,0.0005813631,0.0036415898,0.0007297919,0.000812904,0.01435085],"category_scores_gemma":[0.0071514584,0.00043737987,0.0007078046,0.0048731696,0.00054954883,0.0028775956,0.0014500314,0.00093573175,0.008665272],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047850903,0.00006787144,0.0016314505,0.0004421231,0.000091005844,0.00013048397,0.00031234743,0.0017318712,0.007035496,0.019345587,0.04560393,0.9235599],"study_design_scores_gemma":[0.00003735106,0.00010969067,0.008050307,0.00070270244,0.00015513228,0.0009834254,0.0011666497,0.13730234,0.03288413,0.2612344,0.5572468,0.00012719522],"about_ca_topic_score_codex":0.00060300634,"about_ca_topic_score_gemma":0.0010169472,"teacher_disagreement_score":0.01435085,"about_ca_system_score_codex":0.00047616402,"about_ca_system_score_gemma":0.0003784369,"threshold_uncertainty_score":0.048008442},"labels":[],"label_agreement":null},{"id":"W4312988282","doi":"10.1016/j.procs.2022.09.473","title":"User Sentiment Analysis in Conversational Systems Based on Augmentation and Attention-based BiLSTM","year":2022,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Sentiment analysis; Benchmark (surveying); Task (project management); Conversation; Artificial intelligence; Service (business); Mechanism (biology); Machine learning; Natural language processing","score_opus":0.01328517642429894,"score_gpt":0.2476918364515747,"score_spread":0.23440666002727575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312988282","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28499094,0.002198402,0.69253594,0.001097591,0.0006453318,0.00023445081,0.0009478267,0.010012482,0.007337055],"genre_scores_gemma":[0.9105188,0.00042985726,0.081840664,0.00034247682,0.0001636305,0.00014909744,0.0015013898,0.00018339402,0.00487064],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996712,0.0000732174,0.00002158372,0.00011294834,0.00006259016,0.000058469108],"domain_scores_gemma":[0.99957794,0.00014036744,0.000038244332,0.000040992552,0.00016917002,0.00003320267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007630301,0.0010547905,0.0006426549,0.00054092315,0.00039775285,0.0006154959,0.0008287252,0.0006632686,0.0020160328],"category_scores_gemma":[0.001896595,0.00032662658,0.0006278043,0.00038776232,0.00029998305,0.0014174384,0.0010038523,0.0011268303,0.0014108958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010082982,0.00062345754,0.007863879,0.00038703697,0.00021456978,0.00045118554,0.00079478783,0.071054295,0.11553671,0.0033220493,0.015068031,0.7836757],"study_design_scores_gemma":[0.000010584678,0.00010769224,0.00142693,0.0000140347165,0.000035524652,0.000053430325,0.00005678025,0.98511016,0.009095338,0.0024341955,0.001639865,0.000015437025],"about_ca_topic_score_codex":0.003620075,"about_ca_topic_score_gemma":0.005089877,"teacher_disagreement_score":0.003620075,"about_ca_system_score_codex":0.0005163188,"about_ca_system_score_gemma":0.0005625997,"threshold_uncertainty_score":0.0071980357},"labels":[],"label_agreement":null},{"id":"W4313155193","doi":"10.1007/978-3-031-02157-2","title":"Natural Language Processing for Social Media","year":2015,"lang":"en","type":"book","venue":"Synthesis lectures on human language technologies","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Université de Montréal","funders":"","keywords":"Social media; Interpersonal communication; Natural (archaeology); Computer science; Psychology; Sociology; Internet privacy; World Wide Web; Communication; History","score_opus":0.043810293870779074,"score_gpt":0.3212424037328657,"score_spread":0.27743210986208666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313155193","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034117494,0.031903498,0.8356038,0.004202885,0.0040633604,0.00033499056,0.0051937304,0.015930515,0.09935546],"genre_scores_gemma":[0.057586893,0.023111887,0.63637125,0.0015214655,0.0033557147,0.0007590442,0.017019581,0.0033259697,0.25694814],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994604,0.00009431635,0.000053186734,0.00010375391,0.0002585669,0.00002980491],"domain_scores_gemma":[0.99921167,0.00039594842,0.000036501584,0.00010747797,0.00022658243,0.000021858852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006770313,0.0009392532,0.0006397305,0.0013661609,0.00043987058,0.0021775642,0.0007567645,0.0005264738,0.026416665],"category_scores_gemma":[0.0018315956,0.00036168436,0.0006972997,0.0012082332,0.00056751387,0.0032778427,0.00096239086,0.0012306501,0.018279118],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033705008,0.00004315416,0.0000938999,0.0006172002,0.000037594276,0.000104223895,0.00010244709,0.001466412,0.0067728804,0.037852503,0.25960743,0.6932686],"study_design_scores_gemma":[0.000020259922,0.00004013114,0.0006754702,0.00029246273,0.00003856769,0.00034510915,0.00014805899,0.040674917,0.009585702,0.2134453,0.7346892,0.000044946886],"about_ca_topic_score_codex":0.0014714313,"about_ca_topic_score_gemma":0.0022302172,"teacher_disagreement_score":0.026416665,"about_ca_system_score_codex":0.00059897185,"about_ca_system_score_gemma":0.000646817,"threshold_uncertainty_score":0.08837253},"labels":[],"label_agreement":null},{"id":"W4313294355","doi":"10.1080/0144929x.2022.2156387","title":"Machine learning techniques for emotion detection and sentiment analysis: current state, challenges, and future directions","year":2022,"lang":"en","type":"article","venue":"Behaviour and Information Technology","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":153,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"California State Council on Developmental Disabilities; Directorate for Engineering","keywords":"Current (fluid); Sentiment analysis; Emotion detection; State (computer science); Computer science; Cognitive psychology; Psychology; Data science; Artificial intelligence; Cognitive science; Emotion recognition; Engineering; Electrical engineering","score_opus":0.0116577232750635,"score_gpt":0.2512381424678022,"score_spread":0.23958041919273868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313294355","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013063571,0.97394276,0.012882503,0.009258359,0.0005417295,0.000086562635,0.000082667524,0.000104850034,0.0017941532],"genre_scores_gemma":[0.01842598,0.94057244,0.035298586,0.0026637795,0.0016310285,0.0003257794,0.00021455427,0.000046379468,0.00082138064],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9937687,0.0030434208,0.00069217384,0.00065988756,0.0016114763,0.00022433822],"domain_scores_gemma":[0.9441785,0.04453698,0.0023399089,0.0007831318,0.007775649,0.00038581464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020249808,0.0012313343,0.0023602187,0.0054035066,0.0005869125,0.0040048747,0.0023318713,0.0023575278,0.0030703829],"category_scores_gemma":[0.02599262,0.0007718796,0.002167399,0.0051694964,0.001679981,0.008666362,0.0013601196,0.0032617531,0.0016907565],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007611937,0.0001213336,0.0020223376,0.014147467,0.0001833661,0.000039633844,0.00029788812,0.0007706708,0.0005947558,0.006587046,0.009939548,0.96521986],"study_design_scores_gemma":[0.00020772906,0.0010333043,0.021785168,0.09755198,0.0017155841,0.0010078183,0.0048661483,0.04117461,0.005584563,0.1167295,0.7079034,0.0004400997],"about_ca_topic_score_codex":0.0026457692,"about_ca_topic_score_gemma":0.0027129399,"teacher_disagreement_score":0.020249808,"about_ca_system_score_codex":0.0019586876,"about_ca_system_score_gemma":0.0033881245,"threshold_uncertainty_score":0.10709256},"labels":[],"label_agreement":null},{"id":"W4313343184","doi":"10.1007/978-3-031-23028-8_8","title":"Sentiment Analysis from User Reviews Using a Hybrid Generative-Discriminative HMM-SVM Approach","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Discriminative model; Hidden Markov model; Computer science; Generative grammar; Latent Dirichlet allocation; Artificial intelligence; Support vector machine; Sentiment analysis; Machine learning; Task (project management); Generative model; Context (archaeology); Process (computing); Natural language processing; Topic model; Engineering","score_opus":0.05459002356564102,"score_gpt":0.2907508150165164,"score_spread":0.23616079145087537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313343184","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07859092,0.0010024206,0.911391,0.00028738048,0.0003001531,0.00013632976,0.00093393464,0.00417511,0.0031827022],"genre_scores_gemma":[0.76097614,0.0008418919,0.21735287,0.00022024316,0.00047207938,0.00020792629,0.0042857104,0.00043173425,0.015211434],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994992,0.00011176236,0.00003668181,0.00012926314,0.00013951176,0.00008358282],"domain_scores_gemma":[0.99930036,0.0002441795,0.000049569673,0.00006635408,0.00029624667,0.00004332199],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067442184,0.0008683751,0.0011435454,0.0009771315,0.00040367525,0.00077069516,0.00056856533,0.000693237,0.0025277692],"category_scores_gemma":[0.0012616045,0.00035875235,0.0011880593,0.00090571225,0.00016737929,0.0007437801,0.000610327,0.00086569885,0.004021122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006746767,0.0004043463,0.008328734,0.00027716416,0.00030474048,0.00023853088,0.00016620377,0.032649014,0.097363815,0.0013784361,0.010475584,0.84773874],"study_design_scores_gemma":[0.00001868533,0.00014184845,0.0064152074,0.000017401551,0.00011297168,0.00016290497,0.00005950323,0.9762305,0.013013059,0.0013905717,0.002408625,0.000028644132],"about_ca_topic_score_codex":0.0027127413,"about_ca_topic_score_gemma":0.0047877952,"teacher_disagreement_score":0.0027127413,"about_ca_system_score_codex":0.0002674841,"about_ca_system_score_gemma":0.0004698882,"threshold_uncertainty_score":0.00845623},"labels":[],"label_agreement":null},{"id":"W4313443764","doi":"10.1007/978-981-19-5443-6","title":"Sentiment Analysis and Deep Learning","year":2023,"lang":"en","type":"book","venue":"Advances in intelligent systems and computing","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Sentiment analysis; Deep learning; Computer science; Artificial intelligence; Natural language processing","score_opus":0.01512667283974481,"score_gpt":0.2799872592348246,"score_spread":0.2648605863950798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313443764","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005915777,0.01439272,0.88299054,0.0026306184,0.0015326391,0.00006482436,0.00082377053,0.0026049372,0.08904417],"genre_scores_gemma":[0.14648393,0.021625612,0.4378832,0.0013042733,0.0020807085,0.00021711286,0.0030152423,0.0013078742,0.38608202],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99982774,0.00002103987,0.000009988664,0.000033890483,0.00009032413,0.000016968392],"domain_scores_gemma":[0.99973375,0.00010074275,0.000020253256,0.000037573765,0.000097221004,0.000010415068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034218462,0.00079146633,0.0005865853,0.0007575044,0.000282888,0.0012771192,0.00053427636,0.00056368526,0.011548947],"category_scores_gemma":[0.0011775441,0.00036755242,0.00047270683,0.0011706845,0.0004300868,0.001813419,0.0008405029,0.0014731445,0.0061530764],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039768776,0.00004432654,0.00020396101,0.00021643707,0.00004372547,0.000038806178,0.0000422309,0.011631217,0.005998293,0.077609174,0.07816743,0.8259647],"study_design_scores_gemma":[0.000013213525,0.00004670327,0.0010712862,0.00015665867,0.00005569461,0.00026625255,0.000051891406,0.30302605,0.012592023,0.44641232,0.23626451,0.00004348572],"about_ca_topic_score_codex":0.0010673642,"about_ca_topic_score_gemma":0.0016473542,"teacher_disagreement_score":0.011548947,"about_ca_system_score_codex":0.0005014115,"about_ca_system_score_gemma":0.00035455532,"threshold_uncertainty_score":0.038635015},"labels":[],"label_agreement":null},{"id":"W4315866224","doi":"10.1007/s44196-022-00164-8","title":"WordNet Semantic Relations Based Enhancement of KNN Model for Implicit Aspect Identification in Sentiment Analysis","year":2023,"lang":"en","type":"article","venue":"International Journal of Computational Intelligence Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Overfitting; WordNet; Computer science; Sentiment analysis; Task (project management); Identification (biology); Artificial intelligence; Semantic similarity; Similarity (geometry); Key (lock); Machine learning; Natural language processing; Data mining; Pattern recognition (psychology); Image (mathematics)","score_opus":0.046711505858551786,"score_gpt":0.3515735531804597,"score_spread":0.3048620473219079,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315866224","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20507431,0.00093962066,0.7862301,0.0004594126,0.00021260786,0.00024157058,0.0005265097,0.0014539,0.004861885],"genre_scores_gemma":[0.814131,0.00033209808,0.18179642,0.00020048342,0.00012341347,0.00014330828,0.0011109802,0.00008910467,0.0020731837],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992176,0.00021457332,0.00007599863,0.00020498525,0.0002110316,0.00007567057],"domain_scores_gemma":[0.998868,0.00038657908,0.000114151764,0.0000941379,0.0004944169,0.000042790096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010807378,0.0008572229,0.0007707279,0.0016711983,0.00052554376,0.000844733,0.00089469965,0.00077273557,0.0014689197],"category_scores_gemma":[0.0032517943,0.00021996303,0.0007475541,0.0012485451,0.0003195178,0.0019317882,0.00064026326,0.00079386716,0.0007296208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00074765185,0.00060322526,0.018135527,0.0003273067,0.00029528807,0.0003634042,0.0005554104,0.29675645,0.018085875,0.010937393,0.0075792368,0.6456132],"study_design_scores_gemma":[0.000008468167,0.000034963203,0.0009437243,0.000009645529,0.00002069574,0.00003031775,0.000040482595,0.9939918,0.001275966,0.003035067,0.0006022683,0.0000066314433],"about_ca_topic_score_codex":0.008416877,"about_ca_topic_score_gemma":0.012732444,"teacher_disagreement_score":0.008416877,"about_ca_system_score_codex":0.00067396695,"about_ca_system_score_gemma":0.00071685005,"threshold_uncertainty_score":0.016735792},"labels":[],"label_agreement":null},{"id":"W4316041918","doi":"10.1145/3568231.3568251","title":"Sentiment and Mobility Analysis on COVID-19 Restrictions with Autoencoder","year":2022,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Autoencoder; Coronavirus disease 2019 (COVID-19); Computer science; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Artificial intelligence; Virology; Deep learning; Medicine; Internal medicine","score_opus":0.026673884184880536,"score_gpt":0.2879525193488761,"score_spread":0.2612786351639956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4316041918","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9402806,0.00050148286,0.049606465,0.0006092424,0.00027715848,0.000120581935,0.002109451,0.0008217125,0.005673197],"genre_scores_gemma":[0.97923934,0.00019110176,0.011596498,0.000079653044,0.00005962245,0.00008062656,0.004628811,0.000038311257,0.0040860744],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9996381,0.00006398829,0.000022602537,0.00009085589,0.00008774911,0.00009676652],"domain_scores_gemma":[0.9996507,0.00011711388,0.000037487967,0.000029134773,0.00013648457,0.000029028688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057509553,0.0007157285,0.00047031575,0.0007253371,0.0003200971,0.00040917937,0.00039225427,0.00047016534,0.0018355113],"category_scores_gemma":[0.0013711394,0.00017231652,0.0008266069,0.00063129433,0.00029253968,0.0005680683,0.00047744365,0.00085944333,0.0008312616],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015733946,0.0010793646,0.11547754,0.00045135507,0.00044246303,0.0018606444,0.0012002665,0.28229707,0.04481666,0.0032968086,0.033836,0.5136685],"study_design_scores_gemma":[0.000015370104,0.00009988312,0.03766938,0.000017636437,0.000028094391,0.000081070066,0.00030259354,0.9554905,0.0037321227,0.00057741103,0.0019615067,0.000024406501],"about_ca_topic_score_codex":0.015676394,"about_ca_topic_score_gemma":0.01142954,"teacher_disagreement_score":0.015676394,"about_ca_system_score_codex":0.00061002607,"about_ca_system_score_gemma":0.0004492602,"threshold_uncertainty_score":0.031170249},"labels":[],"label_agreement":null},{"id":"W4316673220","doi":"10.18280/ria.360609","title":"An Adaptive Classification Framework for Handling the Cold Start Problem in Case of News Items","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cold start (automotive); Computer science; Margin (machine learning); Advice (programming); Service (business); Dilemma; Information retrieval; Product (mathematics); Test (biology); Data mining; Machine learning; Engineering; Mathematics","score_opus":0.09959019741652513,"score_gpt":0.32677674838359316,"score_spread":0.22718655096706802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4316673220","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018851314,0.0003538144,0.9777915,0.0004803778,0.00012845398,0.0001800934,0.00012056942,0.0007432202,0.0013506601],"genre_scores_gemma":[0.3871406,0.0003742937,0.6050606,0.0005573082,0.0006553013,0.00038461873,0.00084440986,0.00015110875,0.0048318566],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949233,0.0015132654,0.00045610865,0.0012699001,0.001364074,0.0004733548],"domain_scores_gemma":[0.98952943,0.004167461,0.0010688537,0.00090291334,0.0040130015,0.0003183055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009034873,0.0013261354,0.0022936806,0.0047160005,0.0020406884,0.002595154,0.003864092,0.0030398532,0.0017745796],"category_scores_gemma":[0.013634656,0.00074078265,0.0019288035,0.0030436954,0.001180142,0.0039723907,0.0015087223,0.004366284,0.001214107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064351986,0.0012344992,0.026413977,0.00032714687,0.0005127572,0.0007109369,0.0013745658,0.12294786,0.012259634,0.029447125,0.014379446,0.78974855],"study_design_scores_gemma":[0.000025321686,0.00008302368,0.0019256017,0.000025505688,0.000072048,0.00012505168,0.00010798454,0.9850793,0.0016480562,0.008606701,0.002261784,0.00003969208],"about_ca_topic_score_codex":0.013549461,"about_ca_topic_score_gemma":0.013471386,"teacher_disagreement_score":0.013549461,"about_ca_system_score_codex":0.0014940142,"about_ca_system_score_gemma":0.001822469,"threshold_uncertainty_score":0.047781527},"labels":[],"label_agreement":null},{"id":"W4317387810","doi":"10.18280/mmep.090617","title":"Spam and Sentiment Detection in Arabic Tweets Using MARBERT Model","year":2022,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Imam Abdulrahman Bin Faisal University; Saudi Aramco","keywords":"Sarcasm; Sentiment analysis; Computer science; Social media; Artificial intelligence; Natural language processing; Arabic; Customer satisfaction; Deep learning; Encoder; Recall; World Wide Web; Linguistics; Business; Marketing","score_opus":0.03150540110276414,"score_gpt":0.22079289547952147,"score_spread":0.18928749437675732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317387810","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74403733,0.003010325,0.22061531,0.0038127447,0.00093299017,0.00034832122,0.00188997,0.0071306117,0.018222395],"genre_scores_gemma":[0.95351636,0.0005562588,0.031904574,0.00048439731,0.00023048405,0.000104112136,0.0016144353,0.00010819654,0.011481119],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978095,0.00004393771,0.000014752421,0.000057454756,0.00004938519,0.000053500014],"domain_scores_gemma":[0.99942017,0.00022945079,0.0000569398,0.000029327097,0.00023656731,0.000027515127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057161966,0.0011453212,0.0007245768,0.001362326,0.0006426105,0.0010148097,0.0007121758,0.0011402842,0.0015000034],"category_scores_gemma":[0.0015992944,0.000366574,0.0009749728,0.00041471442,0.00034745742,0.0007164119,0.00043195378,0.0010716573,0.0014164182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013752113,0.0008735825,0.046585366,0.00025171496,0.00044298518,0.001158756,0.0006636795,0.5052527,0.022970214,0.0050481963,0.021857115,0.39352044],"study_design_scores_gemma":[0.0000046022396,0.000022397046,0.00088995724,0.000006597593,0.000015951893,0.000028264014,0.000020440251,0.9964489,0.0015905849,0.00043098402,0.0005349229,0.000006318932],"about_ca_topic_score_codex":0.015951432,"about_ca_topic_score_gemma":0.015879208,"teacher_disagreement_score":0.015951432,"about_ca_system_score_codex":0.0011318006,"about_ca_system_score_gemma":0.00075896905,"threshold_uncertainty_score":0.03171718},"labels":[],"label_agreement":null},{"id":"W4317434601","doi":"10.1145/3580496","title":"Emotional Intelligence Attention Unsupervised Learning Using Lexicon Analysis for Irony-based Advertising","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on Asian and Low-Resource Language Information Processing","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Computer science; Irony; Artificial intelligence; Natural language processing; Classifier (UML); Social media; Machine learning; Lexicon; Word embedding; Embedding; Linguistics; World Wide Web","score_opus":0.019457538019049973,"score_gpt":0.27879410658367926,"score_spread":0.2593365685646293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317434601","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21498524,0.001722895,0.7646698,0.0011297383,0.00024787564,0.00035858987,0.001075168,0.0067851753,0.009025529],"genre_scores_gemma":[0.8691035,0.00039588724,0.115753725,0.00060862256,0.0002698509,0.0003100836,0.0040185493,0.00026820326,0.009271572],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935824,0.00016080403,0.000047892914,0.00022942496,0.000116496994,0.000087081724],"domain_scores_gemma":[0.99864286,0.00079800835,0.00009517662,0.0001343718,0.0002786464,0.000050942283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085989595,0.0011784581,0.0009607135,0.0020136337,0.00062758656,0.0011555721,0.0013988741,0.0010670014,0.0023085559],"category_scores_gemma":[0.0034484053,0.00054109487,0.001236955,0.0013246937,0.00065990887,0.0015221649,0.0011503838,0.0018609592,0.0010242693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004148555,0.0008430748,0.0063109766,0.00021716184,0.00027057537,0.00042644472,0.00038276016,0.17447418,0.014549912,0.006223287,0.016435698,0.77945113],"study_design_scores_gemma":[0.000018120541,0.000034129036,0.00092686823,0.000007965191,0.000028817254,0.00003512842,0.000031891832,0.99149656,0.0017073791,0.004795455,0.0009063827,0.000011289424],"about_ca_topic_score_codex":0.008736108,"about_ca_topic_score_gemma":0.012114542,"teacher_disagreement_score":0.008736108,"about_ca_system_score_codex":0.0011769588,"about_ca_system_score_gemma":0.0009190658,"threshold_uncertainty_score":0.017370522},"labels":[],"label_agreement":null},{"id":"W4317515583","doi":"10.1109/ccis57298.2022.10016405","title":"A Joint Learning Sentiment Analysis Method Incorporating Emoji-Augmentation","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Chizhou University; National Natural Science Foundation of China","keywords":"Emoji; Computer science; Sentiment analysis; Artificial intelligence; Semantics (computer science); Social media; Natural language processing; Sentence; Feeling; Joint (building); Microblogging; World Wide Web; Psychology","score_opus":0.06592982217907196,"score_gpt":0.33671435738703265,"score_spread":0.2707845352079607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317515583","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05961727,0.00083253643,0.9273911,0.0006651056,0.000705539,0.00027454662,0.00065665133,0.0049893674,0.0048678406],"genre_scores_gemma":[0.43293926,0.00075879844,0.5428594,0.0009152123,0.00067276455,0.00065716996,0.0039254245,0.00044440312,0.016827682],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996344,0.000072976785,0.000030117913,0.0001286497,0.0000920102,0.000041816656],"domain_scores_gemma":[0.99948406,0.00011015124,0.00004401167,0.000050803472,0.00027589843,0.000035039975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008056421,0.0017273,0.0007751022,0.0009394738,0.00044112233,0.0005428926,0.0007197658,0.0008572413,0.0018201795],"category_scores_gemma":[0.0014810727,0.0003467336,0.0012343298,0.00059341086,0.00027780308,0.0012403977,0.0006579088,0.0014008319,0.0014784688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047887696,0.00064610207,0.004122227,0.00026328978,0.0002816528,0.00019203575,0.000283505,0.020002142,0.11286323,0.002600565,0.027343964,0.8309224],"study_design_scores_gemma":[0.00005644252,0.00018988154,0.002714311,0.000028587778,0.0001319744,0.00012355839,0.00008169944,0.96231526,0.025084548,0.0024644334,0.00676585,0.000043448632],"about_ca_topic_score_codex":0.0016696221,"about_ca_topic_score_gemma":0.0033855734,"teacher_disagreement_score":0.0018201795,"about_ca_system_score_codex":0.00035092756,"about_ca_system_score_gemma":0.0006492386,"threshold_uncertainty_score":0.0060890913},"labels":[],"label_agreement":null},{"id":"W4321392937","doi":"10.48550/arxiv.2302.08956","title":"AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"DeepMind; Universität Hamburg; International Development Research Centre; Rockefeller Foundation","keywords":"Amharic; Computer science; Arabic languages; Natural language processing; Task (project management); Languages of Africa; Artificial intelligence; Portuguese; Annotation; Benchmark (surveying); Linguistics; Arabic; World Wide Web; Geography","score_opus":0.11001768137149925,"score_gpt":0.23750845139386825,"score_spread":0.127490770022369,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321392937","genre_codex":"dataset","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3858828,0.0029713875,0.01690552,0.003962753,0.0016071429,0.0016966321,0.51651406,0.01602886,0.05443099],"genre_scores_gemma":[0.26487073,0.0008588477,0.044933196,0.0008045139,0.00038012656,0.0016709691,0.67528623,0.0010660059,0.010129345],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986473,0.00040813614,0.00017930909,0.00020683352,0.00039695963,0.00016158709],"domain_scores_gemma":[0.9982008,0.0005544745,0.00019886074,0.00024458003,0.00060853007,0.0001927575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015608349,0.001413476,0.00046826425,0.0036836786,0.0019140628,0.0011514538,0.0008755232,0.0010418713,0.003787796],"category_scores_gemma":[0.00524043,0.00020617916,0.0006607569,0.0027109976,0.00033659372,0.0020271847,0.0017302147,0.00083258946,0.0034158619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015937514,0.000752977,0.031241545,0.00316697,0.000324846,0.0009572536,0.002840386,0.0069397897,0.031041443,0.0053706435,0.75008774,0.16568266],"study_design_scores_gemma":[0.0003969555,0.00057420024,0.124415606,0.0005456691,0.00017183527,0.001189251,0.0072809053,0.11985476,0.03233981,0.007840401,0.70514274,0.00024785302],"about_ca_topic_score_codex":0.009234749,"about_ca_topic_score_gemma":0.019085256,"teacher_disagreement_score":0.009234749,"about_ca_system_score_codex":0.0009862018,"about_ca_system_score_gemma":0.0008421363,"threshold_uncertainty_score":0.018361986},"labels":[],"label_agreement":null},{"id":"W4321780137","doi":"10.1109/access.2023.3248506","title":"Emotion Detection From Micro-Blogs Using Novel Input Representation","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Sentiment analysis; Artificial intelligence; Social media; Classifier (UML); Representation (politics); Emotion detection; Voting; Feature extraction; Natural language processing; Machine learning; Microblogging; Emotion recognition; World Wide Web","score_opus":0.12072132127598183,"score_gpt":0.3658846158583213,"score_spread":0.24516329458233949,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321780137","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40633643,0.00056366774,0.5796804,0.0005557487,0.0004461437,0.00028270672,0.0021886034,0.0040886565,0.0058576157],"genre_scores_gemma":[0.8715191,0.00022348917,0.122956425,0.00010041104,0.000115606126,0.00022423147,0.0021466522,0.00007380997,0.0026403079],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997067,0.00005745355,0.000022790502,0.00007340913,0.000092450246,0.000047183086],"domain_scores_gemma":[0.9994956,0.00019355428,0.00005975926,0.00006000505,0.00017347677,0.000017478726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032578537,0.00073982985,0.00041757268,0.00089542114,0.00026397145,0.00073700986,0.000395138,0.0005551785,0.001087347],"category_scores_gemma":[0.0014976826,0.000086577544,0.00044722378,0.0006542061,0.00016646362,0.0006902974,0.00045215292,0.00037799487,0.00062196964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010130078,0.000558234,0.01620771,0.0003067099,0.00013109259,0.00092247257,0.00046594627,0.042336714,0.11614049,0.0018330272,0.008888591,0.8111959],"study_design_scores_gemma":[0.000023416038,0.00021299766,0.012831269,0.000029253333,0.000060065922,0.00032496965,0.00033636476,0.9400961,0.039596744,0.002494186,0.003965595,0.000029100323],"about_ca_topic_score_codex":0.00066866283,"about_ca_topic_score_gemma":0.00083080155,"teacher_disagreement_score":0.001087347,"about_ca_system_score_codex":0.00022840996,"about_ca_system_score_gemma":0.00016192024,"threshold_uncertainty_score":0.0036374927},"labels":[],"label_agreement":null},{"id":"W4321843579","doi":"10.1109/icatiece56365.2022.10046928","title":"Behavioral Study of Customer Using Deep Learning Techniques","year":2022,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Manitoba Hydro","funders":"","keywords":"Pessimism; Purchasing; Audit; Computer science; Order (exchange); Feeling; The Internet; Task (project management); Data science; Knowledge management; World Wide Web; Marketing; Psychology; Business; Management; Social psychology","score_opus":0.04846380992163602,"score_gpt":0.33302508355560023,"score_spread":0.2845612736339642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321843579","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9908229,0.0000510951,0.0030182775,0.00025872,0.00001613274,0.000046654615,0.00059041596,0.000046116133,0.00514969],"genre_scores_gemma":[0.99185055,0.000083022496,0.0025097332,0.00014021923,0.00001404539,0.000051078674,0.0005589245,0.000008710833,0.0047836173],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995757,0.00014532216,0.000020759688,0.00006528735,0.00012693575,0.000066071116],"domain_scores_gemma":[0.9984596,0.0007442872,0.00013708616,0.000076361524,0.00045102462,0.00013170467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006827042,0.0001742294,0.00019940698,0.00052365987,0.00030906443,0.00055774424,0.00027158513,0.00038628135,0.003723578],"category_scores_gemma":[0.0025258462,0.00008529524,0.00020683797,0.000618911,0.00015443684,0.00043658572,0.0002595888,0.0006734873,0.00097407255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006132369,0.003772727,0.7632706,0.00020386359,0.00013754089,0.00051741325,0.007827474,0.006373874,0.011892342,0.0020482433,0.011398138,0.19194457],"study_design_scores_gemma":[0.000020928062,0.0025200527,0.85691077,0.00006818901,0.0000724541,0.0004835489,0.012752185,0.11357176,0.005556478,0.0013988749,0.0065727904,0.000071929004],"about_ca_topic_score_codex":0.004761672,"about_ca_topic_score_gemma":0.006165701,"teacher_disagreement_score":0.004761672,"about_ca_system_score_codex":0.00032519546,"about_ca_system_score_gemma":0.00024237653,"threshold_uncertainty_score":0.0124566555},"labels":[],"label_agreement":null},{"id":"W4322096737","doi":"10.1007/978-3-031-24337-0_14","title":"Contrastive Reasons Detection and Clustering from Online Polarized Debates","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Automatic summarization; Cluster analysis; Pipeline (software); Viewpoints; Relevance (law); Natural language processing; Artificial intelligence; Phrase; Argument (complex analysis); Contrastive analysis; Information retrieval; Linguistics","score_opus":0.021962704253942006,"score_gpt":0.2534200535667218,"score_spread":0.2314573493127798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322096737","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47943273,0.003234846,0.48254862,0.0011759682,0.0006384337,0.0005123935,0.005731537,0.0034583248,0.02326717],"genre_scores_gemma":[0.81804883,0.00047014796,0.16138722,0.00012287908,0.0004998321,0.00023546851,0.010318663,0.00030690565,0.008610145],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850094,0.00035291183,0.00010254406,0.0003693287,0.0004266529,0.00024768198],"domain_scores_gemma":[0.99670726,0.0016241232,0.00025743915,0.00033432676,0.0009202377,0.00015658211],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013174709,0.00072487927,0.0008182536,0.0051061637,0.0012262699,0.0023884054,0.0010035489,0.00093198795,0.0031838862],"category_scores_gemma":[0.006246049,0.00028563745,0.0009929158,0.0032418561,0.00046059606,0.0017318174,0.0014085431,0.0012683577,0.0027695193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018008561,0.00044772786,0.031096503,0.0005418939,0.00029852652,0.0005728264,0.0022239992,0.007982811,0.050842375,0.018026307,0.029277906,0.8568883],"study_design_scores_gemma":[0.00009493251,0.00027713014,0.050140336,0.0001980438,0.0003923819,0.00078554364,0.004424574,0.80391663,0.05204874,0.046481814,0.0411252,0.00011470426],"about_ca_topic_score_codex":0.0020730156,"about_ca_topic_score_gemma":0.0040673725,"teacher_disagreement_score":0.0051061637,"about_ca_system_score_codex":0.00060075795,"about_ca_system_score_gemma":0.0007514306,"threshold_uncertainty_score":0.010651112},"labels":[],"label_agreement":null},{"id":"W4322735366","doi":"10.1117/12.2667211","title":"Sentiment analysis of 2021 Canadian election tweets","year":2023,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sarcasm; Sentiment analysis; Computer science; Natural language processing; Context (archaeology); Anger; Meaning (existential); Tone (literature); Artificial intelligence; Social media; Linguistics; Psychology; Social psychology; World Wide Web; Irony","score_opus":0.016862417885077745,"score_gpt":0.2663090057576938,"score_spread":0.24944658787261603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322735366","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6206367,0.0014780095,0.0019921956,0.0031599097,0.0015112006,0.00050855736,0.27497754,0.00071744074,0.095018506],"genre_scores_gemma":[0.8003996,0.0014445196,0.0051785004,0.00053054973,0.0005657667,0.00028253486,0.14448474,0.00016748357,0.04694629],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9990792,0.0000461897,0.000041722666,0.00007931227,0.00057675625,0.00017673221],"domain_scores_gemma":[0.9972692,0.00029555825,0.00014409621,0.00005025192,0.0020640998,0.00017685437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006068537,0.00042336728,0.00024199623,0.0034726223,0.0021476655,0.0011462796,0.0002691066,0.00029063845,0.004695962],"category_scores_gemma":[0.0026855592,0.00009990745,0.00024329108,0.0050475257,0.000284826,0.00027183202,0.00030094958,0.00045927943,0.0015611886],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014455974,0.00019786834,0.17798084,0.0010566266,0.000202685,0.0013657799,0.004070418,0.0032128016,0.023087785,0.0040276335,0.6026978,0.18065418],"study_design_scores_gemma":[0.000032818436,0.00008526706,0.62305135,0.00011330089,0.000103249724,0.0002085111,0.0046317135,0.008978315,0.006494377,0.0003521376,0.35586312,0.000085885586],"about_ca_topic_score_codex":0.71693605,"about_ca_topic_score_gemma":0.87113893,"teacher_disagreement_score":0.28306395,"about_ca_system_score_codex":0.00576385,"about_ca_system_score_gemma":0.005423123,"threshold_uncertainty_score":0.56946194},"labels":[],"label_agreement":null},{"id":"W4322749757","doi":"10.2139/ssrn.4374979","title":"An Emotion-Aware Recommendation System Using Deep Learning","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Privy Council Office","funders":"","keywords":"Computer science; Deep learning; Recommender system; Artificial intelligence; Psychology; Cognitive psychology; World Wide Web","score_opus":0.034425036795592286,"score_gpt":0.3033122297864884,"score_spread":0.26888719299089614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322749757","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20005211,0.002638425,0.725928,0.002127583,0.0013469319,0.0005873554,0.007475256,0.045145627,0.014698632],"genre_scores_gemma":[0.5661284,0.0008607857,0.38489336,0.0010727936,0.00041959228,0.0002262501,0.008697219,0.0005612174,0.03714034],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996823,0.000035719695,0.000024547937,0.00009318417,0.00011207478,0.000052292624],"domain_scores_gemma":[0.9993637,0.0001218714,0.000034254455,0.00010969781,0.0003092701,0.00006112529],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045210682,0.00069221546,0.00080695667,0.0008760698,0.00045388652,0.000781731,0.0011358454,0.0010169158,0.0042349584],"category_scores_gemma":[0.0013469389,0.00036542522,0.0005360076,0.00085298106,0.000096571384,0.0011586926,0.0005602548,0.0010275793,0.004426766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001120075,0.0013070151,0.0064253733,0.00021933446,0.00037050285,0.0002910775,0.00008592358,0.010504931,0.0744636,0.0013114025,0.06972712,0.83417356],"study_design_scores_gemma":[0.00016355894,0.0003203181,0.0047669807,0.000028797385,0.00021919713,0.00022491522,0.000057837093,0.9431753,0.03732107,0.0028996826,0.010757207,0.00006513674],"about_ca_topic_score_codex":0.011828254,"about_ca_topic_score_gemma":0.027325159,"teacher_disagreement_score":0.011828254,"about_ca_system_score_codex":0.00041110383,"about_ca_system_score_gemma":0.0004982387,"threshold_uncertainty_score":0.0235188},"labels":[],"label_agreement":null},{"id":"W4323066598","doi":"10.48550/arxiv.2303.00923","title":"On the Role of Reviewer Expertise in Temporal Review Helpfulness Prediction","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Helpfulness; Scarcity; Computer science; Quality (philosophy); Data science; Value (mathematics); Psychology; Economics; Machine learning; Social psychology","score_opus":0.10803291657150864,"score_gpt":0.21487298442205685,"score_spread":0.10684006785054821,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323066598","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82624424,0.021399107,0.1233141,0.004387442,0.0010305059,0.00044299933,0.011212149,0.0027238457,0.009245675],"genre_scores_gemma":[0.94442385,0.001674585,0.04071106,0.0003754242,0.0010392163,0.00014723248,0.008730457,0.00012504208,0.0027731021],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9955526,0.0018177483,0.0003935365,0.0012853038,0.00072671,0.00022409717],"domain_scores_gemma":[0.9400225,0.042530984,0.006759894,0.0022361458,0.0070364014,0.0014140229],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.010627443,0.0012585735,0.0011148937,0.006982117,0.001055194,0.0022206868,0.001147137,0.0018424542,0.0010059351],"category_scores_gemma":[0.040119085,0.0004180231,0.00076270953,0.0033536907,0.0005646486,0.0031286057,0.0008060318,0.0014034068,0.0012408054],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015002956,0.0007273009,0.5211627,0.0014015054,0.000607787,0.00086182094,0.001535899,0.043548994,0.012882038,0.003460672,0.0694969,0.34281406],"study_design_scores_gemma":[0.00013561903,0.000347049,0.09130769,0.00019542687,0.00025883928,0.0013294477,0.0003796152,0.87413657,0.0075968374,0.006639034,0.017548744,0.00012510957],"about_ca_topic_score_codex":0.0080665685,"about_ca_topic_score_gemma":0.020307412,"teacher_disagreement_score":0.98937255,"about_ca_system_score_codex":0.0012974772,"about_ca_system_score_gemma":0.0014843711,"threshold_uncertainty_score":0.05620402},"labels":[],"label_agreement":null},{"id":"W4323349663","doi":"10.1007/s00500-023-07956-w","title":"Class-biased sarcasm detection using BiLSTM variational autoencoder-based synthetic oversampling","year":2023,"lang":"en","type":"article","venue":"Soft Computing","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Sarcasm; Autoencoder; Oversampling; Artificial intelligence; Computer science; Classifier (UML); Machine learning; Pattern recognition (psychology); Artificial neural network; Irony; Bandwidth (computing)","score_opus":0.051286489356840305,"score_gpt":0.298834077493921,"score_spread":0.2475475881370807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323349663","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.557805,0.0006828456,0.43544802,0.00041644377,0.00040585003,0.00008072593,0.00044181646,0.0011524048,0.0035669291],"genre_scores_gemma":[0.9518293,0.000096651216,0.04388312,0.00012401956,0.0000799178,0.00004814398,0.0011480938,0.000089858026,0.0027009002],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966276,0.00010657175,0.000013582949,0.000083448576,0.00008502851,0.000048608323],"domain_scores_gemma":[0.9991184,0.0003537661,0.00006385746,0.00008931115,0.0003297601,0.000044920886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010224364,0.00042439197,0.0004977147,0.00038671374,0.00028823497,0.00039070178,0.00057340803,0.0005959603,0.0010426958],"category_scores_gemma":[0.0026517033,0.00016320881,0.00032842957,0.00025352061,0.00026956806,0.0005003254,0.0005367545,0.00074054376,0.00042366964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013018791,0.0007972135,0.021667503,0.0002679886,0.00029147428,0.00039953098,0.0006913432,0.23479201,0.11783824,0.008561618,0.018980889,0.5944103],"study_design_scores_gemma":[0.000007579397,0.000037511378,0.0018659894,0.0000049471146,0.000012007299,0.00003226801,0.00002577237,0.9930248,0.0037579595,0.0006879852,0.0005372114,0.0000059376584],"about_ca_topic_score_codex":0.002775675,"about_ca_topic_score_gemma":0.005197609,"teacher_disagreement_score":0.002775675,"about_ca_system_score_codex":0.00032438143,"about_ca_system_score_gemma":0.00038839068,"threshold_uncertainty_score":0.0055190325},"labels":[],"label_agreement":null},{"id":"W4323654614","doi":"10.18280/isi.280107","title":"A Deep Neural Network Optimized by a Genetic Algorithm to Improve Arabic Sentiment Classification","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Arabic; Computer science; Artificial neural network; Genetic algorithm; Artificial intelligence; Deep neural networks; Machine learning; Natural language processing; Linguistics","score_opus":0.015063144995178907,"score_gpt":0.2425544151953265,"score_spread":0.2274912702001476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323654614","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11700191,0.0005460678,0.873559,0.0003504905,0.00027318962,0.00018281327,0.00010794679,0.0016151072,0.0063633905],"genre_scores_gemma":[0.6342312,0.0003446287,0.3588829,0.00024319264,0.00005460192,0.00024639064,0.00028987176,0.00009489707,0.0056122877],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998425,0.000028079525,0.000011305472,0.0000418997,0.00004881582,0.0000272455],"domain_scores_gemma":[0.99977547,0.00005732669,0.000023197668,0.000011113753,0.00012191115,0.000010951033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050158624,0.00082845526,0.0005143459,0.0007125771,0.00037528688,0.00048419007,0.0006159921,0.00076377287,0.0012433887],"category_scores_gemma":[0.0010552162,0.00027108303,0.00050513697,0.00055460836,0.00027017065,0.00055618957,0.0003666813,0.00066972215,0.0003192544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001841826,0.00019800938,0.0023008,0.0001442219,0.000117948155,0.00019091272,0.00011511419,0.5771395,0.037073407,0.004076588,0.0032571643,0.37520218],"study_design_scores_gemma":[0.000008869577,0.00003334904,0.0001720135,0.000004249456,0.00001041183,0.000014009806,0.000005483678,0.996619,0.0024183935,0.00032501676,0.00038482578,0.000004379823],"about_ca_topic_score_codex":0.00807956,"about_ca_topic_score_gemma":0.007175306,"teacher_disagreement_score":0.00807956,"about_ca_system_score_codex":0.0008772443,"about_ca_system_score_gemma":0.000882001,"threshold_uncertainty_score":0.016065061},"labels":[],"label_agreement":null},{"id":"W4324137328","doi":"10.1109/icssit55814.2023.10060993","title":"Multilingual Sentiment Analysis using Deep-Learning Architectures","year":2023,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trent University","funders":"","keywords":"Sentiment analysis; Automatic summarization; Computer science; Social media; Artificial intelligence; Machine translation; Python (programming language); Natural language processing; Mindset; World Wide Web; Data science","score_opus":0.02908713870469018,"score_gpt":0.3125370148480503,"score_spread":0.28344987614336015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4324137328","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14579338,0.00088244287,0.8247908,0.0012636747,0.00046972412,0.0001902137,0.0015653187,0.009480543,0.015563861],"genre_scores_gemma":[0.79656404,0.00042314772,0.18248917,0.0003962388,0.00015311399,0.0001680732,0.0035871437,0.00032465806,0.015894443],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996735,0.00007008217,0.000021423994,0.00007542541,0.00006988842,0.0000897563],"domain_scores_gemma":[0.99956554,0.00007734303,0.00003732774,0.00003947524,0.00025341427,0.00002691379],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072641583,0.0009865124,0.00043360319,0.000857463,0.0005115679,0.0010861839,0.0007592132,0.0006949847,0.0061412547],"category_scores_gemma":[0.0014462391,0.00037276046,0.00091054564,0.0006582839,0.0002457455,0.0013474162,0.0010390878,0.0012517155,0.00277823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052280264,0.00059990777,0.0073758326,0.0001930928,0.00035857846,0.0003602147,0.00026028368,0.17070667,0.037121348,0.008668467,0.021006847,0.7528259],"study_design_scores_gemma":[0.00000897394,0.000046086454,0.0007990568,0.000013181156,0.00002358164,0.000020028698,0.00006248307,0.9862365,0.0047365646,0.005725426,0.002317755,0.00001035982],"about_ca_topic_score_codex":0.008780465,"about_ca_topic_score_gemma":0.012747606,"teacher_disagreement_score":0.008780465,"about_ca_system_score_codex":0.0009535636,"about_ca_system_score_gemma":0.000930547,"threshold_uncertainty_score":0.020544529},"labels":[],"label_agreement":null},{"id":"W4324378562","doi":"10.1186/s40537-023-00710-x","title":"A semi-supervised short text sentiment classification method based on improved Bert model from unlabelled data","year":2023,"lang":"en","type":"article","venue":"Journal Of Big Data","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Bottleneck; Sentiment analysis; Artificial intelligence; Semi-supervised learning; Machine learning; Big data; Supervised learning; Language model; Function (biology); Natural language processing; Data mining; Artificial neural network","score_opus":0.2707046506402733,"score_gpt":0.37505771781517383,"score_spread":0.1043530671749005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4324378562","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14525922,0.0006761482,0.8440723,0.00066956045,0.0003192094,0.00025846148,0.0005136308,0.0036159216,0.0046155127],"genre_scores_gemma":[0.8554309,0.00035954767,0.12950148,0.0004128112,0.00029505196,0.00032563784,0.0024866501,0.0001778676,0.0110100005],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994112,0.00009987212,0.00004396329,0.00017269031,0.00019020044,0.00008214654],"domain_scores_gemma":[0.9992041,0.00017924147,0.000068843125,0.000071048635,0.0004241559,0.00005256534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096342806,0.0010571536,0.00095850544,0.0011729643,0.00060588884,0.00080974895,0.0017874517,0.0009995655,0.0022271136],"category_scores_gemma":[0.001627171,0.00038874647,0.0009008281,0.00070970255,0.00039292383,0.0015838176,0.00071886333,0.0010350178,0.0011780838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008885717,0.0005879161,0.008885298,0.00020207227,0.00015217328,0.00033863014,0.000261733,0.23255147,0.032029625,0.0037315523,0.013462832,0.70690805],"study_design_scores_gemma":[0.000005994061,0.000026422938,0.0003550702,0.0000033078431,0.0000074905765,0.000014056503,0.000009585263,0.9974112,0.0014531016,0.00041740554,0.0002907176,0.000005699204],"about_ca_topic_score_codex":0.0063932897,"about_ca_topic_score_gemma":0.0074463394,"teacher_disagreement_score":0.0063932897,"about_ca_system_score_codex":0.00079424697,"about_ca_system_score_gemma":0.00088634394,"threshold_uncertainty_score":0.012712121},"labels":[],"label_agreement":null},{"id":"W4327641201","doi":"10.1109/cse57773.2022.00018","title":"To Mask or Not To Mask? A Machine Learning Approach to Covid News Coverage Attitude Prediction Based on Time Series and Text Content","year":2022,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; University of Waterloo; Lakehead University","funders":"","keywords":"Computer science; Coronavirus disease 2019 (COVID-19); Artificial intelligence; Time series; Focus (optics); Deep learning; Term (time); Machine learning; Information retrieval; Natural language processing","score_opus":0.04002306504520544,"score_gpt":0.25480797809263483,"score_spread":0.2147849130474294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327641201","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5013196,0.004152323,0.47392493,0.006486641,0.0005101111,0.00023412371,0.0030751172,0.0028344332,0.0074628],"genre_scores_gemma":[0.9332184,0.0005965023,0.060893267,0.0004007944,0.00040478047,0.00008143074,0.0018306628,0.00003742206,0.0025367346],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996661,0.00007066459,0.000030046916,0.000120820434,0.00006260894,0.000049793533],"domain_scores_gemma":[0.9990146,0.00051126786,0.00015909733,0.00004726225,0.0002136434,0.000054077304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075280236,0.0007634506,0.00059151975,0.0018142237,0.0003562591,0.0006742914,0.0008321417,0.0009165263,0.00093266665],"category_scores_gemma":[0.002788389,0.00021275769,0.00041335117,0.00122015,0.00030100092,0.0011203858,0.00033087019,0.0013654643,0.00057132955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066622574,0.0009870919,0.04509017,0.00028383578,0.0002257136,0.00036891722,0.00043105567,0.10591957,0.010509919,0.0030668841,0.014929499,0.8175212],"study_design_scores_gemma":[0.000013175891,0.00009827091,0.0047464543,0.000021123999,0.000030645646,0.000057881167,0.00008403592,0.9896416,0.0019135493,0.0022734462,0.0011037389,0.000016104843],"about_ca_topic_score_codex":0.0070006824,"about_ca_topic_score_gemma":0.0076181744,"teacher_disagreement_score":0.0070006824,"about_ca_system_score_codex":0.0005665972,"about_ca_system_score_gemma":0.0004757477,"threshold_uncertainty_score":0.01391989},"labels":[],"label_agreement":null},{"id":"W4328055046","doi":"10.1016/j.ins.2023.03.102","title":"Store, share and transfer: Learning and updating sentiment knowledge for aspect-based sentiment analysis","year":2023,"lang":"en","type":"article","venue":"Information Sciences","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"National Natural Science Foundation of China","keywords":"Computer science; Sentiment analysis; Sentence; Natural language processing; Artificial intelligence; Graph; Context (archaeology); Dependency (UML); Domain knowledge; Theoretical computer science","score_opus":0.03226027747906481,"score_gpt":0.31461164892717625,"score_spread":0.28235137144811145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4328055046","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.104311995,0.0006026342,0.8589705,0.00079590967,0.000511193,0.00070096116,0.0017024693,0.028234996,0.004169398],"genre_scores_gemma":[0.37360796,0.00045012872,0.6146602,0.00030358188,0.000241483,0.0004094593,0.0035944146,0.00079992745,0.005932951],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991574,0.00017832572,0.00007126701,0.00026694653,0.00023907877,0.00008699425],"domain_scores_gemma":[0.99730104,0.0012140148,0.0002122296,0.00054474856,0.00059223885,0.0001356677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025099313,0.0016004331,0.0009422389,0.001867942,0.0006899323,0.0018838869,0.0018940368,0.0011586926,0.005249326],"category_scores_gemma":[0.008489119,0.00065517414,0.0009900687,0.0017820632,0.00048361113,0.00485439,0.002272694,0.0019852822,0.003304966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040681186,0.00042674522,0.0029029967,0.00011816814,0.00012771272,0.000078855795,0.00034938482,0.006991743,0.011096414,0.0013632686,0.015375264,0.9607627],"study_design_scores_gemma":[0.00014426585,0.00026445376,0.0029476988,0.000032363885,0.00019325776,0.000104688836,0.0003551,0.9496488,0.021175904,0.016369086,0.008703682,0.000060638667],"about_ca_topic_score_codex":0.0044111037,"about_ca_topic_score_gemma":0.009043433,"teacher_disagreement_score":0.005249326,"about_ca_system_score_codex":0.0007229563,"about_ca_system_score_gemma":0.0010562863,"threshold_uncertainty_score":0.01756072},"labels":[],"label_agreement":null},{"id":"W4360989124","doi":"10.18280/ria.370101","title":"A Combined Approach of Sentimental Analysis Using Machine Learning Techniques","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sentiment analysis; Computer science; Lexical analysis; Punctuation; Naive Bayes classifier; Support vector machine; Artificial intelligence; Machine learning; tf–idf; Random forest; Stop words; Feature selection; Information retrieval; Word (group theory); Feature (linguistics); Natural language processing; Data science; Data mining; Preprocessor","score_opus":0.05717035176694336,"score_gpt":0.30264272209805704,"score_spread":0.2454723703311137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360989124","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019932853,0.0019920522,0.96099985,0.00072747516,0.00061329344,0.0008667956,0.0011409057,0.004340694,0.009386093],"genre_scores_gemma":[0.15906239,0.0015321806,0.82817894,0.00034532108,0.00062472053,0.00061335403,0.00195912,0.00034812107,0.0073358337],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963408,0.00095888216,0.00039774665,0.0005954817,0.0015231398,0.00018388638],"domain_scores_gemma":[0.9968178,0.0008187491,0.00026757474,0.00027743698,0.001742766,0.00007560468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029451626,0.0018938179,0.0022283483,0.006833456,0.000661641,0.0030742486,0.0012137272,0.0011593314,0.004152687],"category_scores_gemma":[0.005594735,0.0005224389,0.0020626022,0.004393519,0.00037199756,0.0029658657,0.0012962698,0.0013423241,0.0046822955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016220227,0.00033135558,0.0040887073,0.000601668,0.000398326,0.00014554063,0.00026788123,0.0077481964,0.01838784,0.002600903,0.009551518,0.9557159],"study_design_scores_gemma":[0.00006682213,0.0006324695,0.017631838,0.00027499264,0.00047459124,0.00048591447,0.00096616644,0.8790644,0.028463678,0.023260796,0.048419077,0.00025934496],"about_ca_topic_score_codex":0.0013204921,"about_ca_topic_score_gemma":0.0022544488,"teacher_disagreement_score":0.006833456,"about_ca_system_score_codex":0.00058053836,"about_ca_system_score_gemma":0.00087323575,"threshold_uncertainty_score":0.015575707},"labels":[],"label_agreement":null},{"id":"W4361732754","doi":"10.1109/netcit57419.2022.00026","title":"Deep Learning Approaches in Sentiment Analysis","year":2022,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Sentiment analysis; Microblogging; Computer science; Social media; Data science; Public opinion; Event (particle physics); Product (mathematics); Cover (algebra); Tracking (education); Information retrieval; Artificial intelligence; World Wide Web; Psychology; Political science","score_opus":0.041279857813538996,"score_gpt":0.2503870853124504,"score_spread":0.20910722749891142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361732754","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009850658,0.012098825,0.9630304,0.002401524,0.00046489958,0.000083541614,0.00052402454,0.00071889954,0.0108272005],"genre_scores_gemma":[0.49640307,0.031056998,0.433728,0.0019825012,0.0026345528,0.0005453948,0.0034822496,0.00042920784,0.029738074],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994246,0.0001817812,0.000047863992,0.000102837024,0.00016980899,0.00007318105],"domain_scores_gemma":[0.99918133,0.0003738693,0.000072984345,0.00006453124,0.00027196485,0.000035328347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015107163,0.001138643,0.00092866004,0.0014720057,0.0004303033,0.0016686909,0.0011858885,0.0009812126,0.0034622948],"category_scores_gemma":[0.0029945923,0.0004569246,0.00095273304,0.0016696857,0.0005591697,0.0017168656,0.0012557912,0.0022082878,0.0020736202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000097512464,0.00016978197,0.00293367,0.00066746876,0.00037413678,0.00013501414,0.0002716764,0.1476858,0.004182326,0.079955526,0.029565714,0.73396134],"study_design_scores_gemma":[0.0000124370645,0.000031250172,0.00075871975,0.000091781505,0.000040305542,0.000030171599,0.000054184406,0.894066,0.0010040366,0.0914635,0.012432073,0.000015549756],"about_ca_topic_score_codex":0.0042033037,"about_ca_topic_score_gemma":0.005019485,"teacher_disagreement_score":0.0042033037,"about_ca_system_score_codex":0.000866577,"about_ca_system_score_gemma":0.00082411844,"threshold_uncertainty_score":0.011582494},"labels":[],"label_agreement":null},{"id":"W4362575691","doi":"10.22215/etd/2023-15412","title":"A Comprehensive Solution to Predict Short-term and Long-term user Intention with Environmental Context","year":2023,"lang":"en","type":"dissertation","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Term (time); Computer science; Polarity (international relations); Context (archaeology); Relation (database); Sentiment analysis; Baseline (sea); Task (project management); Graph; Artificial intelligence; Artificial neural network; Machine learning; Data mining; Theoretical computer science; Engineering","score_opus":0.024078653212673076,"score_gpt":0.2696788039437606,"score_spread":0.2456001507310875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362575691","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32308802,0.00071171025,0.6667515,0.00059018837,0.00007798511,0.00023600119,0.0018210697,0.0026588498,0.004064668],"genre_scores_gemma":[0.8525581,0.0002391028,0.14169596,0.000115315226,0.00006908815,0.00016113557,0.0024058637,0.000053814576,0.0027015603],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975854,0.000049878177,0.000016973934,0.000096417796,0.000045344488,0.000032971046],"domain_scores_gemma":[0.99939835,0.00016640067,0.00006750642,0.000057023113,0.0002679154,0.000042689688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051001116,0.00091535767,0.00057506096,0.0011903635,0.00027697795,0.000494915,0.0005247846,0.00075371616,0.0010470389],"category_scores_gemma":[0.0012592473,0.00036505418,0.0006499583,0.0009434439,0.00012849823,0.0012623348,0.0005036888,0.00070807984,0.0007155962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047651606,0.0010906333,0.09529465,0.00023749794,0.00055044447,0.00024727138,0.0002255448,0.1914263,0.022523433,0.00224319,0.006222182,0.6794623],"study_design_scores_gemma":[0.000005238965,0.00008928365,0.01004617,0.000007873887,0.000037123955,0.000021924096,0.0000334499,0.98710644,0.0010814605,0.0011654298,0.0003948169,0.0000107903525],"about_ca_topic_score_codex":0.009390203,"about_ca_topic_score_gemma":0.020073352,"teacher_disagreement_score":0.009390203,"about_ca_system_score_codex":0.00033703246,"about_ca_system_score_gemma":0.0005816336,"threshold_uncertainty_score":0.018671095},"labels":[],"label_agreement":null},{"id":"W4362575702","doi":"10.22215/etd/2023-15400","title":"Empirical Study on Improving Hate Speech Detection: Novel BERT based One-Versus-All Classification Approach (BOVAC) with a Novel Performance Metric: Global Performance (GP)","year":2023,"lang":"en","type":"dissertation","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Voice activity detection; Metric (unit); Task (project management); Artificial intelligence; Natural language processing; Process (computing); Speech recognition; Performance metric; Speech processing; Empirical research; Machine learning; Engineering; Mathematics","score_opus":0.1107862799980144,"score_gpt":0.33153069944193697,"score_spread":0.2207444194439226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362575702","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82541406,0.007946669,0.14458172,0.0021132096,0.0008355464,0.00063076185,0.00201941,0.0037999558,0.012658598],"genre_scores_gemma":[0.9164055,0.00049740274,0.07577172,0.00036723918,0.00023626279,0.0001273665,0.002842691,0.00024371283,0.003508031],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98959935,0.0054702666,0.0005405481,0.0016108071,0.002283335,0.0004957277],"domain_scores_gemma":[0.9626235,0.024203716,0.0018974041,0.004610633,0.005586714,0.0010780946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012850264,0.0019396952,0.0016574353,0.0020441017,0.001323587,0.0018901088,0.0019290413,0.0021750168,0.0018946979],"category_scores_gemma":[0.035387073,0.00029976663,0.0008079166,0.0016911598,0.0011480072,0.003953638,0.0018166103,0.0033563233,0.0020831402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0029731034,0.0020974046,0.04928093,0.001151207,0.00056550826,0.0001853989,0.0010392376,0.055874173,0.023768924,0.0037577006,0.027953064,0.83135337],"study_design_scores_gemma":[0.00018905113,0.00548035,0.054519422,0.00014095589,0.0003544942,0.0006384161,0.0012814087,0.8804391,0.040882148,0.005785704,0.010112541,0.00017647419],"about_ca_topic_score_codex":0.004421001,"about_ca_topic_score_gemma":0.0049927263,"teacher_disagreement_score":0.012850264,"about_ca_system_score_codex":0.0015290931,"about_ca_system_score_gemma":0.0009699501,"threshold_uncertainty_score":0.06795955},"labels":[],"label_agreement":null},{"id":"W4362600856","doi":"10.1007/s00500-023-08045-8","title":"Correction to: Class-biased sarcasm detection using BiLSTM variational autoencoder-based synthetic oversampling","year":2023,"lang":"en","type":"article","venue":"Soft Computing","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Oversampling; Sarcasm; Autoencoder; Class (philosophy); Artificial intelligence; Computer science; Pattern recognition (psychology); Artificial neural network; Linguistics; Philosophy; Telecommunications; Irony","score_opus":0.042737985320631146,"score_gpt":0.30044134535087147,"score_spread":0.2577033600302403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362600856","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05149207,0.0015260575,0.75417745,0.011430172,0.10420803,0.0004648273,0.017476903,0.048931953,0.0102925375],"genre_scores_gemma":[0.41422057,0.00065172504,0.46693823,0.0029501605,0.003836422,0.00055023143,0.017840186,0.015312255,0.07770022],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977786,0.00032860483,0.0002307902,0.00067408034,0.00077935384,0.00020869474],"domain_scores_gemma":[0.98765326,0.0032057783,0.00081287423,0.003022822,0.0048040976,0.0005012254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037118702,0.0019587826,0.0015372811,0.0014637072,0.0013276626,0.0019239683,0.00163152,0.003085176,0.09305188],"category_scores_gemma":[0.03227282,0.0008492869,0.0008767191,0.0017405156,0.0006561267,0.001378071,0.002774449,0.0037461126,0.029294003],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010743908,0.00015532543,0.00514469,0.0008246766,0.00028066614,0.0015258695,0.00041001986,0.009019912,0.037673905,0.011626326,0.6006295,0.33163473],"study_design_scores_gemma":[0.000377558,0.00020944342,0.015002557,0.0004221664,0.00017862186,0.0030964685,0.00044548683,0.5949264,0.07894,0.029432122,0.27663025,0.0003389495],"about_ca_topic_score_codex":0.0034070963,"about_ca_topic_score_gemma":0.010215518,"teacher_disagreement_score":0.09305188,"about_ca_system_score_codex":0.0007315508,"about_ca_system_score_gemma":0.0022357907,"threshold_uncertainty_score":0.31128955},"labels":[],"label_agreement":null},{"id":"W4366724860","doi":"10.1109/iccicc57084.2022.10101520","title":"Weighted Lexicon-based Sentiment Analysis for Women Career Traits in Information Technology","year":2022,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Institute of Development and Economic Alternatives","keywords":"Sadness; Lexicon; Psychology; Sentiment analysis; Anger; Happiness; Computer science; Social psychology; Artificial intelligence","score_opus":0.014931325946898603,"score_gpt":0.23790482986746952,"score_spread":0.22297350392057091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366724860","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5881897,0.0020563705,0.371941,0.0022727177,0.00074235705,0.0010659355,0.009416165,0.0019317325,0.022384066],"genre_scores_gemma":[0.91977066,0.0005070073,0.067824215,0.00021970582,0.00020535942,0.0003728038,0.006536895,0.000076221324,0.004487129],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954337,0.00015449738,0.00005020481,0.00007183459,0.00012775649,0.000052359705],"domain_scores_gemma":[0.99898094,0.00048707332,0.00013488124,0.000037798825,0.00032308444,0.00003620129],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089586835,0.000584309,0.0004106561,0.0022534688,0.00045993135,0.0010940932,0.00035762653,0.0003915917,0.0019219548],"category_scores_gemma":[0.0024209863,0.00014525492,0.00076166686,0.001261512,0.00020180034,0.0009069346,0.0004129038,0.000564499,0.0011651287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012139695,0.00083309,0.10033444,0.0009840025,0.00050213997,0.00090911187,0.001918871,0.018425282,0.07381329,0.008981979,0.04155222,0.7505316],"study_design_scores_gemma":[0.0000667343,0.00041825452,0.10327787,0.00015757214,0.00031351607,0.00044378027,0.0019080663,0.85043377,0.010605799,0.009743731,0.022534126,0.000096867436],"about_ca_topic_score_codex":0.0027775895,"about_ca_topic_score_gemma":0.006466056,"teacher_disagreement_score":0.0027775895,"about_ca_system_score_codex":0.00062350923,"about_ca_system_score_gemma":0.00055112067,"threshold_uncertainty_score":0.006429553},"labels":[],"label_agreement":null},{"id":"W4376143588","doi":"10.54097/hbem.v10i.8135","title":"Research on Marketing Methods based on Machine Learning Model","year":2023,"lang":"en","type":"article","venue":"Highlights in Business Economics and Management","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Machine learning; Computer science; Artificial intelligence; Computational learning theory; Big data; Online machine learning; Instance-based learning; Plan (archaeology); Active learning (machine learning); Data science; Data mining","score_opus":0.08273429589405472,"score_gpt":0.3563704737806137,"score_spread":0.273636177886559,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376143588","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019561531,0.042444967,0.8658889,0.009375517,0.0010720781,0.00016507275,0.00016149,0.00040007982,0.060930345],"genre_scores_gemma":[0.6802692,0.06629896,0.21708468,0.0024721501,0.0045328876,0.0004589828,0.00035432982,0.00020607746,0.028322773],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983878,0.0005899761,0.00006188686,0.0002787634,0.0005906077,0.000090960246],"domain_scores_gemma":[0.9968483,0.0022649167,0.00020380257,0.00015197248,0.00047747465,0.000053551965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002517332,0.0007505382,0.00089364016,0.002219738,0.00059059274,0.0027848247,0.0012160091,0.0013769515,0.0040457402],"category_scores_gemma":[0.006886213,0.00031116803,0.0010565846,0.0023075226,0.00115392,0.0061592087,0.00070647727,0.0018538391,0.0010225718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051687188,0.00016970465,0.0038443648,0.000932678,0.0002063548,0.00015042542,0.0002871485,0.05106734,0.0010192017,0.63616073,0.011362218,0.29474813],"study_design_scores_gemma":[0.00002651399,0.0000644149,0.002050991,0.00024344857,0.0000693133,0.00024070697,0.00017494427,0.54243726,0.0012078249,0.41932592,0.034102317,0.000056425524],"about_ca_topic_score_codex":0.0014405726,"about_ca_topic_score_gemma":0.0007527321,"teacher_disagreement_score":0.0040457402,"about_ca_system_score_codex":0.0018810977,"about_ca_system_score_gemma":0.001117576,"threshold_uncertainty_score":0.013648331},"labels":[],"label_agreement":null},{"id":"W4376865864","doi":"10.18280/isi.280210","title":"Word Search with Trending Reviews on Twitter","year":2023,"lang":"fr","type":"article","venue":"Ingénierie des systèmes d information","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Word (group theory); Information retrieval; Natural language processing; Computer science; Linguistics; Philosophy","score_opus":0.08152204767991385,"score_gpt":0.30464368345969955,"score_spread":0.22312163577978572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376865864","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6072902,0.0069820248,0.020450238,0.0032421502,0.0012571773,0.002017979,0.27184442,0.011828817,0.07508699],"genre_scores_gemma":[0.702354,0.0032748452,0.062481895,0.0004770812,0.00091436855,0.0015488589,0.18952028,0.0006444292,0.03878422],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9991602,0.000154214,0.00012562501,0.00016408983,0.00029353672,0.00010239934],"domain_scores_gemma":[0.9986285,0.00047671524,0.00023644282,0.00007128406,0.00048486932,0.000102262595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040783212,0.00072744535,0.0005138889,0.00976666,0.00084216916,0.0012882929,0.00033521204,0.00049655134,0.010701201],"category_scores_gemma":[0.004219627,0.00015535166,0.00057486125,0.0064387703,0.00013605994,0.0018022517,0.0008484251,0.00028491806,0.008835016],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019570403,0.00035137072,0.09141516,0.0053971107,0.00035954925,0.0030527373,0.0044261534,0.002896264,0.033818595,0.0046191723,0.3192721,0.5324348],"study_design_scores_gemma":[0.00022457758,0.0015556718,0.25917566,0.0008770802,0.0004386595,0.0032209838,0.015462315,0.11343083,0.033151615,0.008885766,0.5633175,0.00025933387],"about_ca_topic_score_codex":0.005823506,"about_ca_topic_score_gemma":0.014786935,"teacher_disagreement_score":0.010701201,"about_ca_system_score_codex":0.0006422719,"about_ca_system_score_gemma":0.00056669634,"threshold_uncertainty_score":0.035799086},"labels":[],"label_agreement":null},{"id":"W4378192551","doi":"10.1080/01605682.2023.2215823","title":"Ranking products through online reviews: A novel data-driven method based on interval type-2 fuzzy sets and sentiment analysis","year":2023,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Ranking (information retrieval); Latent Dirichlet allocation; Computer science; Sentiment analysis; Data mining; Product (mathematics); Fuzzy logic; Machine learning; Artificial intelligence; Multiple-criteria decision analysis; Information retrieval; Operations research; Topic model; Mathematics","score_opus":0.29712324754170233,"score_gpt":0.48528260820225544,"score_spread":0.1881593606605531,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378192551","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027921675,0.00033671217,0.9676729,0.0002736205,0.000108463326,0.00029101322,0.00051214715,0.0005913479,0.0022921397],"genre_scores_gemma":[0.35020113,0.0003397294,0.6445362,0.00014456632,0.0002392902,0.0004381775,0.0010586218,0.00007462096,0.0029677218],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99808156,0.000392738,0.00017894348,0.00043327067,0.0008174313,0.00009614359],"domain_scores_gemma":[0.9974293,0.00091220194,0.00031926043,0.000101324265,0.0011512517,0.000086706255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015300274,0.0010253472,0.0010957755,0.0033732413,0.00043859796,0.0012988559,0.0011242437,0.0007348214,0.0014659096],"category_scores_gemma":[0.005134608,0.0004249393,0.0014228522,0.0018175831,0.00028654138,0.0011597257,0.0005901898,0.0008563473,0.0007109515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004292087,0.00034079008,0.011929431,0.00055866037,0.00034285,0.00035757743,0.00067451474,0.07299785,0.022792703,0.007838247,0.007409328,0.87432885],"study_design_scores_gemma":[0.000022619031,0.000088895555,0.0026742555,0.000025466406,0.00005465557,0.000097912955,0.00010561513,0.9861902,0.004286773,0.0038125585,0.0026051437,0.000035875837],"about_ca_topic_score_codex":0.0049708863,"about_ca_topic_score_gemma":0.0060805557,"teacher_disagreement_score":0.0049708863,"about_ca_system_score_codex":0.00077172153,"about_ca_system_score_gemma":0.00097589195,"threshold_uncertainty_score":0.009883881},"labels":[],"label_agreement":null},{"id":"W4378420295","doi":"10.1007/978-3-031-33743-7_1","title":"Community Opinion Network Maximization for Mining Top K Seed Social Network Users","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Maximization; Construct (python library); Social network (sociolinguistics); Sentiment analysis; Public opinion; Graph; Data mining; Artificial intelligence; World Wide Web; Mathematics; Theoretical computer science; Social media; Mathematical optimization; Political science; Computer network","score_opus":0.04358713543604506,"score_gpt":0.2681043933694471,"score_spread":0.22451725793340202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378420295","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.068315625,0.0008855668,0.9220096,0.00067835784,0.00010970769,0.00040509112,0.001146646,0.0015430338,0.0049064597],"genre_scores_gemma":[0.55787635,0.00052623806,0.42854357,0.0003775152,0.0003878168,0.00071560696,0.0045526065,0.00027922582,0.006741126],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990964,0.00024738788,0.000051338506,0.00027431487,0.00021348064,0.00011717369],"domain_scores_gemma":[0.997546,0.0015452326,0.0001588236,0.0001566683,0.0004955914,0.00009757368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021683718,0.0010927994,0.0014858504,0.001677808,0.00077310967,0.0011645983,0.0021387872,0.0015489608,0.0031722423],"category_scores_gemma":[0.0073109027,0.00053914037,0.0010829603,0.0015997399,0.00054978643,0.0018272889,0.0010310306,0.0011669007,0.0019778814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010821285,0.00080536224,0.0121649,0.00085748406,0.0004275481,0.00028275998,0.00038179389,0.15027873,0.02280635,0.017627025,0.039770544,0.7535153],"study_design_scores_gemma":[0.000026794305,0.0000585362,0.00064286473,0.000017288687,0.000031367897,0.000047122438,0.000033573473,0.989921,0.0022491892,0.006118451,0.0008463596,0.0000075255816],"about_ca_topic_score_codex":0.0024674418,"about_ca_topic_score_gemma":0.0040343036,"teacher_disagreement_score":0.0031722423,"about_ca_system_score_codex":0.0007106348,"about_ca_system_score_gemma":0.0010531247,"threshold_uncertainty_score":0.011467576},"labels":[],"label_agreement":null},{"id":"W4378469246","doi":"10.2196/41953","title":"Evaluating the Applicability of Existing Lexicon-Based Sentiment Analysis Techniques on Family Medicine Resident Feedback Field Notes: Retrospective Cohort Study","year":2023,"lang":"en","type":"article","venue":"JMIR Medical Education","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Lexicon; Sentiment analysis; Field (mathematics); Computer science; Natural language processing; Artificial intelligence; Information retrieval; Mathematics","score_opus":0.09052484199692926,"score_gpt":0.46298413263170735,"score_spread":0.3724592906347781,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378469246","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9972511,0.00014555901,0.0007988544,0.00004957348,0.000015551783,0.00040627984,0.0010114735,0.000008865246,0.00031281813],"genre_scores_gemma":[0.9940619,0.00021839482,0.0021537698,0.00014227221,0.000031640404,0.0010231714,0.0018540389,0.00002351701,0.0004912937],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.996382,0.0010642085,0.0005138714,0.0009356156,0.00077420403,0.00033008648],"domain_scores_gemma":[0.9802618,0.0058814944,0.005182002,0.001973416,0.005741021,0.00096022827],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.009499594,0.00046146903,0.00057816284,0.0022245562,0.0011165597,0.0009006998,0.0006303098,0.00054447603,0.0014337328],"category_scores_gemma":[0.028882727,0.00048677757,0.0008091446,0.00138875,0.0006992699,0.0010077548,0.0009543293,0.0006092887,0.0007377512],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004367179,0.00018966715,0.9864863,0.00011392847,0.00008877541,0.00020817337,0.0033198996,0.000060733353,0.0007216299,0.000056965895,0.00095610553,0.0073610493],"study_design_scores_gemma":[0.000053312644,0.0011914462,0.9892855,0.00009518456,0.00010920459,0.00040960417,0.0051013483,0.00075182924,0.00053797464,0.00007715062,0.0023494363,0.000037922913],"about_ca_topic_score_codex":0.015072812,"about_ca_topic_score_gemma":0.023757407,"teacher_disagreement_score":0.9905004,"about_ca_system_score_codex":0.0014896868,"about_ca_system_score_gemma":0.0018426181,"threshold_uncertainty_score":0.050239265},"labels":[],"label_agreement":null},{"id":"W4378695905","doi":"10.3390/a16060271","title":"Enhancing Social Media Platforms with Machine Learning Algorithms and Neural Networks","year":2023,"lang":"en","type":"article","venue":"Algorithms","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Canada West","funders":"","keywords":"Machine learning; Computer science; Artificial neural network; Artificial intelligence; Social media; Upload; Context (archaeology); Social network (sociolinguistics); Workload; Algorithm; World Wide Web","score_opus":0.02024740755852831,"score_gpt":0.24908366809520022,"score_spread":0.22883626053667191,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378695905","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09441085,0.0012046736,0.8750813,0.0019639507,0.0002501364,0.0004075185,0.00020501169,0.00256862,0.02390799],"genre_scores_gemma":[0.6090944,0.0012016229,0.38211998,0.00029684827,0.00028802693,0.00028479256,0.0002474907,0.0002060217,0.0062607485],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999108,0.00034044485,0.000039866653,0.000117132375,0.00032317452,0.00007133598],"domain_scores_gemma":[0.9973423,0.00150653,0.00028824556,0.00025618493,0.0005428134,0.000063938336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015130694,0.0010725652,0.0005468821,0.0016026908,0.00056262896,0.0018318432,0.00087478274,0.000929079,0.0034847986],"category_scores_gemma":[0.007896959,0.00026614906,0.0004810232,0.0009119318,0.00043871882,0.0035763308,0.001131496,0.0009424911,0.0013940823],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021677015,0.00095840613,0.0038932748,0.00039602403,0.00014526374,0.00015107228,0.00017573374,0.22885652,0.02028952,0.02109976,0.006212983,0.7176047],"study_design_scores_gemma":[0.000013608784,0.00010293794,0.00054947374,0.00003167434,0.00002499737,0.000025810063,0.00007388958,0.97627705,0.005678312,0.013475892,0.0037297015,0.000016670587],"about_ca_topic_score_codex":0.0022917872,"about_ca_topic_score_gemma":0.0033127079,"teacher_disagreement_score":0.0034847986,"about_ca_system_score_codex":0.00089567626,"about_ca_system_score_gemma":0.00047524413,"threshold_uncertainty_score":0.011657774},"labels":[],"label_agreement":null},{"id":"W4379385814","doi":"10.14569/ijacsa.2023.0140505","title":"An Enhanced SVM Model for Implicit Aspect Identification in Sentiment Analysis","year":2023,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Overfitting; Computer science; Support vector machine; WordNet; Sentiment analysis; Artificial intelligence; Machine learning; Benchmark (surveying); Identification (biology); Kernel (algebra); Task (project management); Artificial neural network","score_opus":0.018695742225354292,"score_gpt":0.3463898390964029,"score_spread":0.3276940968710486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379385814","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0741336,0.0006061136,0.9206919,0.00048553036,0.0001572537,0.00009774772,0.00025720964,0.0013016565,0.0022690515],"genre_scores_gemma":[0.8258822,0.0003747079,0.16766964,0.00028160683,0.00015623048,0.00018890786,0.00096581446,0.00010496424,0.00437589],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995845,0.00011133868,0.000046845682,0.000105607556,0.000097718905,0.00005407225],"domain_scores_gemma":[0.9992023,0.000271838,0.00006394161,0.00005363775,0.00037809493,0.000030210158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008791366,0.0006650013,0.0007794868,0.0005381628,0.00029789595,0.0009429932,0.0009794912,0.0008269551,0.0019076057],"category_scores_gemma":[0.0020629624,0.00026809686,0.0007466662,0.0005694314,0.00021894819,0.0013116753,0.00053576496,0.0012434496,0.0011790476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051001844,0.00038895427,0.00814089,0.00022700703,0.00016755474,0.00022588967,0.00022840161,0.4052774,0.02169851,0.008011696,0.0069576795,0.54816604],"study_design_scores_gemma":[0.0000032774628,0.000018863697,0.00023366166,0.0000036916688,0.000007114792,0.000011619171,0.0000054993425,0.9980925,0.00048449033,0.0008002894,0.00033650393,0.0000024865278],"about_ca_topic_score_codex":0.0029247245,"about_ca_topic_score_gemma":0.0025702494,"teacher_disagreement_score":0.0029247245,"about_ca_system_score_codex":0.00048081618,"about_ca_system_score_gemma":0.00066461385,"threshold_uncertainty_score":0.006381631},"labels":[],"label_agreement":null},{"id":"W4380714981","doi":"10.1007/s11227-023-05439-1","title":"DialogueINAB: an interaction neural network based on attitudes and behaviors of interlocutors for dialogue emotion recognition","year":2023,"lang":"en","type":"article","venue":"The Journal of Supercomputing","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":92,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"National Natural Science Foundation of China","keywords":"Conversation; Computer science; Emotion recognition; Focus (optics); Perception; Comprehension; Classifier (UML); Cognitive psychology; Human–computer interaction; Artificial intelligence; Psychology; Communication","score_opus":0.05491289198521056,"score_gpt":0.3106297191072561,"score_spread":0.2557168271220456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380714981","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32162166,0.0042441194,0.64543015,0.0008716472,0.0012416496,0.0005788454,0.004538769,0.010931488,0.010541748],"genre_scores_gemma":[0.83862907,0.00075305055,0.14082973,0.0005773563,0.00021259591,0.0005664037,0.004331571,0.00026552705,0.01383466],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997793,0.000049542286,0.000008384174,0.00008348489,0.000038717408,0.00004064203],"domain_scores_gemma":[0.99977845,0.000080520746,0.00001865666,0.000017598128,0.00006700029,0.000037714057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060707645,0.0010648222,0.0006322878,0.00076255144,0.00043005767,0.0005041293,0.0009382465,0.00082410435,0.003386631],"category_scores_gemma":[0.0008114321,0.0003233104,0.0004427605,0.00047757063,0.00020651973,0.00074508437,0.0010721347,0.0011038529,0.0011368374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017633338,0.0011852437,0.011748616,0.00030721794,0.0004137927,0.00021878873,0.00026853866,0.042205445,0.06471067,0.0013708231,0.018328587,0.8574791],"study_design_scores_gemma":[0.000043096887,0.00028792932,0.0060968627,0.00002810676,0.000077802295,0.00006296507,0.000072895185,0.9796893,0.0092551345,0.0011090745,0.003242908,0.000033886023],"about_ca_topic_score_codex":0.006469552,"about_ca_topic_score_gemma":0.0125631085,"teacher_disagreement_score":0.006469552,"about_ca_system_score_codex":0.0005802781,"about_ca_system_score_gemma":0.00048727592,"threshold_uncertainty_score":0.012863755},"labels":[],"label_agreement":null},{"id":"W4381512438","doi":"10.1177/03611981231179167","title":"Using Twitter to Gauge Customer Satisfaction Response to a Major Transit Service Change in Calgary, Canada","year":2023,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lexicon; Sentiment analysis; Customer satisfaction; Service quality; Schedule; Computer science; Service (business); Social media; Reliability (semiconductor); Public transport; Loyalty; Marketing; Business; Engineering; Transport engineering; Artificial intelligence; World Wide Web","score_opus":0.18713712680522437,"score_gpt":0.41638297076738773,"score_spread":0.22924584396216335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381512438","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9897271,0.00006614301,0.0004473875,0.00042195836,0.000016656339,0.00009190898,0.003999812,0.000040920997,0.005188165],"genre_scores_gemma":[0.98962456,0.00016178231,0.0010995102,0.00019646468,0.000010673622,0.000067606205,0.0029479233,0.000019755977,0.0058718165],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994868,0.000059945836,0.000022905184,0.00007740541,0.00022155386,0.00013148533],"domain_scores_gemma":[0.9979061,0.00023131087,0.00015144744,0.000040272924,0.0014864636,0.00018438263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000539368,0.00022762187,0.00021698758,0.00088091346,0.0015831301,0.0013620995,0.00047141267,0.000314494,0.002085587],"category_scores_gemma":[0.0023132204,0.00013688211,0.00015155725,0.0024986323,0.0004885475,0.00039861148,0.0005363996,0.00043763887,0.0004975959],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005011626,0.00022485248,0.8670607,0.00018518507,0.00007208463,0.0005821355,0.014727089,0.0019084995,0.005792721,0.000506206,0.020702302,0.087736994],"study_design_scores_gemma":[0.000015421814,0.00008683309,0.953366,0.000036950267,0.000029242141,0.000041952597,0.025380211,0.010653903,0.0016371277,0.00007328464,0.008641789,0.000037228034],"about_ca_topic_score_codex":0.9817981,"about_ca_topic_score_gemma":0.9886253,"teacher_disagreement_score":0.018201888,"about_ca_system_score_codex":0.013601156,"about_ca_system_score_gemma":0.0101018865,"threshold_uncertainty_score":0.098683715},"labels":[],"label_agreement":null},{"id":"W4382776025","doi":"10.18280/mmep.100308","title":"Enhancing Arabic Sentiment Analysis in E-Commerce Reviews on Social Media Through a Stacked Ensemble Deep Learning Approach","year":2023,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Arabic; Sentiment analysis; Social media; Artificial intelligence; Computer science; Natural language processing; Data science; World Wide Web; Linguistics; Philosophy","score_opus":0.06552159951890932,"score_gpt":0.26800306455878137,"score_spread":0.20248146503987205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382776025","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4951457,0.002666978,0.4843848,0.0010734322,0.00042519168,0.0001896247,0.0013406667,0.0052008703,0.009572737],"genre_scores_gemma":[0.9303552,0.0006978654,0.061284382,0.0002821331,0.00018976326,0.0000703199,0.0018167602,0.00007666682,0.005226848],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996724,0.00007327176,0.000024239363,0.00008690042,0.000086934575,0.00005638208],"domain_scores_gemma":[0.9994671,0.00012304618,0.00006264054,0.00003907755,0.0002748357,0.000033243596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065335276,0.0012786023,0.00065066863,0.0012464545,0.00026764505,0.00072625355,0.0004642814,0.0004896643,0.0008256367],"category_scores_gemma":[0.0014202021,0.00029306783,0.0008004691,0.0006406772,0.00017462506,0.0011670758,0.00061572914,0.00076906243,0.0009284613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004989012,0.00052691594,0.022211894,0.00016576512,0.0005444175,0.00030838323,0.00039306242,0.12640445,0.030994985,0.0011938901,0.011131417,0.8056259],"study_design_scores_gemma":[0.0000050807917,0.000067038985,0.0027876135,0.000010150953,0.00006353973,0.00003341686,0.000054958855,0.99131376,0.0039929408,0.000702094,0.0009556235,0.000013789506],"about_ca_topic_score_codex":0.006136943,"about_ca_topic_score_gemma":0.011021302,"teacher_disagreement_score":0.006136943,"about_ca_system_score_codex":0.0004038338,"about_ca_system_score_gemma":0.00042894893,"threshold_uncertainty_score":0.012202442},"labels":[],"label_agreement":null},{"id":"W4383268266","doi":"10.1007/978-3-031-33617-1_6","title":"Conclusions","year":2023,"lang":"en","type":"book-chapter","venue":"SpringerBriefs in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland; Toronto Metropolitan University; McGill University; Dalhousie University","funders":"","keywords":"Social media; Generative grammar; Sociology; Political science; Public relations; Computer science; World Wide Web; Artificial intelligence","score_opus":0.034517579098859275,"score_gpt":0.27015807882655934,"score_spread":0.23564049972770007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383268266","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00498492,0.003808183,0.00838381,0.03910651,0.010620184,0.00020678004,0.0057575325,0.0010464946,0.92608565],"genre_scores_gemma":[0.09889581,0.005564363,0.007885757,0.043434214,0.0041694674,0.0003482767,0.010941897,0.0013950685,0.82736516],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977901,0.0003282891,0.00007627562,0.00054251764,0.0009348421,0.00032806344],"domain_scores_gemma":[0.9952356,0.000815455,0.00019624943,0.0005862008,0.0025487358,0.0006177725],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0026208016,0.0008655887,0.00043280638,0.0013329053,0.0016618684,0.0051940843,0.0018084568,0.0019718797,0.37302408],"category_scores_gemma":[0.011961565,0.00021148572,0.0010083142,0.00097222615,0.0009520053,0.0032152997,0.0018245678,0.0020118763,0.14438051],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048555498,0.00010855771,0.0033897976,0.000868747,0.000054862012,0.00029580484,0.0006871991,0.00047675145,0.0015427632,0.09043568,0.6504919,0.25116247],"study_design_scores_gemma":[0.000037594764,0.000030051413,0.0022600603,0.00043301008,0.0000331004,0.00016288052,0.0009527217,0.0001845935,0.0011163527,0.023646489,0.97113055,0.000012547618],"about_ca_topic_score_codex":0.0074021174,"about_ca_topic_score_gemma":0.0063608903,"teacher_disagreement_score":0.6269759,"about_ca_system_score_codex":0.002882706,"about_ca_system_score_gemma":0.0034804943,"threshold_uncertainty_score":0.89430505},"labels":[],"label_agreement":null},{"id":"W4383268287","doi":"10.1007/978-3-031-33617-1_4","title":"Topic and Sentiment Modelling for Social Media","year":2023,"lang":"en","type":"book-chapter","venue":"SpringerBriefs in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland; Toronto Metropolitan University; McGill University; Dalhousie University","funders":"","keywords":"Sentiment analysis; Social media; Computer science; Data science; World Wide Web; Artificial intelligence","score_opus":0.07126405210916505,"score_gpt":0.27664840501618404,"score_spread":0.205384352907019,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383268287","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009102401,0.0018912974,0.9804539,0.00081425067,0.0004165901,0.000089583635,0.0013137279,0.0017381792,0.0041800905],"genre_scores_gemma":[0.3996763,0.004988194,0.55126524,0.00047781714,0.001674685,0.0007148229,0.009721467,0.0013134291,0.030168032],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992417,0.00032714722,0.000058425707,0.00013225862,0.00017346165,0.00006707374],"domain_scores_gemma":[0.9988053,0.00076370523,0.00007914608,0.00011484319,0.00018280953,0.000054211574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015979193,0.00089524157,0.00084889575,0.0010966422,0.00054251374,0.0021101546,0.0009589603,0.0010791632,0.0054946374],"category_scores_gemma":[0.005325424,0.0005322884,0.0017138353,0.0014231876,0.0003055117,0.0027022916,0.0010920824,0.0018067042,0.003748233],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033302317,0.00033975663,0.0033930705,0.0006714991,0.00051256194,0.00028368324,0.0005865974,0.15840459,0.014077902,0.07467757,0.07697901,0.66974074],"study_design_scores_gemma":[0.000009548973,0.000024897648,0.00071813393,0.00003485346,0.00003909814,0.00006211997,0.0000539543,0.9344455,0.0011482984,0.050772645,0.012674662,0.000016252028],"about_ca_topic_score_codex":0.0039035045,"about_ca_topic_score_gemma":0.0055091563,"teacher_disagreement_score":0.0054946374,"about_ca_system_score_codex":0.00061059813,"about_ca_system_score_gemma":0.0006047592,"threshold_uncertainty_score":0.018381357},"labels":[],"label_agreement":null},{"id":"W4383535550","doi":"10.54254/2755-2721/5/20230633","title":"Performance analysis of sentiment classification based neural network","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of the Fraser Valley; Queen's University","funders":"","keywords":"Computer science; Word2vec; Recurrent neural network; Word embedding; Artificial intelligence; Deep learning; Artificial neural network; Pooling; Convolutional neural network; Softmax function; Encoder; Transformer; Context (archaeology); Language model; Sentiment analysis; Embedding; Machine learning","score_opus":0.01593937030535209,"score_gpt":0.22282005101240202,"score_spread":0.20688068070704993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383535550","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81174684,0.006613637,0.14498064,0.0013634501,0.0011575683,0.00023961376,0.0026991859,0.005982443,0.025216699],"genre_scores_gemma":[0.965996,0.0010675141,0.023605093,0.00015729204,0.00009790385,0.00010895051,0.0038051321,0.00012290556,0.005039176],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989863,0.0002338823,0.00009648583,0.00020290262,0.00032182044,0.00015868673],"domain_scores_gemma":[0.99859375,0.00045523574,0.00010932463,0.00009882159,0.00069302844,0.000049832797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001686456,0.0014003728,0.000862801,0.0011766528,0.000443604,0.0008933694,0.00068275566,0.0007548403,0.0023841565],"category_scores_gemma":[0.0039760163,0.0002133474,0.00059967214,0.00081100536,0.00021429498,0.001289676,0.0005755353,0.0006501327,0.0011089774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022242921,0.000534513,0.025230221,0.00059076364,0.0004845324,0.00021970524,0.00010813308,0.29542536,0.01803169,0.002081214,0.018497346,0.63657224],"study_design_scores_gemma":[0.000013197395,0.00012395601,0.0021312593,0.000013345271,0.000037070804,0.000022005592,0.000024092451,0.99153286,0.0051638368,0.00038164828,0.0005466244,0.000010077922],"about_ca_topic_score_codex":0.008740842,"about_ca_topic_score_gemma":0.004894271,"teacher_disagreement_score":0.008740842,"about_ca_system_score_codex":0.0010582232,"about_ca_system_score_gemma":0.00056536566,"threshold_uncertainty_score":0.01737994},"labels":[],"label_agreement":null},{"id":"W4383560769","doi":"10.54254/2755-2721/5/20230694","title":"Analysis of sentiment analysis model based on deep learning","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Sentiment analysis; Computer science; Artificial intelligence; Deep learning; Convolutional neural network; Recurrent neural network; Machine learning; Natural language processing; Artificial neural network","score_opus":0.008983315016342275,"score_gpt":0.22281215350672154,"score_spread":0.21382883849037926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383560769","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31662765,0.0011637611,0.6659751,0.0010301546,0.0002671095,0.0001371432,0.0012161669,0.00210307,0.011479816],"genre_scores_gemma":[0.9589916,0.0005098518,0.03363714,0.00011820976,0.000053905267,0.00007275881,0.001378961,0.00007681441,0.005160756],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9998031,0.000033283486,0.000011265872,0.000038615522,0.00007067003,0.000043013006],"domain_scores_gemma":[0.99962556,0.00010269657,0.000033097203,0.00002176967,0.00020102659,0.000015837079],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061014784,0.00064276083,0.000480234,0.0007143336,0.00026589035,0.0006027843,0.00042229428,0.00031525502,0.0029926298],"category_scores_gemma":[0.0013527987,0.00016996135,0.0007297067,0.00038388127,0.00014800673,0.0007781854,0.00025580148,0.00069102267,0.0006942453],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061456993,0.00034657714,0.021142257,0.00031855493,0.00037959014,0.00046925232,0.00015599265,0.55692667,0.042969894,0.021419542,0.020110272,0.33514684],"study_design_scores_gemma":[0.0000030119993,0.000014573782,0.0007650024,0.0000031335105,0.0000106093885,0.000011066972,0.0000065642844,0.9961683,0.0013415503,0.0013516145,0.00032145015,0.000003058378],"about_ca_topic_score_codex":0.0054212403,"about_ca_topic_score_gemma":0.0039036348,"teacher_disagreement_score":0.0054212403,"about_ca_system_score_codex":0.0006735996,"about_ca_system_score_gemma":0.0006049616,"threshold_uncertainty_score":0.010779381},"labels":[],"label_agreement":null},{"id":"W4383617168","doi":"10.1007/978-3-031-35915-6_42","title":"Customer Review Classification Using Machine Learning and Deep Learning Techniques","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Customer intelligence; Customer base; Computer science; Customer retention; Voice of the customer; Customer to customer; Customer advocacy; Context (archaeology); Competitive advantage; Marketing; Product (mathematics); Service (business); Confusion; Business; Service quality","score_opus":0.04088826005156904,"score_gpt":0.3050112540376721,"score_spread":0.26412299398610306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383617168","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4045018,0.009019249,0.5430309,0.002275622,0.0011651558,0.0006429905,0.0034687018,0.0048337537,0.031061769],"genre_scores_gemma":[0.83991295,0.0020846298,0.120167255,0.00029097215,0.00097359606,0.00021483318,0.0042390195,0.00014435589,0.03197233],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943393,0.00009728876,0.00005116988,0.000082387145,0.00024335031,0.00009190143],"domain_scores_gemma":[0.99822146,0.000512265,0.00015591855,0.00008793474,0.00095053576,0.00007186107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007421443,0.00076297927,0.0007806657,0.0023270117,0.0003098105,0.0012667074,0.0006699992,0.00066294626,0.0026774067],"category_scores_gemma":[0.002189694,0.00023266155,0.0006339134,0.0016940281,0.000111413785,0.00086511916,0.00041606533,0.00086895947,0.0021987506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024102275,0.00037952245,0.0099376915,0.0001922523,0.00012887023,0.00014642712,0.000053360167,0.016102053,0.010280975,0.001183544,0.03265299,0.9287012],"study_design_scores_gemma":[0.000012991273,0.000082288534,0.0057493956,0.000026035814,0.000059060927,0.0001059637,0.000045534623,0.9822667,0.0059347665,0.0013469114,0.004352661,0.000017805005],"about_ca_topic_score_codex":0.005254598,"about_ca_topic_score_gemma":0.009726854,"teacher_disagreement_score":0.005254598,"about_ca_system_score_codex":0.0006749771,"about_ca_system_score_gemma":0.0006271298,"threshold_uncertainty_score":0.010448039},"labels":[],"label_agreement":null},{"id":"W4384284067","doi":"10.1109/tcss.2023.3290558","title":"An NLP-Deep Learning Approach for Product Rating Prediction Based on Online Reviews and Product Features","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Computational Social Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial intelligence; Computer science; Machine learning; Sentiment analysis; Deep learning; F1 score; Strengths and weaknesses; Product (mathematics); Artificial neural network; Popularity; Laptop; Data mining; Mathematics","score_opus":0.04447984219110944,"score_gpt":0.3092533803491714,"score_spread":0.2647735381580619,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384284067","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13439439,0.0049758996,0.83871686,0.0016723548,0.00042690046,0.00036976638,0.003219995,0.007031955,0.009191841],"genre_scores_gemma":[0.8084186,0.0014159023,0.17057367,0.0007661346,0.00038542887,0.00033643554,0.00623603,0.00011474863,0.011753101],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994167,0.00010790862,0.000051350264,0.00021377322,0.0001417134,0.00006856912],"domain_scores_gemma":[0.9991209,0.00034521395,0.000105224775,0.00006736487,0.0003177595,0.000043593976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007129286,0.0012817736,0.00093369937,0.0015151593,0.00026812727,0.00073037174,0.0012289435,0.0011106462,0.0017898517],"category_scores_gemma":[0.0018334865,0.00046318735,0.0008023068,0.001401064,0.00022741537,0.0013411887,0.00064210076,0.0015200553,0.0014256672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004339273,0.0010469878,0.010247549,0.00035842494,0.00025678446,0.0005770877,0.00014773436,0.16128974,0.009121267,0.0021135188,0.016145835,0.79826117],"study_design_scores_gemma":[0.000010088081,0.000042054602,0.0008700123,0.000009910846,0.000023626473,0.00003737497,0.00001157912,0.99625933,0.0008143041,0.001085215,0.00082935137,0.000007236933],"about_ca_topic_score_codex":0.011152823,"about_ca_topic_score_gemma":0.013303543,"teacher_disagreement_score":0.011152823,"about_ca_system_score_codex":0.0008280464,"about_ca_system_score_gemma":0.0009314447,"threshold_uncertainty_score":0.022175848},"labels":[],"label_agreement":null},{"id":"W4385078483","doi":"10.18280/isi.280325","title":"An Innovative Arabic Word Embedding Representation for Enhanced Sentiment Analysis","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Arabic; Natural language processing; Representation (politics); Word (group theory); Word embedding; Sentiment analysis; Computer science; Embedding; Artificial intelligence; Linguistics; Political science; Philosophy","score_opus":0.028263918566714917,"score_gpt":0.31873616405306865,"score_spread":0.29047224548635375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385078483","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061792202,0.0008595733,0.9283668,0.00047816825,0.00047421575,0.00015402454,0.00080239103,0.0024173046,0.0046553947],"genre_scores_gemma":[0.3695706,0.0011762687,0.61691064,0.00025660018,0.00028860115,0.00026461616,0.0025334114,0.0003339713,0.008665238],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997274,0.00006968779,0.000029834573,0.00005987319,0.00008163599,0.000031615105],"domain_scores_gemma":[0.9996124,0.00010801254,0.000044025677,0.00003892956,0.00017980412,0.000016848166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035597,0.00080105464,0.00041719785,0.0010142805,0.00024787485,0.0008354385,0.00034105303,0.0004225669,0.0037160409],"category_scores_gemma":[0.0016581654,0.00012795998,0.00049528945,0.0010362471,0.00021967925,0.0012952733,0.00069587707,0.00058907317,0.0023604052],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033745915,0.0001097595,0.0010628274,0.00022952832,0.000053693017,0.000185048,0.0002820464,0.009967909,0.07929602,0.00889813,0.008852605,0.89072496],"study_design_scores_gemma":[0.000057537698,0.0004761644,0.003375059,0.000102065016,0.00012757386,0.000784458,0.0005868893,0.85785735,0.071451545,0.017796136,0.047298014,0.0000871408],"about_ca_topic_score_codex":0.00069487677,"about_ca_topic_score_gemma":0.0009733866,"teacher_disagreement_score":0.0037160409,"about_ca_system_score_codex":0.00018141845,"about_ca_system_score_gemma":0.00033590867,"threshold_uncertainty_score":0.012431443},"labels":[],"label_agreement":null},{"id":"W4385196166","doi":"10.1007/978-3-031-33065-0_8","title":"Detecting Trending Topics and Influence on Social Media","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in social networks","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Influencer marketing; Social media; Field (mathematics); Point (geometry); Computer science; Data science; Health care; Social network analysis; Social network (sociolinguistics); Internet privacy; World Wide Web; Political science; Business; Mathematics; Marketing","score_opus":0.037744123123662585,"score_gpt":0.2784802059467725,"score_spread":0.24073608282310988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385196166","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8351439,0.0036973567,0.1325451,0.0006082279,0.00039585502,0.00022482018,0.006290546,0.0021687304,0.018925378],"genre_scores_gemma":[0.93876386,0.0015430206,0.044376116,0.00006839943,0.00081429427,0.00012159284,0.0055962186,0.0002356098,0.008480995],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956113,0.0000659306,0.000026335914,0.00010968192,0.00017521706,0.00006180167],"domain_scores_gemma":[0.9989222,0.00055854867,0.00013558056,0.00005738116,0.00024686495,0.00007940916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045757147,0.0006274996,0.000499397,0.0048050163,0.00047839322,0.0014847387,0.0003704138,0.00046260757,0.0021329345],"category_scores_gemma":[0.002223757,0.00025062868,0.00064459175,0.0037934645,0.00017727545,0.0017679562,0.00052425667,0.0004906123,0.0016552293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061340583,0.00035843512,0.14776528,0.00044625206,0.00037724723,0.0007022164,0.0013156015,0.0063256244,0.0690582,0.003988188,0.018921364,0.7501282],"study_design_scores_gemma":[0.00005470957,0.0005672943,0.32193062,0.00012237902,0.00069400173,0.0014498039,0.0025288009,0.5906824,0.033510435,0.015247119,0.033121534,0.00009091106],"about_ca_topic_score_codex":0.0027028518,"about_ca_topic_score_gemma":0.0056369617,"teacher_disagreement_score":0.0048050163,"about_ca_system_score_codex":0.00030426675,"about_ca_system_score_gemma":0.00019341668,"threshold_uncertainty_score":0.0071353316},"labels":[],"label_agreement":null},{"id":"W4385385145","doi":"10.1007/978-3-031-37660-3_11","title":"MTGR: Improving Emotion and Sentiment Analysis with Gated Residual Networks","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Modalities; Sentiment analysis; Residual; Transformer; Artificial intelligence; Emotion recognition; Machine learning; Speech recognition; Algorithm","score_opus":0.014127612112920388,"score_gpt":0.2332972907404317,"score_spread":0.21916967862751133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385385145","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036832027,0.00068697496,0.94407886,0.00038262547,0.0004461526,0.00016840521,0.0010937513,0.011584857,0.0047263033],"genre_scores_gemma":[0.36734712,0.0005882876,0.59649235,0.0005740402,0.0004383587,0.00028475336,0.0061382614,0.002011556,0.026125235],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999608,0.00010339997,0.000017961387,0.00010433188,0.000106946885,0.000059457958],"domain_scores_gemma":[0.9995012,0.00017794072,0.00003665814,0.00009396072,0.00016147313,0.000028687737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007854264,0.0012711255,0.00074309646,0.000799866,0.0004036237,0.00077244214,0.0014600278,0.00080128835,0.0075422996],"category_scores_gemma":[0.0019892685,0.00033932217,0.0007876307,0.0007710805,0.0003010466,0.0014188903,0.0012083304,0.0014175719,0.004355437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005004698,0.0002610664,0.0009217694,0.00016055575,0.00017342376,0.00013947889,0.000121636265,0.0681677,0.0444166,0.0052990923,0.033860754,0.8459775],"study_design_scores_gemma":[0.000031731055,0.00009065944,0.00053561147,0.000013781521,0.000046915677,0.000034098503,0.000028241355,0.9793767,0.009219433,0.006389642,0.004219737,0.00001356716],"about_ca_topic_score_codex":0.004365593,"about_ca_topic_score_gemma":0.0068955556,"teacher_disagreement_score":0.0075422996,"about_ca_system_score_codex":0.00039340375,"about_ca_system_score_gemma":0.00040004568,"threshold_uncertainty_score":0.02523154},"labels":[],"label_agreement":null},{"id":"W4385461227","doi":"10.21203/rs.3.rs-3208999/v1","title":"Enhanced heterogeneous graph convolutional networks with dual-level attention for aspect-based sentiment analysis","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Graph; Sentence; Dependency (UML); Parsing; Artificial intelligence; Dependency graph; Dependency grammar; Convolutional neural network; Node (physics); Sentiment analysis; Natural language processing; Dual (grammatical number); Theoretical computer science","score_opus":0.11242862957659328,"score_gpt":0.38094615515239766,"score_spread":0.2685175255758044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385461227","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20148803,0.00090826186,0.7874691,0.00078833645,0.00016665153,0.00007952703,0.0005011908,0.0030687577,0.00553015],"genre_scores_gemma":[0.9114792,0.00038084455,0.08234181,0.00030322306,0.000061275445,0.00006149837,0.0008580557,0.00011479071,0.0043992572],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986446,0.000023905419,0.0000061393202,0.000043476026,0.00002609461,0.000035814453],"domain_scores_gemma":[0.99979883,0.000060150065,0.00002871673,0.000023280147,0.000070769674,0.000018231873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000332461,0.0008651109,0.00037912026,0.0007144571,0.00026338015,0.0005406069,0.0008424067,0.00064472784,0.0013862678],"category_scores_gemma":[0.00087563205,0.0002771589,0.00066019327,0.0006549609,0.0003046609,0.0010870886,0.00054757483,0.00081462046,0.00043134033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041891512,0.0003188533,0.005345989,0.00014091349,0.0002626172,0.00028781267,0.00017968404,0.51474875,0.055062342,0.012739845,0.009108985,0.40138528],"study_design_scores_gemma":[0.0000027460487,0.000013388976,0.00031819174,0.0000027693816,0.000016953765,0.000007291111,0.000004966978,0.99541485,0.0018491948,0.002063268,0.0003030898,0.0000032860355],"about_ca_topic_score_codex":0.012023199,"about_ca_topic_score_gemma":0.014383261,"teacher_disagreement_score":0.012023199,"about_ca_system_score_codex":0.001044389,"about_ca_system_score_gemma":0.00054402114,"threshold_uncertainty_score":0.02390647},"labels":[],"label_agreement":null},{"id":"W4385485108","doi":"10.1109/compsac57700.2023.00135","title":"Modeling Time-Varying User Attitudes in Social Media","year":2023,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Université Laval","funders":"","keywords":"Anticipation (artificial intelligence); Social media; Computer science; Data science; Artificial intelligence; World Wide Web","score_opus":0.06138400868926154,"score_gpt":0.30788822356267154,"score_spread":0.24650421487341,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385485108","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9196882,0.00020508913,0.07713797,0.00052012573,0.000069359485,0.00006171232,0.0008311713,0.00018322933,0.0013030423],"genre_scores_gemma":[0.9914111,0.00007577793,0.00718238,0.000030557487,0.000040449908,0.0000355346,0.00043217337,0.00000848996,0.0007836209],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966073,0.00012869161,0.000016862323,0.00009318856,0.00004702576,0.00005347369],"domain_scores_gemma":[0.9978302,0.0014548937,0.00030729818,0.00010303826,0.00021211957,0.000092471666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013340947,0.0005738195,0.00031732177,0.0009957991,0.00025928355,0.0007464962,0.00046855022,0.00066820387,0.00076161174],"category_scores_gemma":[0.0038009807,0.00020192083,0.00048257777,0.00076682493,0.00025598155,0.00089509436,0.00032682906,0.0009913311,0.00039568683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010130125,0.001457711,0.2883776,0.00017240389,0.0003568919,0.00062807905,0.0012479812,0.499804,0.019018272,0.0072856196,0.0039033908,0.17673509],"study_design_scores_gemma":[0.00000441839,0.000042967993,0.011236725,0.000003593647,0.0000107481155,0.000017476254,0.000055232427,0.98667204,0.0005780319,0.0011322761,0.0002391886,0.0000073053698],"about_ca_topic_score_codex":0.007989718,"about_ca_topic_score_gemma":0.011440537,"teacher_disagreement_score":0.007989718,"about_ca_system_score_codex":0.00054097007,"about_ca_system_score_gemma":0.00028995067,"threshold_uncertainty_score":0.015886426},"labels":[],"label_agreement":null},{"id":"W4385570923","doi":"10.18653/v1/2023.acl-long.421","title":"ConFEDE: Contrastive Feature Decomposition for Multimodal Sentiment Analysis","year":2023,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":142,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence; Modalities; Sentiment analysis; Modality (human–computer interaction); Feature (linguistics); Representation (politics); Natural language processing; Feature learning; Decomposition; Pattern recognition (psychology); Linguistics","score_opus":0.017996843034994103,"score_gpt":0.3187499885937276,"score_spread":0.3007531455587335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385570923","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0523793,0.0012511828,0.9339295,0.00042824858,0.00022125457,0.00034924241,0.0021259151,0.0047183875,0.004596992],"genre_scores_gemma":[0.4804743,0.0008018628,0.5024387,0.00042765646,0.00029863676,0.00072096486,0.008563341,0.00042451822,0.005850014],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99947494,0.00013875353,0.000032460946,0.00012870156,0.00016407204,0.00006118133],"domain_scores_gemma":[0.9992487,0.00028106465,0.00007913501,0.00010165333,0.00024806516,0.00004144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014904453,0.0014302867,0.0008676924,0.0018893525,0.00034389616,0.0008461733,0.0008107681,0.00074202224,0.0036868264],"category_scores_gemma":[0.004221481,0.00021741794,0.001161993,0.0012360286,0.00035589738,0.0014920912,0.0012072432,0.0015776632,0.0014199377],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053228886,0.0003609077,0.0035057038,0.0003007412,0.00026193116,0.00016803642,0.00019648465,0.026215961,0.05778601,0.0069434554,0.02738216,0.87634623],"study_design_scores_gemma":[0.00007171022,0.00027345115,0.0051291655,0.00004221223,0.000088659486,0.0001608007,0.0001532948,0.9475959,0.018969774,0.01472447,0.012740221,0.00005029985],"about_ca_topic_score_codex":0.0017460778,"about_ca_topic_score_gemma":0.0030360608,"teacher_disagreement_score":0.0036868264,"about_ca_system_score_codex":0.0006333913,"about_ca_system_score_gemma":0.0005003838,"threshold_uncertainty_score":0.0123336315},"labels":[],"label_agreement":null},{"id":"W4385570955","doi":"10.18653/v1/2023.findings-acl.860","title":"ConKI: Contrastive Knowledge Injection for Multimodal Sentiment Analysis","year":2023,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Sentiment analysis; Artificial intelligence; Natural language processing; Modality (human–computer interaction); Modalities; Domain knowledge","score_opus":0.029614213008232994,"score_gpt":0.3166614163898587,"score_spread":0.2870472033816257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385570955","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02449747,0.00040497308,0.9572039,0.00026244434,0.00013304928,0.0003042631,0.00069609174,0.012131196,0.0043666223],"genre_scores_gemma":[0.41986877,0.00040988627,0.56810915,0.000635818,0.00015416142,0.00061531394,0.0035597498,0.000828038,0.005819073],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999181,0.00019056999,0.000046202596,0.00023791191,0.0002570141,0.00008727194],"domain_scores_gemma":[0.99833316,0.0007481073,0.00014828307,0.00032768308,0.00036560048,0.00007713783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001776849,0.0016758402,0.00077225186,0.0013258625,0.0004086062,0.0012878622,0.001657198,0.0011080824,0.0055384864],"category_scores_gemma":[0.0070553753,0.00035555012,0.001101886,0.00078396307,0.0006528374,0.0029143556,0.0023771317,0.0023119706,0.0023610406],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00071379106,0.00070498214,0.0035948688,0.00047472728,0.00036372698,0.00030844478,0.0004737185,0.046258017,0.05669893,0.011889259,0.02256828,0.85595125],"study_design_scores_gemma":[0.000052514035,0.00019721981,0.0015576907,0.00004117348,0.000082552615,0.00014127324,0.00013827744,0.94218296,0.030590188,0.017959688,0.007011888,0.00004454119],"about_ca_topic_score_codex":0.0017274464,"about_ca_topic_score_gemma":0.0032266881,"teacher_disagreement_score":0.0055384864,"about_ca_system_score_codex":0.0008179924,"about_ca_system_score_gemma":0.00084100803,"threshold_uncertainty_score":0.018528104},"labels":[],"label_agreement":null},{"id":"W4385572274","doi":"10.18653/v1/2023.semeval-1.33","title":"UBC-DLNLP at SemEval-2023 Task 12: Impact of Transfer Learning on African Sentiment Analysis","year":2023,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"SemEval; Computer science; Sentiment analysis; Transfer of learning; Task (project management); Artificial intelligence; Natural language processing; F1 score; Machine learning","score_opus":0.025686279420517717,"score_gpt":0.2928840390103468,"score_spread":0.2671977595898291,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385572274","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43418086,0.007456945,0.19435543,0.009187738,0.006359043,0.002865383,0.1047512,0.14974214,0.09110126],"genre_scores_gemma":[0.52541524,0.0010863296,0.17854662,0.0028514473,0.000684491,0.0017631331,0.2473768,0.0090881875,0.033187885],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9944084,0.0026571245,0.00026674123,0.0013719524,0.000739979,0.00055577897],"domain_scores_gemma":[0.9915513,0.0035802533,0.00023097011,0.0022787824,0.0017289899,0.0006297641],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008760693,0.004311692,0.0016833664,0.0023257611,0.0024168508,0.0030238144,0.0030412425,0.003338768,0.021652076],"category_scores_gemma":[0.01672806,0.00072677596,0.0019598904,0.0022579804,0.0010917078,0.0067073978,0.005438416,0.0050140107,0.018183388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019973451,0.0020756975,0.007567617,0.0012358658,0.0005262549,0.0008216717,0.00077503506,0.022189667,0.016797623,0.0027566531,0.509326,0.43393055],"study_design_scores_gemma":[0.0016947839,0.0013885376,0.018383091,0.0005218383,0.0003511238,0.0015223308,0.0023929717,0.6248453,0.075837076,0.020567289,0.2521103,0.00038531062],"about_ca_topic_score_codex":0.017280228,"about_ca_topic_score_gemma":0.022303076,"teacher_disagreement_score":0.021652076,"about_ca_system_score_codex":0.0020590513,"about_ca_system_score_gemma":0.0024732384,"threshold_uncertainty_score":0.07243341},"labels":[],"label_agreement":null},{"id":"W4385572562","doi":"10.18653/v1/2023.semeval-1.315","title":"SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)","year":2023,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"SemEval; Computer science; Sentiment analysis; Task (project management); Natural language processing; Artificial intelligence; Management","score_opus":0.03172911908924325,"score_gpt":0.3074321463866484,"score_spread":0.27570302729740515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385572562","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17795609,0.007902987,0.056117345,0.009368567,0.00865341,0.004763664,0.6524248,0.040637385,0.042175774],"genre_scores_gemma":[0.12604883,0.0009981754,0.08673518,0.0013472188,0.0006030202,0.00359153,0.756805,0.0025673786,0.021303589],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99592847,0.0020523665,0.00033963614,0.0005933058,0.00070193736,0.00038424204],"domain_scores_gemma":[0.99534684,0.0016674683,0.00025023488,0.00074967986,0.0014214393,0.00056429394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007316712,0.003766049,0.0015090699,0.003255753,0.0026478071,0.0030370438,0.0020087508,0.0034235388,0.01593677],"category_scores_gemma":[0.010626361,0.00056558265,0.001983198,0.002112589,0.0007752709,0.0040287985,0.005365881,0.0031659096,0.020958502],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010041003,0.00055101444,0.003422199,0.0014498534,0.00019371463,0.0005115637,0.0006742554,0.0011364847,0.010490232,0.0013759591,0.8619169,0.117273696],"study_design_scores_gemma":[0.0022045653,0.00089021533,0.036454014,0.0010763166,0.00044302133,0.0020729466,0.0069366745,0.067413606,0.04552711,0.011496981,0.82511675,0.0003678409],"about_ca_topic_score_codex":0.010523305,"about_ca_topic_score_gemma":0.016463093,"teacher_disagreement_score":0.01593677,"about_ca_system_score_codex":0.0013186089,"about_ca_system_score_gemma":0.0024244955,"threshold_uncertainty_score":0.05331379},"labels":[],"label_agreement":null},{"id":"W4385662215","doi":"10.22541/au.169147263.34210293/v1","title":"Personalized and Explainable Aspect-based Recommendation using Latent Opinion Groups","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Recommender system; Set (abstract data type); Construct (python library); Transparency (behavior); Dependency (UML); Latent semantic analysis; Product (mathematics); Semantics (computer science); Service (business); Data science; Information retrieval; World Wide Web; Artificial intelligence","score_opus":0.12049897146612498,"score_gpt":0.32720833214683037,"score_spread":0.2067093606807054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385662215","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1621933,0.0048745517,0.8155414,0.0015663275,0.00019354581,0.00035491434,0.00529086,0.005758868,0.004226347],"genre_scores_gemma":[0.6739533,0.0010434022,0.30775738,0.0006248424,0.00030450904,0.00021215585,0.011158265,0.00017148216,0.004774643],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884856,0.00030467613,0.0000802715,0.00039943954,0.0002895257,0.00007745941],"domain_scores_gemma":[0.99732876,0.0014430238,0.0002967551,0.00042843606,0.00040589555,0.000097150885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095616636,0.0010395458,0.0011865925,0.0020693128,0.000432038,0.0010087164,0.001514169,0.0014890693,0.0016204547],"category_scores_gemma":[0.004835549,0.000393007,0.0018794431,0.0021373152,0.00036174073,0.002071434,0.00065837987,0.0013193124,0.0012278792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014153165,0.0007956837,0.054389838,0.00092593976,0.0013084408,0.0007374179,0.0015697867,0.098744586,0.025141379,0.008705197,0.031777054,0.7744893],"study_design_scores_gemma":[0.000078801175,0.00013167397,0.0060968315,0.000036744812,0.00016175029,0.0002244659,0.00009874997,0.9775882,0.0025622519,0.008649897,0.0043262686,0.000044406395],"about_ca_topic_score_codex":0.013260941,"about_ca_topic_score_gemma":0.030338325,"teacher_disagreement_score":0.013260941,"about_ca_system_score_codex":0.00069324166,"about_ca_system_score_gemma":0.0005186937,"threshold_uncertainty_score":0.026367486},"labels":[],"label_agreement":null},{"id":"W4385950072","doi":"10.1504/ijenm.2023.10058459","title":"Opinion mining of customers reviews using new Jaccard dissimilarity kernel function","year":2023,"lang":"en","type":"article","venue":"International Journal of Enterprise Network Management","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Jaccard index; Function (biology); Kernel (algebra); Computer science; Information retrieval; Data mining; Artificial intelligence; Mathematics; Pattern recognition (psychology); Biology; Combinatorics","score_opus":0.04475662754013013,"score_gpt":0.3246377396004022,"score_spread":0.2798811120602721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385950072","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37737092,0.00091112824,0.61664176,0.00041496105,0.00020203789,0.00021487735,0.00050561497,0.00044172417,0.0032969578],"genre_scores_gemma":[0.9176175,0.00029223957,0.079236224,0.000059547972,0.00015929573,0.00009987122,0.00077561685,0.00003184341,0.0017279495],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986461,0.000278348,0.00014528466,0.00027930815,0.0005419406,0.000109107146],"domain_scores_gemma":[0.99821883,0.00043431492,0.00023365782,0.00010582223,0.0009375157,0.00006987169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009937155,0.0004838117,0.0008236913,0.002408678,0.00044318844,0.0010453624,0.00063317275,0.0006387128,0.0007121202],"category_scores_gemma":[0.0048319497,0.00013780658,0.0008608777,0.0019832642,0.0002711828,0.0011802482,0.00039872705,0.0005427566,0.00034203217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010931271,0.00093494717,0.06956844,0.0005955771,0.0007110723,0.00091371784,0.0009292085,0.091919206,0.0643799,0.019203298,0.010784287,0.73896724],"study_design_scores_gemma":[0.000017433365,0.00011094989,0.014147995,0.000009486658,0.000045834102,0.0001860973,0.00009198815,0.9751356,0.0056658913,0.002170936,0.0023872952,0.000030550036],"about_ca_topic_score_codex":0.0019967544,"about_ca_topic_score_gemma":0.0014220021,"teacher_disagreement_score":0.002408678,"about_ca_system_score_codex":0.00067464466,"about_ca_system_score_gemma":0.00044119993,"threshold_uncertainty_score":0.0052553415},"labels":[],"label_agreement":null},{"id":"W4386092100","doi":"10.1504/ijenm.2023.132966","title":"Opinion mining of customers reviews using new Jaccard dissimilarity kernel function","year":2023,"lang":"en","type":"article","venue":"International Journal of Enterprise Network Management","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Jaccard index; Computer science; Support vector machine; Artificial intelligence; Machine learning; Kernel (algebra); Data mining; Popularity; AKA; Sentiment analysis; Information retrieval; Pattern recognition (psychology); Mathematics","score_opus":0.04475662754013013,"score_gpt":0.3246377396004022,"score_spread":0.2798811120602721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386092100","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37737092,0.00091112824,0.61664176,0.00041496105,0.00020203789,0.00021487735,0.00050561497,0.00044172417,0.0032969578],"genre_scores_gemma":[0.9176175,0.00029223957,0.079236224,0.000059547972,0.00015929573,0.00009987122,0.00077561685,0.00003184341,0.0017279495],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986461,0.000278348,0.00014528466,0.00027930815,0.0005419406,0.000109107146],"domain_scores_gemma":[0.99821883,0.00043431492,0.00023365782,0.00010582223,0.0009375157,0.00006987169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009937155,0.0004838117,0.0008236913,0.002408678,0.00044318844,0.0010453624,0.00063317275,0.0006387128,0.0007121202],"category_scores_gemma":[0.0048319497,0.00013780658,0.0008608777,0.0019832642,0.0002711828,0.0011802482,0.00039872705,0.0005427566,0.00034203217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010931271,0.00093494717,0.06956844,0.0005955771,0.0007110723,0.00091371784,0.0009292085,0.091919206,0.0643799,0.019203298,0.010784287,0.73896724],"study_design_scores_gemma":[0.000017433365,0.00011094989,0.014147995,0.000009486658,0.000045834102,0.0001860973,0.00009198815,0.9751356,0.0056658913,0.002170936,0.0023872952,0.000030550036],"about_ca_topic_score_codex":0.0019967544,"about_ca_topic_score_gemma":0.0014220021,"teacher_disagreement_score":0.002408678,"about_ca_system_score_codex":0.00067464466,"about_ca_system_score_gemma":0.00044119993,"threshold_uncertainty_score":0.0052553415},"labels":[],"label_agreement":null},{"id":"W4386384178","doi":"10.1007/978-3-031-36938-4_20","title":"Analyzing the Trends of Responses to COVID-19 Related Tweets from News Stations: An Analysis of Three Countries","year":2023,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University; Saint Mary's University","funders":"","keywords":"Popularity; Politics; Social media; Coronavirus disease 2019 (COVID-19); Advertising; Sentiment analysis; Political science; Test (biology); Geography; Computer science; Artificial intelligence; Business; Law","score_opus":0.19505981489530397,"score_gpt":0.42249035038510385,"score_spread":0.22743053548979988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386384178","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99182796,0.00016867986,0.00032368486,0.00014447424,0.0000142730805,0.000010557503,0.004763441,0.000029844037,0.0027169876],"genre_scores_gemma":[0.9889389,0.00022234237,0.00058161945,0.000043908673,0.000023707087,0.000021692715,0.008146398,0.000019431636,0.0020020015],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99970895,0.00007038648,0.000029336077,0.000059654587,0.000067720684,0.00006398783],"domain_scores_gemma":[0.9979095,0.00093999953,0.0005002349,0.00007483538,0.0004427049,0.0001327217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045431484,0.00020620902,0.00018346871,0.0021036894,0.00033482973,0.0011048139,0.00030334626,0.00036408225,0.0017320812],"category_scores_gemma":[0.0018822718,0.00013377519,0.00031979178,0.004090532,0.00020332266,0.00069590233,0.0004736902,0.0003999458,0.00082097744],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045219602,0.00010283854,0.93963516,0.00021815687,0.0002300845,0.0003701446,0.0039790333,0.0018843595,0.0057929745,0.0009089757,0.007900598,0.038525365],"study_design_scores_gemma":[0.0000039666734,0.00006972463,0.9844545,0.00001956465,0.000074492506,0.00010265447,0.0058157058,0.0031705091,0.001178813,0.00012057025,0.0049768137,0.000012607001],"about_ca_topic_score_codex":0.012848897,"about_ca_topic_score_gemma":0.017256495,"teacher_disagreement_score":0.012848897,"about_ca_system_score_codex":0.00039396095,"about_ca_system_score_gemma":0.00024375775,"threshold_uncertainty_score":0.02554822},"labels":[],"label_agreement":null},{"id":"W4386497984","doi":"10.1287/msom.2021.0531","title":"Selecting Cover Images for Restaurant Reviews: AI vs. Wisdom of the Crowd","year":2023,"lang":"en","type":"article","venue":"Manufacturing & Service Operations Management","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; McGill University","funders":"","keywords":"Computer science; Context (archaeology); Scarcity; Cover (algebra); Artificial intelligence; Field (mathematics); Machine learning; Exploit; Data science; World Wide Web; Computer security","score_opus":0.022591984908525438,"score_gpt":0.2800979030675454,"score_spread":0.25750591815901996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386497984","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6201727,0.0071717175,0.31710917,0.021229383,0.0011427444,0.00243795,0.0014531414,0.0023821588,0.02690106],"genre_scores_gemma":[0.9409268,0.0004589361,0.05328861,0.0013157276,0.0005484439,0.00030990154,0.0004435821,0.000073308795,0.0026346005],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98915136,0.0053788805,0.0005111057,0.0024789392,0.002055546,0.0004242365],"domain_scores_gemma":[0.9524449,0.034846615,0.0042975154,0.0024671492,0.004322081,0.0016218404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011999134,0.0013626555,0.0021206592,0.0025673686,0.0018655066,0.003670191,0.0020856159,0.0029596274,0.0027066306],"category_scores_gemma":[0.04347834,0.0006662739,0.0010172771,0.001574487,0.0018695893,0.005067801,0.0021673834,0.002140627,0.0012337022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0032796415,0.0024280392,0.11971203,0.0044649513,0.0012879015,0.0009494683,0.008360494,0.09938662,0.027435217,0.021095939,0.06270969,0.64889],"study_design_scores_gemma":[0.0003108021,0.000869652,0.048168506,0.00021494157,0.00030551452,0.00069190573,0.0035056523,0.8930446,0.009365891,0.030904038,0.012339825,0.00027869246],"about_ca_topic_score_codex":0.008617674,"about_ca_topic_score_gemma":0.006973104,"teacher_disagreement_score":0.011999134,"about_ca_system_score_codex":0.0022064017,"about_ca_system_score_gemma":0.0017643186,"threshold_uncertainty_score":0.063458204},"labels":[],"label_agreement":null},{"id":"W4386566452","doi":"10.18653/v1/2023.findings-eacl.136","title":"Best Practices in the Creation and Use of Emotion Lexicons","year":2023,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Lexicon; Set (abstract data type); Computer science; Sentiment analysis; Tracking (education); Work (physics); Emotion classification; Word (group theory); Cognitive psychology; Emotion detection; Artificial intelligence; Natural language processing; Psychology; Emotion recognition; Linguistics","score_opus":0.16832087426191697,"score_gpt":0.3703668228333954,"score_spread":0.20204594857147842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386566452","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0051963096,0.0021488527,0.937929,0.021018945,0.0006293591,0.0008154425,0.00036615456,0.0041297413,0.027766133],"genre_scores_gemma":[0.046018515,0.0017546581,0.94240713,0.0025050011,0.0002737448,0.0009554974,0.0007061416,0.001796433,0.0035828182],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.867516,0.08265209,0.014441771,0.009644687,0.023725234,0.002020246],"domain_scores_gemma":[0.74439603,0.11967238,0.008838125,0.07556939,0.048874088,0.0026499534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09473101,0.0020940169,0.0015839842,0.007937859,0.0030248885,0.023053817,0.006650418,0.0049224896,0.0059987656],"category_scores_gemma":[0.26523226,0.002188947,0.0024371475,0.0051136855,0.01184449,0.023168903,0.011041111,0.008967706,0.01087711],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003524768,0.00041058395,0.0032723635,0.0028702538,0.000331109,0.00053750345,0.030072402,0.0048233587,0.013120098,0.27241787,0.050378177,0.6214139],"study_design_scores_gemma":[0.00014044839,0.00013247237,0.0017391648,0.004260207,0.00020556797,0.0009757338,0.0085209375,0.022091266,0.021191558,0.5093792,0.43099666,0.0003666304],"about_ca_topic_score_codex":0.003465096,"about_ca_topic_score_gemma":0.003951462,"teacher_disagreement_score":0.09473101,"about_ca_system_score_codex":0.0037285164,"about_ca_system_score_gemma":0.004392107,"threshold_uncertainty_score":0.50099146},"labels":[],"label_agreement":null},{"id":"W4386576789","doi":"10.18653/v1/2023.findings-eacl.125","title":"On the Role of Reviewer Expertise in Temporal Review Helpfulness Prediction","year":2023,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Helpfulness; Computer science; Scarcity; Quality (philosophy); Data science; Focus (optics); Value (mathematics); Key (lock); Psychology; Machine learning; Computer security","score_opus":0.02892292897469124,"score_gpt":0.27639565641839786,"score_spread":0.24747272744370663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386576789","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8197378,0.025747323,0.12277095,0.004417643,0.0011442141,0.0004915068,0.012312916,0.003060572,0.01031711],"genre_scores_gemma":[0.9408431,0.0019373406,0.04220997,0.00040368616,0.0010999715,0.00015536943,0.010059982,0.00014305947,0.0031474598],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9953544,0.0018435993,0.00041137714,0.0013466905,0.00080896687,0.00023495425],"domain_scores_gemma":[0.9384059,0.043180227,0.006933539,0.0023391491,0.007650765,0.0014905158],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01060332,0.0013105146,0.0011152544,0.007355809,0.001077225,0.002232213,0.0011623645,0.0018194035,0.0010854634],"category_scores_gemma":[0.04077941,0.00041694893,0.00076448655,0.0034666555,0.00057194685,0.0032583943,0.00081597886,0.0013705597,0.001350603],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014598578,0.00075082714,0.5143402,0.0014504517,0.00059648085,0.00095150707,0.0015135437,0.046833124,0.01244611,0.0033146914,0.06964908,0.34669417],"study_design_scores_gemma":[0.00013718854,0.00038349826,0.094447725,0.00021577768,0.0002687246,0.0016299245,0.00040400153,0.86758417,0.007869797,0.0063809045,0.02053604,0.00014226265],"about_ca_topic_score_codex":0.008964346,"about_ca_topic_score_gemma":0.023141513,"teacher_disagreement_score":0.9893967,"about_ca_system_score_codex":0.0013069354,"about_ca_system_score_gemma":0.0016276125,"threshold_uncertainty_score":0.056076407},"labels":[],"label_agreement":null},{"id":"W4386641354","doi":"10.5430/wjel.v13n8p237","title":"News Coverage of Covid-19 and Swine Flu: A Corpus-Assisted Study","year":2023,"lang":"en","type":"article","venue":"World Journal of English Language","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pandemic; Guardian; Newspaper; Outbreak; Coronavirus disease 2019 (COVID-19); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); History; Geography; Political science; Medicine; Business; Disease; Virology; Advertising; Infectious disease (medical specialty); Pathology; Law","score_opus":0.02438795991263113,"score_gpt":0.30038898450815077,"score_spread":0.2760010245955196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386641354","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9782361,0.0007221653,0.0010425564,0.0003413869,0.00011162901,0.00016534587,0.012409394,0.0000550296,0.006916396],"genre_scores_gemma":[0.95199686,0.0011892278,0.0058173416,0.00018423355,0.00017971503,0.0006245523,0.035451375,0.0000871466,0.004469431],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.99912566,0.00035875215,0.00013218285,0.00013893117,0.0001850686,0.000059350998],"domain_scores_gemma":[0.98908037,0.00787687,0.0010192918,0.00036672587,0.0014082454,0.00024840966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013585233,0.00022177705,0.00027132608,0.006262411,0.0011108726,0.001405124,0.00029745835,0.00052489596,0.003335569],"category_scores_gemma":[0.0079657845,0.00015443566,0.00024472462,0.006965807,0.0007285866,0.0012353642,0.0011184488,0.0005488304,0.00085509976],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019199312,0.0016942397,0.41540265,0.007955852,0.00037990592,0.009965796,0.19737208,0.0017783255,0.043487612,0.0063965125,0.07082572,0.2428214],"study_design_scores_gemma":[0.000093198,0.00031807378,0.78422016,0.0006355815,0.00030912203,0.0030190018,0.0824846,0.0069402116,0.0054776235,0.00048573018,0.11591892,0.000097744734],"about_ca_topic_score_codex":0.0077663544,"about_ca_topic_score_gemma":0.01275708,"teacher_disagreement_score":0.0077663544,"about_ca_system_score_codex":0.00073726167,"about_ca_system_score_gemma":0.0006119186,"threshold_uncertainty_score":0.015442312},"labels":[],"label_agreement":null},{"id":"W4386753352","doi":"10.21203/rs.3.rs-3343151/v1","title":"Homogenous Ensemble Boosting Approach to Improve the Consistency in the Accuracy of Text Data Classification","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; New Brunswick Innovation Foundation","keywords":"Sentiment analysis; Computer science; Consistency (knowledge bases); Artificial intelligence; Globe; The Internet; Machine learning; Boosting (machine learning); Unstructured data; Data science; Natural language processing; Data mining; Big data; World Wide Web","score_opus":0.36894048068499774,"score_gpt":0.44562259091701756,"score_spread":0.07668211023201982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386753352","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13106678,0.001566779,0.8614131,0.00046710897,0.00037111415,0.00017636319,0.00021410867,0.0013219283,0.0034026778],"genre_scores_gemma":[0.82064927,0.00038117624,0.17511782,0.00031233355,0.00034787646,0.0001304849,0.00064381893,0.00012496446,0.0022921977],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979054,0.00069462304,0.00016841233,0.00045433012,0.00054432306,0.000232914],"domain_scores_gemma":[0.99504906,0.001677962,0.00029091115,0.0006201963,0.0021995709,0.00016231873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060708923,0.0009299408,0.0023903889,0.0015867823,0.00081026374,0.0012247628,0.0019539013,0.0011881946,0.001430233],"category_scores_gemma":[0.008718853,0.00036181952,0.0011741902,0.001236452,0.00046350205,0.0015278006,0.001037602,0.0013836385,0.0008106974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00086588395,0.00068320526,0.013535144,0.00016565638,0.00043324725,0.00022069714,0.00027663624,0.3322865,0.02159481,0.005265501,0.008457698,0.61621505],"study_design_scores_gemma":[0.000007688519,0.000052359013,0.00075254537,0.000006749411,0.00003496358,0.000019688352,0.000014670976,0.9959829,0.001722669,0.00090935756,0.0004914984,0.0000048816605],"about_ca_topic_score_codex":0.003011832,"about_ca_topic_score_gemma":0.0028589405,"teacher_disagreement_score":0.0060708923,"about_ca_system_score_codex":0.0006333484,"about_ca_system_score_gemma":0.0009821693,"threshold_uncertainty_score":0.03210634},"labels":[],"label_agreement":null},{"id":"W4386816371","doi":"10.23977/tracam.2023.030102","title":"Methods of Analysis of Amazon Product Reviews and Rating Prediction","year":2023,"lang":"en","type":"article","venue":"Transactions on Computational and Applied Mathematics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick; University of Toronto","funders":"","keywords":"Sentiment analysis; Computer science; Product (mathematics); Data science; Semantic analysis (machine learning); New product development; Information retrieval; Data mining; Artificial intelligence; Marketing; Business","score_opus":0.0534949310274284,"score_gpt":0.3384569325872421,"score_spread":0.28496200155981366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386816371","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034966137,0.0013593616,0.95249325,0.0005312711,0.00021106322,0.0003463809,0.0024019787,0.0015173571,0.006173197],"genre_scores_gemma":[0.44516844,0.0012585273,0.53983784,0.00015969182,0.0005061534,0.00069014856,0.004310976,0.0002221098,0.007846099],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99595,0.0012393324,0.00028637366,0.00087531866,0.001470575,0.00017844215],"domain_scores_gemma":[0.99346465,0.00268691,0.00074134103,0.00065792934,0.0023586096,0.00009050791],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029500816,0.0010942103,0.0008339464,0.0036440073,0.00040415712,0.0018381762,0.0012092129,0.0006975925,0.0024200813],"category_scores_gemma":[0.015533699,0.00043610943,0.0015656145,0.0026798116,0.00034355553,0.0014744175,0.0006082898,0.00085623394,0.0021440662],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003337296,0.00029020492,0.03921144,0.00072518067,0.00053708325,0.0004670136,0.00046113008,0.06853618,0.0159511,0.03854342,0.020443667,0.81449986],"study_design_scores_gemma":[0.000015423951,0.00006749761,0.015446374,0.00004500197,0.00006992066,0.00024301678,0.00010489568,0.9561963,0.0052423165,0.012751587,0.009766263,0.00005138965],"about_ca_topic_score_codex":0.0073287254,"about_ca_topic_score_gemma":0.0068205786,"teacher_disagreement_score":0.0073287254,"about_ca_system_score_codex":0.0006901622,"about_ca_system_score_gemma":0.0011025125,"threshold_uncertainty_score":0.015601754},"labels":[],"label_agreement":null},{"id":"W4386939645","doi":"10.23977/aetp.2023.071205","title":"Accuracy analysis of university authority evaluation based on Chinese university data + neural algorithm","year":2023,"lang":"en","type":"article","venue":"Advances in Educational Technology and Psychology","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Quality (philosophy); Computer science; Training (meteorology); Artificial neural network; Sentiment analysis; Higher education; Value (mathematics); Training set; Public opinion; Data science; Knowledge management; Artificial intelligence; Machine learning; Political science","score_opus":0.04142802220301396,"score_gpt":0.39865452381400485,"score_spread":0.3572265016109909,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386939645","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8887528,0.0015618183,0.09413546,0.00046368092,0.00034108528,0.0001395311,0.0017208081,0.0013383001,0.01154645],"genre_scores_gemma":[0.9876489,0.0002066982,0.0085648205,0.00002175896,0.00006611942,0.000036042016,0.0015544706,0.000025803896,0.0018753106],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979997,0.0004176012,0.00028002055,0.00042798897,0.0006425742,0.00023207859],"domain_scores_gemma":[0.99717903,0.0009055485,0.00021734282,0.00026809808,0.0013363791,0.00009366002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024959429,0.0006647054,0.00087377307,0.0035524853,0.0007148474,0.0014190273,0.0006770061,0.0006824242,0.0021141586],"category_scores_gemma":[0.008911949,0.00013765162,0.00077452,0.0023017325,0.00027860995,0.0012234662,0.00051079784,0.00044614435,0.00077613874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014608508,0.00037047116,0.1527806,0.00040397118,0.00033542133,0.00034670762,0.0002886311,0.15930176,0.007722088,0.00244521,0.012162396,0.66238195],"study_design_scores_gemma":[0.000018737273,0.00006775556,0.031440113,0.000017762302,0.00007273805,0.000073326584,0.00011242953,0.96277404,0.0038696486,0.00048331643,0.0010484218,0.00002172606],"about_ca_topic_score_codex":0.025283609,"about_ca_topic_score_gemma":0.011898646,"teacher_disagreement_score":0.025283609,"about_ca_system_score_codex":0.0013573018,"about_ca_system_score_gemma":0.0009984463,"threshold_uncertainty_score":0.050272882},"labels":[],"label_agreement":null},{"id":"W4387042344","doi":"10.1109/access.2023.3319455","title":"TEmoX: Classification of Textual Emotion Using Ensemble of Transformers","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Engineering and Technology, Lahore","keywords":"Bengali; Artificial intelligence; Computer science; Natural language processing; Sentiment analysis; Disgust; Categorization; Classifier (UML); Machine learning; Transformer; Anger; Information retrieval; Psychology","score_opus":0.125216788340946,"score_gpt":0.3655042593502999,"score_spread":0.2402874710093539,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387042344","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6091421,0.0013015699,0.37209406,0.00038471873,0.00035325045,0.0002570351,0.0018564976,0.0072541963,0.007356473],"genre_scores_gemma":[0.93101066,0.0003490146,0.058519166,0.00007752962,0.000056821176,0.00009386749,0.003689656,0.0001173035,0.006085939],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997247,0.000052232637,0.000018623126,0.00008629184,0.00006699771,0.000051147388],"domain_scores_gemma":[0.99961394,0.000117018026,0.00002430301,0.00004461124,0.00017303778,0.000027158574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008398804,0.0009368427,0.00046941638,0.0008741948,0.00024440122,0.0005742624,0.0006037911,0.00041236274,0.0014805843],"category_scores_gemma":[0.0013483352,0.00016923882,0.0007384074,0.00047566,0.00015607191,0.00097041926,0.00063844136,0.00072858937,0.0008876024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010405515,0.00032421117,0.01876365,0.00012419227,0.00025865235,0.00021072513,0.0003100756,0.06429552,0.046793647,0.0012287182,0.008928044,0.8577219],"study_design_scores_gemma":[0.0000174638,0.000265845,0.010013129,0.000013147253,0.000100763464,0.000120419936,0.0002025235,0.96505994,0.021338372,0.0008065941,0.0020397443,0.000022106928],"about_ca_topic_score_codex":0.004298341,"about_ca_topic_score_gemma":0.0047654402,"teacher_disagreement_score":0.004298341,"about_ca_system_score_codex":0.0005366742,"about_ca_system_score_gemma":0.00032613653,"threshold_uncertainty_score":0.008546591},"labels":[],"label_agreement":null},{"id":"W4387099847","doi":"10.54254/2755-2721/6/20230831","title":"Sentiment analysis of Amazon product reviews","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Random forest; Naive Bayes classifier; Computer science; Sentiment analysis; Amazon rainforest; Support vector machine; Product (mathematics); Recall; Machine learning; Artificial intelligence; tf–idf; Security token; Precision and recall; Bayes' theorem; Data science; Data mining; Term (time); Information retrieval; Bayesian probability; Psychology; Mathematics; Computer security; Cognitive psychology","score_opus":0.015808690560455342,"score_gpt":0.2420592292557835,"score_spread":0.22625053869532816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387099847","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97926563,0.0009719868,0.007332849,0.00036121233,0.00016024755,0.0001235742,0.004763301,0.00037255333,0.006648715],"genre_scores_gemma":[0.9837763,0.00032587248,0.009119447,0.000066053966,0.00009158701,0.000058140846,0.0044768834,0.000031342657,0.0020544112],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989741,0.00023540489,0.00010554105,0.00011869762,0.00048639992,0.00007992301],"domain_scores_gemma":[0.99731135,0.00057860475,0.00032516287,0.00008146208,0.0016464747,0.000056976365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008411402,0.00032476263,0.000343712,0.0013266387,0.00021365884,0.000482301,0.00014899312,0.00018529022,0.0007733496],"category_scores_gemma":[0.004366255,0.000091466,0.00039957365,0.0010699781,0.00009223466,0.0003582648,0.00014751994,0.00018361451,0.0005190344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022488632,0.00035287795,0.27939162,0.0014502363,0.00055662263,0.0012031853,0.0013372172,0.0091105765,0.069029704,0.0017331203,0.058274124,0.57531196],"study_design_scores_gemma":[0.000057776324,0.0005958781,0.6854237,0.00012827214,0.00027958307,0.001262577,0.001345958,0.24451584,0.038610455,0.0009977187,0.02668495,0.000097299846],"about_ca_topic_score_codex":0.004786539,"about_ca_topic_score_gemma":0.005865875,"teacher_disagreement_score":0.004786539,"about_ca_system_score_codex":0.00038220247,"about_ca_system_score_gemma":0.0002706556,"threshold_uncertainty_score":0.009517372},"labels":[],"label_agreement":null},{"id":"W4387171550","doi":"10.3233/faia230643","title":"Building a Few-Shot Cross-Domain Multilingual NLU Model for Customer Care","year":2023,"lang":"en","type":"book-chapter","venue":"Frontiers in artificial intelligence and applications","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Classifier (UML); Sentence; Bottleneck; Artificial intelligence; Domain (mathematical analysis); Natural language processing; Language model; Labeled data","score_opus":0.09383101615275154,"score_gpt":0.3602005779507071,"score_spread":0.26636956179795557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387171550","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18862948,0.0023404395,0.782699,0.0020004334,0.00027836338,0.00035107747,0.004057443,0.01401235,0.0056313677],"genre_scores_gemma":[0.709515,0.0006669275,0.25209847,0.0014942499,0.00016825803,0.0004934282,0.018212233,0.0005708819,0.016780656],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996332,0.000063910345,0.000018953975,0.00017966285,0.00004793978,0.000056409768],"domain_scores_gemma":[0.9995604,0.00021776279,0.000029121862,0.00006469711,0.00009645395,0.000031674765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006707017,0.0009051317,0.0008474067,0.00081122323,0.00068498,0.0009342453,0.0015989824,0.0019285593,0.002439632],"category_scores_gemma":[0.0018341307,0.0004980505,0.0012066955,0.00071040494,0.00055052846,0.0017676188,0.0012106565,0.0025718343,0.00228506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008195835,0.0008377392,0.008722287,0.00037790777,0.0002465896,0.0012052055,0.0008924563,0.35776362,0.020452095,0.0069251517,0.035877917,0.5658795],"study_design_scores_gemma":[0.000015309157,0.00005187375,0.00047694,0.000011977396,0.00002174565,0.000085154636,0.00006740437,0.99279976,0.0019101368,0.0025285792,0.0020180098,0.000013065913],"about_ca_topic_score_codex":0.018153323,"about_ca_topic_score_gemma":0.028920755,"teacher_disagreement_score":0.018153323,"about_ca_system_score_codex":0.0011137181,"about_ca_system_score_gemma":0.0014127803,"threshold_uncertainty_score":0.03609532},"labels":[],"label_agreement":null},{"id":"W4387449659","doi":"10.1007/s12652-023-04712-8","title":"Emotion detection and semantic trends during COVID-19 social isolation using artificial intelligence techniques","year":2023,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Novelty; Computer science; Feeling; Emotion detection; Sentiment analysis; Affect (linguistics); Isolation (microbiology); Data science; Artificial intelligence; Pipeline (software); Computational intelligence; Cognitive psychology; Psychology; Emotion recognition; Social psychology","score_opus":0.09582393646922119,"score_gpt":0.35266096024663396,"score_spread":0.2568370237774128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387449659","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9814198,0.00024241216,0.007925688,0.00037908574,0.00014946377,0.00006299754,0.0012087496,0.00013091767,0.008480849],"genre_scores_gemma":[0.99483216,0.00007063389,0.0024851642,0.000038766346,0.000055390174,0.000028184699,0.0010575901,0.00002146503,0.0014107034],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99952924,0.000079963465,0.00003791419,0.00010454815,0.00015108178,0.00009725202],"domain_scores_gemma":[0.9983388,0.00047206954,0.00027258138,0.00009351908,0.00064165366,0.0001813525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005909979,0.00025908512,0.00025737376,0.0013190943,0.00083665136,0.0013520103,0.00033268324,0.00038789745,0.002185726],"category_scores_gemma":[0.0037455221,0.00007926833,0.00021971234,0.0012241132,0.00031153165,0.0011964331,0.0009403191,0.00056570634,0.00066593866],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002528931,0.00069846236,0.49899608,0.00049800164,0.0002680097,0.002313286,0.014096012,0.00633229,0.08672777,0.009384123,0.020002391,0.35815465],"study_design_scores_gemma":[0.000029416738,0.0004551531,0.8158494,0.00008876828,0.00015523762,0.0007285294,0.022424065,0.11367124,0.01439345,0.006257338,0.025859382,0.00008799395],"about_ca_topic_score_codex":0.00515319,"about_ca_topic_score_gemma":0.007945857,"teacher_disagreement_score":0.00515319,"about_ca_system_score_codex":0.00045896735,"about_ca_system_score_gemma":0.00036169952,"threshold_uncertainty_score":0.010246396},"labels":[],"label_agreement":null},{"id":"W4387645871","doi":"10.23977/acss.2023.070808","title":"Research on Semantic Analysis-Based Recognition of Telecommunication Fraud Discourse Patterns","year":2023,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Lanzhou University","keywords":"Computer science; Semantic analysis (machine learning); Data science; Computer security; Artificial intelligence","score_opus":0.10092583928511131,"score_gpt":0.4037060694978501,"score_spread":0.3027802302127388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387645871","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3748895,0.016390186,0.5042063,0.016433537,0.0010413431,0.00057971186,0.00084591587,0.0005982873,0.085015275],"genre_scores_gemma":[0.85445684,0.005590792,0.13427155,0.0008201231,0.00047559987,0.00020435672,0.0007742657,0.000103413324,0.003303039],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9950034,0.0025041278,0.00046318173,0.0006194109,0.0011944452,0.00021537968],"domain_scores_gemma":[0.9748214,0.01737593,0.0029965946,0.001250114,0.0032481556,0.0003078185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048684753,0.0006145993,0.0004404322,0.010239681,0.0012498981,0.0059112622,0.00085512706,0.001342477,0.0020205285],"category_scores_gemma":[0.025720071,0.000318376,0.00072345324,0.006984723,0.0031955221,0.014818584,0.0012896286,0.0015771444,0.0006417356],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032975708,0.00037792744,0.030758077,0.001821897,0.00018202914,0.0005102588,0.030599404,0.0048201145,0.017844459,0.29467785,0.007125254,0.610953],"study_design_scores_gemma":[0.00006608751,0.00035130276,0.064839415,0.0027701429,0.0002946644,0.0029347395,0.073071264,0.23014188,0.028277796,0.44455513,0.1523416,0.00035588254],"about_ca_topic_score_codex":0.0020172468,"about_ca_topic_score_gemma":0.0012982881,"teacher_disagreement_score":0.010239681,"about_ca_system_score_codex":0.0016888679,"about_ca_system_score_gemma":0.0020280008,"threshold_uncertainty_score":0.0257473},"labels":[],"label_agreement":null},{"id":"W4387717450","doi":"10.1109/tce.2023.3325335","title":"A Dual Channel Cyber–Physical Transportation Network for Detecting Traffic Incidents and Driver Emotion","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Consumer Electronics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Graph; Dual (grammatical number); Traffic congestion; Intelligent transportation system; Attention network; Channel (broadcasting); Artificial intelligence; Computer network; Theoretical computer science; Transport engineering; Engineering","score_opus":0.018352948328230253,"score_gpt":0.26271108855694936,"score_spread":0.2443581402287191,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387717450","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3344201,0.0033610114,0.6271525,0.003081051,0.00096307223,0.00035589066,0.0064715412,0.0053287083,0.018866107],"genre_scores_gemma":[0.92677927,0.00090604916,0.054624047,0.00043680795,0.00026965703,0.00017460634,0.005756245,0.00007562318,0.010977708],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99978596,0.00003924877,0.0000074457967,0.00008617196,0.000041145544,0.000040115665],"domain_scores_gemma":[0.9997516,0.00008691136,0.00002645533,0.000022136004,0.00009518566,0.000017689645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003134293,0.0008675155,0.00039214082,0.0012811439,0.00030959203,0.00050971104,0.0009789817,0.00074396975,0.0018140122],"category_scores_gemma":[0.0009451723,0.00022607477,0.000612322,0.0009413164,0.00023852692,0.0012105752,0.00071304815,0.00083393406,0.0007758477],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012620912,0.00077757216,0.026075691,0.0003778936,0.00024750934,0.0004665202,0.000192397,0.26886097,0.016403409,0.008170668,0.032927867,0.6442374],"study_design_scores_gemma":[0.000010303301,0.00006559399,0.0045969984,0.000010136658,0.000060372262,0.00007831631,0.00003389229,0.9869098,0.0020679026,0.0031941477,0.0029597082,0.000012805836],"about_ca_topic_score_codex":0.01511922,"about_ca_topic_score_gemma":0.025839938,"teacher_disagreement_score":0.01511922,"about_ca_system_score_codex":0.0010395363,"about_ca_system_score_gemma":0.00040902765,"threshold_uncertainty_score":0.030062437},"labels":[],"label_agreement":null},{"id":"W4387814951","doi":"10.1145/3606039","title":"Proceedings of the 4th on Multimodal Sentiment Analysis Challenge and Workshop: Mimicked Emotions, Humour and Personalisation","year":2023,"lang":"en","type":"paratext","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sentiment analysis; Disappointment; Personalization; Valence (chemistry); Computer science; Multimedia; Psychology; World Wide Web; Artificial intelligence; Social psychology; Chemistry","score_opus":0.03358999919223172,"score_gpt":0.28041984719778046,"score_spread":0.24682984800554875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387814951","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15640943,0.013395388,0.49410295,0.097633906,0.05436029,0.003857222,0.042087838,0.017631866,0.12052117],"genre_scores_gemma":[0.30808997,0.0058205384,0.22844446,0.010098939,0.011515095,0.0046486435,0.09587101,0.005288079,0.33022332],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99722606,0.0013638412,0.000102557584,0.00043927765,0.0006777825,0.00019049375],"domain_scores_gemma":[0.99201995,0.0032339573,0.00013441472,0.00083744666,0.0022894724,0.0014847191],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00599907,0.0015198744,0.0012600188,0.0005731362,0.0015746685,0.0047373334,0.0016884579,0.0024260057,0.018342685],"category_scores_gemma":[0.013804269,0.00031305844,0.0010976938,0.00052655913,0.0011281617,0.0034050276,0.0037972091,0.003583977,0.009855736],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063114974,0.00048029007,0.00094353384,0.00047408015,0.000098418765,0.00035311191,0.0012037659,0.0033337134,0.007636388,0.0031311533,0.83585906,0.14585531],"study_design_scores_gemma":[0.00022270912,0.0006030587,0.008667386,0.00035169866,0.00010656422,0.0005074918,0.0024723627,0.07645449,0.0133690415,0.01948554,0.877553,0.00020665205],"about_ca_topic_score_codex":0.0065843584,"about_ca_topic_score_gemma":0.017355135,"teacher_disagreement_score":0.018342685,"about_ca_system_score_codex":0.0014599082,"about_ca_system_score_gemma":0.0014259714,"threshold_uncertainty_score":0.061362386},"labels":[],"label_agreement":null},{"id":"W4387896315","doi":"10.54254/2755-2721/13/20230739","title":"Ethnic minorities' mentality and homosexuality psychology in literature: A text emotion analysis with NRC lexicon","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Lexicon; Ethnic group; Character (mathematics); Linguistics; Natural (archaeology); Homosexuality; Psychology; Pragmatics; Artificial intelligence; Computer science; Sociology; History; Anthropology; Psychoanalysis; Philosophy","score_opus":0.017784160912183836,"score_gpt":0.27706158207172066,"score_spread":0.25927742115953684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387896315","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8779218,0.0011099377,0.076869145,0.0010064198,0.00022710439,0.001099995,0.015788604,0.0037541424,0.022222701],"genre_scores_gemma":[0.88187486,0.00042941753,0.09214602,0.00017015771,0.00006991455,0.0007944188,0.01962863,0.00023854391,0.0046480354],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99954563,0.00012006997,0.00007145637,0.00009858252,0.00012119139,0.000042961634],"domain_scores_gemma":[0.99867046,0.00059091044,0.0001249983,0.00008877225,0.0004594023,0.00006549227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053823006,0.00037771714,0.00025594566,0.0034806232,0.0007435555,0.0012853808,0.00029799252,0.0003467972,0.0031435366],"category_scores_gemma":[0.0028786573,0.00014327544,0.00046764375,0.002144401,0.0004097637,0.0008836603,0.0007687053,0.0003822888,0.0015064598],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009742186,0.00069765607,0.11196635,0.0024253132,0.00021688192,0.0023377945,0.011942695,0.0022401428,0.16399594,0.006619678,0.05227953,0.6443038],"study_design_scores_gemma":[0.00020948537,0.00050801237,0.59090275,0.00061689573,0.00079372094,0.0034984658,0.022651209,0.15570721,0.07106823,0.0069795805,0.14679135,0.00027313447],"about_ca_topic_score_codex":0.011154523,"about_ca_topic_score_gemma":0.015156657,"teacher_disagreement_score":0.011154523,"about_ca_system_score_codex":0.00082832103,"about_ca_system_score_gemma":0.0011248726,"threshold_uncertainty_score":0.022179186},"labels":[],"label_agreement":null},{"id":"W4387914178","doi":"10.1109/codit58514.2023.10284166","title":"Sentiment Analysis Using Smoothed Probabilistic-Based Models","year":2023,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Sentiment analysis; Computer science; Probabilistic logic; Cluster analysis; Smoothing; Artificial intelligence; Latent Dirichlet allocation; Dirichlet distribution; Topic model; Statistical model; Machine learning; Hierarchical Dirichlet process; Natural language processing; Data mining; Mathematics","score_opus":0.07229150159583578,"score_gpt":0.30284218125441176,"score_spread":0.23055067965857598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387914178","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0064253765,0.00019593225,0.990453,0.00035951092,0.00006122276,0.000057303718,0.00019748912,0.00069588237,0.0015542562],"genre_scores_gemma":[0.45142362,0.0010267034,0.53335434,0.0007154862,0.0006036906,0.0005877529,0.0025713793,0.00045887072,0.0092581175],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998379,0.00051007594,0.000101526704,0.00042214774,0.0004958421,0.00009145802],"domain_scores_gemma":[0.9970169,0.0018034523,0.00026149742,0.0002986924,0.00056964305,0.000049773473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002335086,0.0009993779,0.0009876108,0.0022432394,0.0006807901,0.0020081124,0.0016123096,0.0012072439,0.0034836824],"category_scores_gemma":[0.009911999,0.00066315674,0.002203998,0.0018684898,0.00081524014,0.0034076497,0.0010577257,0.0020665172,0.0028243742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025259051,0.00016878775,0.0047600577,0.00025107077,0.00039470755,0.00017876794,0.00046931993,0.55041546,0.008430295,0.09757234,0.011989733,0.32511693],"study_design_scores_gemma":[0.000007711542,0.000016188344,0.00040063116,0.000009819476,0.000015695428,0.000029137627,0.000016171014,0.96033645,0.00050963834,0.03666536,0.0019794428,0.000013781991],"about_ca_topic_score_codex":0.0047868183,"about_ca_topic_score_gemma":0.0058206245,"teacher_disagreement_score":0.0047868183,"about_ca_system_score_codex":0.0010540576,"about_ca_system_score_gemma":0.001130591,"threshold_uncertainty_score":0.012349248},"labels":[],"label_agreement":null},{"id":"W4388513663","doi":"10.18280/ria.370519","title":"Effective Disaster Management Through Transformer-Based Multimodal Tweet Classification","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Emergency management; Computer science; Transformer; Natural language processing; Engineering; Political science; Electrical engineering","score_opus":0.05487605052266355,"score_gpt":0.3114893120081182,"score_spread":0.25661326148545466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388513663","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21977961,0.0014581311,0.76139855,0.0011474701,0.0005914903,0.00031932708,0.0020723164,0.005965218,0.007267896],"genre_scores_gemma":[0.9204621,0.00050345244,0.07110059,0.00021374926,0.00021578696,0.0001321066,0.0019169734,0.00007702785,0.005378167],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997727,0.000030449204,0.000016337173,0.00006439264,0.000059274906,0.00005691357],"domain_scores_gemma":[0.9997137,0.0000874544,0.000049746206,0.000031706706,0.00009072925,0.000026657386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031879728,0.00083596486,0.000539654,0.0013059742,0.00027868064,0.0005491213,0.00070379063,0.0005412922,0.0016840487],"category_scores_gemma":[0.0013691129,0.00019008988,0.00056031597,0.00077095156,0.00021899705,0.0011795739,0.0008672911,0.00083913404,0.001069434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009099754,0.00035560687,0.010026006,0.00022369347,0.000104662104,0.0005157769,0.0002496914,0.052971456,0.043689113,0.0018585145,0.014384749,0.8747108],"study_design_scores_gemma":[0.00002375262,0.00012290078,0.0042514326,0.000019294766,0.000051812756,0.00017628008,0.00021245924,0.97545546,0.0131340055,0.0032669362,0.0032560993,0.000029595254],"about_ca_topic_score_codex":0.0025742867,"about_ca_topic_score_gemma":0.0035062362,"teacher_disagreement_score":0.0025742867,"about_ca_system_score_codex":0.00033778712,"about_ca_system_score_gemma":0.0004582738,"threshold_uncertainty_score":0.005633652},"labels":[],"label_agreement":null},{"id":"W4388684128","doi":"10.2139/ssrn.4633292","title":"Objective and Neutral Summarization of Customer Reviews","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Automatic summarization; Computer science; Business; Information retrieval","score_opus":0.028541757325291794,"score_gpt":0.28862347223502655,"score_spread":0.26008171490973475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388684128","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5969224,0.0067777373,0.3566872,0.0022130182,0.0018662078,0.0007122615,0.010610738,0.0027607186,0.021449693],"genre_scores_gemma":[0.90243053,0.0012567521,0.076544,0.00013628072,0.0015887349,0.00020744251,0.009935566,0.000247526,0.00765326],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973878,0.0008984445,0.00029345366,0.00036814713,0.0008788801,0.00017322092],"domain_scores_gemma":[0.9919377,0.002218136,0.0010447989,0.00037818713,0.0042119985,0.00020910149],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027502629,0.00080319645,0.00080538454,0.003090043,0.0004489894,0.0025200022,0.00046106428,0.00057317776,0.002175583],"category_scores_gemma":[0.013155845,0.00018751506,0.00060775614,0.0017122747,0.00022188111,0.0015100668,0.000645417,0.00060980255,0.0015602955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023952615,0.00031616833,0.018352974,0.0013100128,0.0005813465,0.00029311204,0.0007717062,0.009836721,0.048601147,0.0045209127,0.03215422,0.8808664],"study_design_scores_gemma":[0.00020970471,0.0021863615,0.11658753,0.00041996816,0.001573324,0.00089049054,0.002262349,0.7235835,0.082992874,0.01938363,0.049657527,0.00025265335],"about_ca_topic_score_codex":0.0005847547,"about_ca_topic_score_gemma":0.0014606258,"teacher_disagreement_score":0.003090043,"about_ca_system_score_codex":0.0004968783,"about_ca_system_score_gemma":0.0006248956,"threshold_uncertainty_score":0.014544964},"labels":[],"label_agreement":null},{"id":"W4388773811","doi":"10.1016/j.eswa.2023.122582","title":"A machine learning tool for collecting and analyzing subjective road safety data from Twitter","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Support vector machine; Naive Bayes classifier; Random forest; Artificial intelligence; Machine learning; Crowdsourcing; Classifier (UML); Social media; World Wide Web","score_opus":0.05363648456002865,"score_gpt":0.3167575060949625,"score_spread":0.2631210215349339,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388773811","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20101772,0.00046212008,0.61565214,0.0012606948,0.00040935187,0.0034360606,0.09401327,0.07014266,0.013605963],"genre_scores_gemma":[0.2939183,0.00027852049,0.6374465,0.00037407948,0.00026673643,0.0031358325,0.05457849,0.00053606543,0.009465471],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987803,0.00021230352,0.00019697865,0.0002161927,0.00049954007,0.000094650924],"domain_scores_gemma":[0.9960198,0.0019249811,0.00042219216,0.00035020325,0.0010972073,0.00018555889],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001670942,0.00096252025,0.00077402964,0.0057084993,0.00085823063,0.0010728417,0.0007969167,0.0008010768,0.0042898776],"category_scores_gemma":[0.0063242903,0.00034169678,0.0005893821,0.003741586,0.00021169917,0.0018567425,0.0010382873,0.00080912415,0.004484753],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006458568,0.0013258979,0.053461745,0.0010281702,0.00036544979,0.00063598715,0.0011509213,0.008451971,0.057215035,0.0030224144,0.11289562,0.7598009],"study_design_scores_gemma":[0.00019689526,0.00077587843,0.08696429,0.00019219135,0.00031205642,0.00070602953,0.0015223835,0.75663584,0.060021125,0.009328162,0.0831401,0.00020510529],"about_ca_topic_score_codex":0.0038698434,"about_ca_topic_score_gemma":0.00900937,"teacher_disagreement_score":0.0057084993,"about_ca_system_score_codex":0.0005990501,"about_ca_system_score_gemma":0.0010516865,"threshold_uncertainty_score":0.01435101},"labels":[],"label_agreement":null},{"id":"W4388777264","doi":"10.2196/50150","title":"A Comparison of ChatGPT and Fine-Tuned Open Pre-Trained Transformers (OPT) Against Widely Used Sentiment Analysis Tools: Sentiment Analysis of COVID-19 Survey Data","year":2023,"lang":"en","type":"article","venue":"JMIR Mental Health","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":72,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Arthritis and Musculoskeletal and Skin Diseases; National Institutes of Health","keywords":"Sentiment analysis; Computer science; Social media; Context (archaeology); Data science; Sentence; Coronavirus disease 2019 (COVID-19); Artificial intelligence; Data mining; World Wide Web; Medicine","score_opus":0.19093777966228806,"score_gpt":0.4657761489750528,"score_spread":0.27483836931276473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388777264","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.56403357,0.003172616,0.3311362,0.0015189054,0.0017138597,0.0014673783,0.010225578,0.07687187,0.009860066],"genre_scores_gemma":[0.7860582,0.0005221931,0.18478149,0.00089582405,0.00018271954,0.0009097069,0.020440174,0.0014315604,0.0047781565],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970463,0.0011847981,0.00023631126,0.0008892293,0.00043895558,0.00020449124],"domain_scores_gemma":[0.99336654,0.0041165645,0.00030581295,0.00052592566,0.001410937,0.00027429842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053168936,0.0019781413,0.00077556865,0.0020853896,0.0007234628,0.0013897404,0.0015987214,0.0012142817,0.0032348912],"category_scores_gemma":[0.01891799,0.00044179463,0.0009828368,0.00088658975,0.00058602873,0.0026946086,0.002007606,0.001891873,0.002679975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0042819213,0.0010754986,0.03437414,0.0025109157,0.0006404236,0.0007200181,0.0021671152,0.042279374,0.036881026,0.0018470162,0.064106174,0.8091165],"study_design_scores_gemma":[0.0003779518,0.0015122845,0.02078464,0.00029396845,0.00024490716,0.0004815272,0.0020284185,0.9213839,0.03183194,0.0043713064,0.01653077,0.00015847161],"about_ca_topic_score_codex":0.0059059267,"about_ca_topic_score_gemma":0.009951436,"teacher_disagreement_score":0.0059059267,"about_ca_system_score_codex":0.0011259012,"about_ca_system_score_gemma":0.0013973588,"threshold_uncertainty_score":0.02811879},"labels":[],"label_agreement":null},{"id":"W4388946201","doi":"10.3389/friot.2023.1287832","title":"Analyzing public sentiments on the Cullen Commission inquiry into money laundering: harnessing deep learning in the AI of Things Era","year":2023,"lang":"en","type":"article","venue":"Frontiers in the Internet of Things","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Roads University","funders":"","keywords":"Commission; Sadness; Anger; Money laundering; Sentiment analysis; Scope (computer science); Recall; European commission; Psychology; Political science; Business; Artificial intelligence; Law; Social psychology; Computer science; Cognitive psychology","score_opus":0.032417488472857324,"score_gpt":0.2842503025579746,"score_spread":0.2518328140851173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388946201","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97299427,0.00025657678,0.0065325014,0.0019459046,0.00011868872,0.00006772289,0.0007927322,0.00021171429,0.017079843],"genre_scores_gemma":[0.9888032,0.00020329497,0.003723989,0.00022580047,0.00003503123,0.000023137121,0.00059141393,0.000028614295,0.0063655796],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994863,0.000119589175,0.000022036347,0.000051899955,0.00021339396,0.00010671301],"domain_scores_gemma":[0.9978955,0.0007062222,0.00041521792,0.00011592845,0.00072515977,0.0001420472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010355706,0.00036598422,0.00025468424,0.0010687109,0.0010062179,0.0017990685,0.0002722618,0.0004920963,0.0013747435],"category_scores_gemma":[0.003724995,0.0001138829,0.00016627263,0.0011408504,0.000549248,0.00092005194,0.00073648326,0.0008263982,0.0006591056],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013315196,0.0003616445,0.4597922,0.00048447918,0.00019371581,0.0024560816,0.015599266,0.014302902,0.04520714,0.00591728,0.030275892,0.42407784],"study_design_scores_gemma":[0.000026790434,0.00029612926,0.60305464,0.00038580125,0.0001762171,0.0004199207,0.060731493,0.21724646,0.037094574,0.00709061,0.0733007,0.00017660219],"about_ca_topic_score_codex":0.08807175,"about_ca_topic_score_gemma":0.18712391,"teacher_disagreement_score":0.91192824,"about_ca_system_score_codex":0.002057347,"about_ca_system_score_gemma":0.0013488522,"threshold_uncertainty_score":0.17511827},"labels":[],"label_agreement":null},{"id":"W4389111262","doi":"10.1145/3628454.3630041","title":"Framework for Choosing a Supervised Machine Learning Method for Classification Based on Object Categories : Classifying Subjectivity of Online Comments by Product Categories","year":2023,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Product (mathematics); Machine learning; Context (archaeology); Task (project management); Supervised learning; Object (grammar); Data science; Artificial neural network; Engineering; Mathematics; Systems engineering","score_opus":0.07898424644605456,"score_gpt":0.36123710302072226,"score_spread":0.2822528565746677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389111262","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005372926,0.0001489663,0.99206287,0.0005550617,0.000046321682,0.00038623,0.00011353626,0.000340344,0.0009736769],"genre_scores_gemma":[0.09688096,0.00014883735,0.8994367,0.00026943587,0.00018159485,0.0012938362,0.00038477153,0.00006164329,0.0013421876],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99105525,0.0049455124,0.00070681254,0.0012757983,0.0016991212,0.00031746505],"domain_scores_gemma":[0.9893055,0.0063907285,0.00079676625,0.0005139712,0.002702554,0.00029052963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01377383,0.0012825828,0.0013092334,0.005441068,0.0012211686,0.0028496764,0.002531086,0.00170537,0.002374868],"category_scores_gemma":[0.018133828,0.0004395221,0.0015580286,0.0021923876,0.00153265,0.002159032,0.001542534,0.0026608976,0.0019440453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038737108,0.0014580713,0.017238336,0.0008162017,0.00038870293,0.0003199164,0.0023360397,0.058296088,0.018656109,0.06431552,0.012278009,0.8235096],"study_design_scores_gemma":[0.000065848486,0.0003043073,0.00387709,0.00016829598,0.00007841462,0.00014935457,0.00058299035,0.92382336,0.0050715366,0.057868484,0.007930071,0.00008017517],"about_ca_topic_score_codex":0.0039630495,"about_ca_topic_score_gemma":0.005098365,"teacher_disagreement_score":0.01377383,"about_ca_system_score_codex":0.0015816868,"about_ca_system_score_gemma":0.0024197418,"threshold_uncertainty_score":0.07284385},"labels":[],"label_agreement":null},{"id":"W4389123750","doi":"10.2139/ssrn.4621982","title":"Generative AI and User-Generated Content: Evidence from Online Reviews","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Generative grammar; User-generated content; Content (measure theory); Computer science; Information retrieval; World Wide Web; Natural language processing; Artificial intelligence; Social media; Mathematics","score_opus":0.0850705682919215,"score_gpt":0.32042466124601926,"score_spread":0.23535409295409776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389123750","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9687062,0.0011141687,0.011582334,0.0011165545,0.000077746015,0.00016447016,0.0011795067,0.00020190029,0.015857177],"genre_scores_gemma":[0.9949189,0.0003218956,0.0028507223,0.00012211721,0.00006486074,0.000043639557,0.0007331426,0.000038251885,0.00090643385],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99663615,0.0020923773,0.00014129946,0.00026632522,0.00076964387,0.000094236115],"domain_scores_gemma":[0.8038527,0.1753388,0.0060947565,0.00482742,0.008954693,0.0009315484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004488165,0.00035953385,0.00043138058,0.0017915145,0.0005785476,0.0022064804,0.00071464514,0.001119086,0.0031792526],"category_scores_gemma":[0.06988979,0.0003096797,0.00038567864,0.0019185598,0.00085206755,0.0017283667,0.00051387417,0.0008831586,0.0011255302],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00426684,0.0016553922,0.54935277,0.002457068,0.0011231826,0.002652638,0.018317776,0.015966238,0.019061914,0.020960938,0.019944783,0.34424037],"study_design_scores_gemma":[0.00051644654,0.0015425475,0.6622651,0.00037661326,0.0006877662,0.0027537541,0.0056964313,0.24904293,0.011934061,0.036564585,0.028396953,0.00022288352],"about_ca_topic_score_codex":0.0024439471,"about_ca_topic_score_gemma":0.0029981008,"teacher_disagreement_score":0.004488165,"about_ca_system_score_codex":0.0005628705,"about_ca_system_score_gemma":0.00038265868,"threshold_uncertainty_score":0.023736},"labels":[],"label_agreement":null},{"id":"W4389139310","doi":"10.1504/ijpspm.2023.135035","title":"Analysing political opinions using machine learning","year":2023,"lang":"en","type":"article","venue":"International Journal of Public Sector Performance Management","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Leverage (statistics); Sentiment analysis; Newspaper; Social media; Politics; Computer science; Artificial intelligence; Data science; Natural language processing; World Wide Web; Political science; Sociology; Media studies","score_opus":0.06873278639001247,"score_gpt":0.3190122008834394,"score_spread":0.25027941449342694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389139310","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71392304,0.0011578352,0.24836142,0.0035499483,0.0003653999,0.00034915915,0.0031713783,0.00093376625,0.028188013],"genre_scores_gemma":[0.97080505,0.00031195433,0.024490014,0.00017593562,0.00021236221,0.000076182194,0.0019099334,0.000022755701,0.0019957602],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886775,0.0004099471,0.00009198981,0.00013778261,0.00036428522,0.00012821224],"domain_scores_gemma":[0.9970155,0.0016529943,0.0003915896,0.00011836033,0.0007566732,0.000064974905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00134882,0.00042499622,0.0003908413,0.0022461975,0.00036570468,0.0016222472,0.0003696883,0.00057022984,0.0017803293],"category_scores_gemma":[0.005767351,0.00012313199,0.0004230147,0.0015367167,0.00026542787,0.0012380813,0.0003922259,0.00083698705,0.0010136183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004900922,0.0008864991,0.08610069,0.0004621,0.00036938963,0.0006388972,0.0014283124,0.07696968,0.014496188,0.013503602,0.017825112,0.7868294],"study_design_scores_gemma":[0.00002219062,0.00014214279,0.02906173,0.00006424425,0.000063139516,0.00008131634,0.0014616332,0.93022734,0.0066907792,0.021896932,0.01025245,0.00003606251],"about_ca_topic_score_codex":0.0022748108,"about_ca_topic_score_gemma":0.0023146844,"teacher_disagreement_score":0.0022748108,"about_ca_system_score_codex":0.00064577634,"about_ca_system_score_gemma":0.00035324876,"threshold_uncertainty_score":0.0071333647},"labels":[],"label_agreement":null},{"id":"W4389482560","doi":"10.54254/2753-8818/18/20230402","title":"Simulating real-time tweet sentiment analysis by different machine learning methods based on spark","year":2023,"lang":"en","type":"article","venue":"Theoretical and Natural Science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Sentiment analysis; SPARK (programming language); Naive Bayes classifier; Computer science; Machine learning; Artificial intelligence; Decision tree; Pipeline (software); Random forest; Logistic regression; Data mining; Support vector machine","score_opus":0.009934381523975131,"score_gpt":0.3127698872418651,"score_spread":0.30283550571789,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389482560","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82986695,0.00030154618,0.14447409,0.0008644182,0.00048638321,0.00026438385,0.0019569276,0.0069360556,0.0148491645],"genre_scores_gemma":[0.9487914,0.00013920774,0.047946725,0.00010019789,0.000033417826,0.00012845127,0.0011376893,0.00020661441,0.0015163968],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995265,0.000116862364,0.000036169662,0.00008463032,0.00014345284,0.00009228551],"domain_scores_gemma":[0.99826884,0.0009961255,0.00008953587,0.00014963123,0.0003728243,0.00012300129],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010407304,0.00057361746,0.0005478742,0.00048102133,0.0004479309,0.00061950024,0.0010166251,0.00056369364,0.0015265238],"category_scores_gemma":[0.0029621471,0.00023985488,0.0005858799,0.00069478503,0.00043004128,0.0008767261,0.00041475595,0.0008561829,0.0002511061],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012152205,0.0005848203,0.013726283,0.00027138233,0.000167505,0.0003170222,0.0003792564,0.9104556,0.01600037,0.011285194,0.010860413,0.034736898],"study_design_scores_gemma":[0.000046328907,0.000059816084,0.0005433455,0.0000020900195,0.000006513263,0.000013692331,0.000035765024,0.9939564,0.0030623497,0.0013365159,0.00093021314,0.0000070848146],"about_ca_topic_score_codex":0.0075995373,"about_ca_topic_score_gemma":0.0043316646,"teacher_disagreement_score":0.0075995373,"about_ca_system_score_codex":0.00062605814,"about_ca_system_score_gemma":0.00065756903,"threshold_uncertainty_score":0.015110612},"labels":[],"label_agreement":null},{"id":"W4389520166","doi":"10.18653/v1/2023.findings-emnlp.271","title":"Evaluating Emotion Arcs Across Languages: Bridging the Global Divide in Sentiment Analysis","year":2023,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of Alberta","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; Alberta Innovates; Bayerische Akademie der Wissenschaften; University of Alberta; DeepMind","keywords":"Computer science; Lexicon; Bridging (networking); Natural language processing; Sentiment analysis; Artificial intelligence; Salient; Arabic; Emotion classification; Linguistics","score_opus":0.06157868698262214,"score_gpt":0.41532806091084257,"score_spread":0.3537493739282204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389520166","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73671055,0.0023740856,0.20995605,0.002252551,0.0006352455,0.0006066641,0.010903503,0.004035131,0.03252622],"genre_scores_gemma":[0.88905674,0.0005193718,0.08950818,0.00049799244,0.00016729884,0.0004677358,0.015929587,0.0007621413,0.0030908333],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966893,0.0017189618,0.0002707745,0.00063163746,0.00052564294,0.00016368389],"domain_scores_gemma":[0.99288404,0.0043811663,0.0006249139,0.0006250206,0.0012985541,0.00018625896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005255774,0.0010016436,0.00044180616,0.002782986,0.00061904284,0.0023341596,0.0004648436,0.00063584966,0.0022387027],"category_scores_gemma":[0.0163418,0.00018993444,0.0008807372,0.002138609,0.0006705387,0.0036626123,0.0020663491,0.001381114,0.0014231998],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013745258,0.0004238259,0.16507097,0.0014913316,0.00055194355,0.0005345224,0.009675836,0.016346278,0.024646847,0.012482701,0.051156696,0.7162446],"study_design_scores_gemma":[0.00032293863,0.0007329092,0.22670706,0.0008030322,0.00052454666,0.0010444832,0.020719867,0.54831237,0.034232404,0.06849748,0.09783155,0.00027132698],"about_ca_topic_score_codex":0.001512453,"about_ca_topic_score_gemma":0.0029916111,"teacher_disagreement_score":0.005255774,"about_ca_system_score_codex":0.00075969374,"about_ca_system_score_gemma":0.00045960044,"threshold_uncertainty_score":0.027795494},"labels":[],"label_agreement":null},{"id":"W4389523844","doi":"10.18653/v1/2023.findings-emnlp.780","title":"Efficient Cross-Task Prompt Tuning for Few-Shot Conversational Emotion Recognition","year":2023,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Research (Canada)","funders":"Nanyang Technological University; National Research Foundation Singapore; National Research Foundation","keywords":"Computer science; Conversation; Task (project management); Shot (pellet); Curse of dimensionality; Artificial intelligence; Machine learning; Speech recognition; Human–computer interaction","score_opus":0.07862116703193545,"score_gpt":0.3224445381416058,"score_spread":0.24382337110967034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389523844","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049143583,0.001196684,0.9377671,0.00024260809,0.00028186032,0.00021578585,0.00023505592,0.008602376,0.002314892],"genre_scores_gemma":[0.6564114,0.0004075558,0.33185887,0.00082294235,0.00024452637,0.00078609405,0.0023402032,0.0011179566,0.0060105026],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982333,0.00043428852,0.00009218466,0.00075130654,0.0002627021,0.00022606719],"domain_scores_gemma":[0.99825376,0.00076388865,0.000090234076,0.0002989335,0.00042580557,0.00016745589],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026543178,0.002149066,0.001766585,0.0006837467,0.00068075676,0.0010325231,0.0020867109,0.0015771189,0.0043550357],"category_scores_gemma":[0.008967638,0.0006429943,0.0010570186,0.0006684904,0.00073774345,0.0024357417,0.0025876567,0.0029359844,0.0027126549],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012478688,0.0009261689,0.0029029055,0.00041121,0.00021474526,0.00026685785,0.000434893,0.14481145,0.051839143,0.0029146548,0.0156122865,0.7784179],"study_design_scores_gemma":[0.00006501229,0.00018187679,0.0011110661,0.000013020251,0.000030970714,0.000087738845,0.00010114697,0.9852786,0.007670532,0.0038267442,0.0016026733,0.000030663887],"about_ca_topic_score_codex":0.0033157712,"about_ca_topic_score_gemma":0.0048091183,"teacher_disagreement_score":0.0043550357,"about_ca_system_score_codex":0.0006702342,"about_ca_system_score_gemma":0.0016786125,"threshold_uncertainty_score":0.014569044},"labels":[],"label_agreement":null},{"id":"W4389609583","doi":"10.1016/j.inffus.2023.102143","title":"Aspect-level sentiment analysis based on aspect-sentence graph convolution network","year":2023,"lang":"en","type":"article","venue":"Information Fusion","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nipissing University","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Computer science; Sentence; Graph; Sentiment analysis; Convolution (computer science); Natural language processing; Artificial intelligence; Theoretical computer science; Artificial neural network","score_opus":0.02546519475571722,"score_gpt":0.25225686667227704,"score_spread":0.2267916719165598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389609583","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20583095,0.0006192896,0.7823469,0.00036909516,0.00017782125,0.00013147254,0.0006633485,0.002128854,0.0077322293],"genre_scores_gemma":[0.8763089,0.00040727257,0.11741764,0.000112953334,0.00011575132,0.00008914959,0.0013697399,0.00012521086,0.004053377],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997725,0.000032849915,0.000012783678,0.000060700004,0.00008175654,0.00003951196],"domain_scores_gemma":[0.99973327,0.000054365173,0.00003733151,0.000018564946,0.00013766678,0.000018819035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003644165,0.0005303609,0.00042709857,0.0012540652,0.0004323464,0.00065720285,0.00040557687,0.00039143904,0.0014678108],"category_scores_gemma":[0.00089549186,0.00014154882,0.00066745887,0.0014170483,0.00021932095,0.0010789058,0.00046470205,0.0005045098,0.0004823235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006897187,0.00037803952,0.015887389,0.00023189254,0.00030422976,0.0005151949,0.0003410453,0.054511152,0.16998641,0.016248308,0.011114263,0.72979236],"study_design_scores_gemma":[0.000007365982,0.00006137455,0.0058155498,0.0000071262466,0.000077280616,0.00008868711,0.0000547954,0.9744761,0.012112442,0.0055489396,0.0017342257,0.000016110602],"about_ca_topic_score_codex":0.0033428068,"about_ca_topic_score_gemma":0.0045899316,"teacher_disagreement_score":0.0033428068,"about_ca_system_score_codex":0.00044547694,"about_ca_system_score_gemma":0.00048453675,"threshold_uncertainty_score":0.0066466928},"labels":[],"label_agreement":null},{"id":"W4389933352","doi":"10.1109/wi-iat59888.2023.00017","title":"Emotion-based Analysis of Reviews using Knowledge Graph","year":2023,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Sentiment analysis; Categorization; Computer science; Perspective (graphical); Natural language processing; Process (computing); Artificial intelligence; Graph; Data science; Theoretical computer science","score_opus":0.10138331395283272,"score_gpt":0.3558536898463045,"score_spread":0.25447037589347177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389933352","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16822311,0.0018256316,0.8080394,0.0011239065,0.00014469416,0.0003505627,0.0051770667,0.0022552316,0.012860453],"genre_scores_gemma":[0.76003677,0.001252189,0.22993824,0.00014734846,0.000102951395,0.00024260662,0.005685204,0.00011589086,0.0024788368],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992162,0.00022066591,0.00005666029,0.00019556313,0.00026978884,0.000041064097],"domain_scores_gemma":[0.9974132,0.0014434103,0.000381687,0.00017238205,0.00051621714,0.000073124436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058033643,0.0005342005,0.00036420033,0.006272776,0.00048589284,0.0015352344,0.00046029888,0.00045836074,0.001326973],"category_scores_gemma":[0.0044086208,0.00015429936,0.000725744,0.0037119072,0.00036828246,0.0021610723,0.00055749976,0.00043220836,0.0004648121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005684594,0.00042392124,0.033274174,0.0014779402,0.0006481391,0.0017512126,0.0032826785,0.09646333,0.03972674,0.07582027,0.016234946,0.73032826],"study_design_scores_gemma":[0.000024527271,0.00018624954,0.027287703,0.00019837376,0.00028670952,0.00061256986,0.0017558816,0.8160717,0.0115672555,0.10635534,0.035562858,0.000090894304],"about_ca_topic_score_codex":0.0051754247,"about_ca_topic_score_gemma":0.006203179,"teacher_disagreement_score":0.006272776,"about_ca_system_score_codex":0.00090486225,"about_ca_system_score_gemma":0.0004749172,"threshold_uncertainty_score":0.010290563},"labels":[],"label_agreement":null},{"id":"W4390551645","doi":"10.1109/icaeeci58247.2023.10370825","title":"Aspect-based sentiment analysis for social media text using NLP and Deep Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Sentiment analysis; Computer science; Artificial intelligence; Social media; Variety (cybernetics); Set (abstract data type); Field (mathematics); Natural language processing; Event (particle physics); Perspective (graphical); Machine learning; Feeling; Deep learning; Data science; World Wide Web; Psychology; Social psychology","score_opus":0.05387675889160938,"score_gpt":0.3062661427110056,"score_spread":0.25238938381939624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390551645","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08037041,0.000397746,0.90712345,0.0009007178,0.00021581778,0.00032265592,0.002093468,0.0038133434,0.0047624446],"genre_scores_gemma":[0.61558115,0.00046582142,0.37394825,0.0002464161,0.00027397877,0.00033150645,0.004819735,0.00031444372,0.004018645],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994429,0.00016258644,0.0000659384,0.00011368258,0.0001546338,0.00006025919],"domain_scores_gemma":[0.99888283,0.0005174058,0.0001628888,0.000093831055,0.00030001008,0.000043058288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012532085,0.0007097506,0.00042938074,0.001958119,0.00053341384,0.0014256034,0.0005544106,0.00058945705,0.0027121855],"category_scores_gemma":[0.0035258755,0.00024732554,0.00091668003,0.0013573284,0.00036416453,0.0019008776,0.0008295221,0.0012955952,0.0016038473],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029324275,0.00033256592,0.012503323,0.0003626501,0.0001869838,0.0004102402,0.0008836166,0.04261621,0.04061483,0.011839947,0.016496079,0.8734603],"study_design_scores_gemma":[0.0000088996585,0.000038230275,0.0032356672,0.00001917317,0.000019678435,0.000051946437,0.00025635245,0.9757606,0.0039513246,0.012187209,0.0044581466,0.000012679279],"about_ca_topic_score_codex":0.0021153693,"about_ca_topic_score_gemma":0.0039201304,"teacher_disagreement_score":0.0027121855,"about_ca_system_score_codex":0.00072040403,"about_ca_system_score_gemma":0.0004559196,"threshold_uncertainty_score":0.009073198},"labels":[],"label_agreement":null},{"id":"W4390679436","doi":"10.1109/aike59827.2023.00011","title":"Influencer Lookalikes: A Novel Approach to Identifying Similar Social Media Users","year":2023,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Office of the Chief Medical Examiner","funders":"","keywords":"Social media; Computer science; Internet privacy; World Wide Web","score_opus":0.10305339089129467,"score_gpt":0.314410804906578,"score_spread":0.21135741401528335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390679436","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19667222,0.0005917509,0.78362393,0.0003742637,0.00018679728,0.00093124964,0.0014607887,0.006161724,0.009997175],"genre_scores_gemma":[0.6448164,0.000166168,0.34754795,0.00011049927,0.00018882369,0.00033104556,0.0012272804,0.00027668124,0.0053352234],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981376,0.00038560404,0.00010747728,0.00065075065,0.0006048034,0.00011377279],"domain_scores_gemma":[0.99657685,0.0012459051,0.0007099857,0.00051938015,0.000767196,0.00018071746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015531537,0.000774224,0.0009255515,0.0058625033,0.00091775105,0.0014241915,0.0012296074,0.0007495872,0.0022971488],"category_scores_gemma":[0.007165801,0.0003237271,0.00070156576,0.0022038303,0.00066774583,0.0018299662,0.0014957936,0.00076490146,0.0014625291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010587669,0.00062144303,0.060668707,0.0003859426,0.00056484883,0.0007000665,0.002634478,0.007879355,0.0722183,0.012142047,0.010912029,0.83021396],"study_design_scores_gemma":[0.00007771023,0.0007816747,0.0681289,0.000076546174,0.00025152462,0.002583903,0.0015249681,0.84875935,0.04216472,0.01378802,0.021674654,0.00018808147],"about_ca_topic_score_codex":0.0019938755,"about_ca_topic_score_gemma":0.005679828,"teacher_disagreement_score":0.0058625033,"about_ca_system_score_codex":0.00047018105,"about_ca_system_score_gemma":0.00048390028,"threshold_uncertainty_score":0.008213937},"labels":[],"label_agreement":null},{"id":"W4390883256","doi":"10.33423/jabe.v25i7.6724","title":"A Comparative Analysis of Text Mining Methodologies for Online Consumer Reviews","year":2024,"lang":"en","type":"article","venue":"Journal of Applied Business and Economics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sentiment analysis; Computer science; Convolutional neural network; Artificial intelligence; Support vector machine; Biomedical text mining; Information extraction; Machine learning; Natural language processing; Data mining; Data science; Text mining; Information retrieval","score_opus":0.1502320202679658,"score_gpt":0.36412318796856247,"score_spread":0.21389116770059666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390883256","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6772857,0.05923204,0.21925911,0.0028866476,0.0010256799,0.0010384253,0.010695901,0.00601208,0.022564357],"genre_scores_gemma":[0.7375373,0.013583899,0.22873117,0.00034690343,0.0006122681,0.0006647204,0.013452625,0.00042222187,0.004648879],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99530196,0.0020769732,0.0005231077,0.0006655311,0.0013027224,0.00012965023],"domain_scores_gemma":[0.97235775,0.01920818,0.0018025213,0.0011143644,0.0052694194,0.00024784784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005990393,0.0011065581,0.0007630941,0.008077775,0.00038455595,0.0015514473,0.00061368244,0.0006530872,0.0008761625],"category_scores_gemma":[0.02209046,0.00027422686,0.0013466388,0.004193402,0.00019972304,0.0024176235,0.00041155986,0.00055941043,0.000998059],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007136022,0.0003698313,0.046344794,0.0028820946,0.0007695835,0.00021444754,0.0007230119,0.015403418,0.009768177,0.0029156366,0.01849964,0.90139586],"study_design_scores_gemma":[0.000107853615,0.001568064,0.149078,0.0008262428,0.0006293296,0.0013960064,0.00161931,0.7520664,0.029073313,0.004488031,0.058920797,0.00022673319],"about_ca_topic_score_codex":0.0030091144,"about_ca_topic_score_gemma":0.00567982,"teacher_disagreement_score":0.008077775,"about_ca_system_score_codex":0.0010852298,"about_ca_system_score_gemma":0.0006886271,"threshold_uncertainty_score":0.031680584},"labels":[],"label_agreement":null},{"id":"W4391074521","doi":"10.1016/j.engappai.2024.107907","title":"AGCVT-prompt for sentiment classification: Automatically generating chain of thought and verbalizer in prompt learning","year":2024,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Key Science and Technology Program of Shaanxi Province; Department of Science and Technology of Sichuan Province; National Natural Science Foundation of China","keywords":"Computer science; Interpretability; Artificial intelligence; Template; Transparency (behavior); Deep learning; Sentiment analysis; Machine learning; Natural language processing; Computer security","score_opus":0.02761439038441367,"score_gpt":0.2969081063464841,"score_spread":0.26929371596207047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391074521","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04554931,0.00066574843,0.55476856,0.0008321539,0.001888486,0.0011255487,0.026368408,0.35921472,0.009587058],"genre_scores_gemma":[0.2254284,0.00035482718,0.7081962,0.00054013386,0.00048694335,0.0018831356,0.045660064,0.0049877358,0.012462554],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986368,0.00040203932,0.000094639676,0.0004977579,0.00024453615,0.00012430035],"domain_scores_gemma":[0.99550617,0.002242752,0.00023190839,0.0004745228,0.0012628912,0.0002817096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017502425,0.0029320775,0.00094574166,0.0018618131,0.0007296139,0.0015647336,0.0018414283,0.0019831255,0.03413605],"category_scores_gemma":[0.008830336,0.0006358148,0.0009582717,0.0010852137,0.00040856042,0.0033704946,0.002196005,0.0027064802,0.024072146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001142018,0.00046302282,0.0040270095,0.00087080966,0.000057584595,0.0004937874,0.00058427395,0.0033891245,0.032996006,0.0040480644,0.1883873,0.763541],"study_design_scores_gemma":[0.0006218461,0.0012060937,0.006698399,0.00034258797,0.00016857631,0.0008292689,0.001493586,0.68098927,0.13193779,0.03975903,0.13576683,0.00018676327],"about_ca_topic_score_codex":0.0022652282,"about_ca_topic_score_gemma":0.0030635537,"teacher_disagreement_score":0.03413605,"about_ca_system_score_codex":0.00089011644,"about_ca_system_score_gemma":0.0017362243,"threshold_uncertainty_score":0.11419648},"labels":[],"label_agreement":null},{"id":"W4391102777","doi":"10.32985/ijeces.15.1.7","title":"A Survey of Sentiment Analysis and Sarcasm Detection","year":2024,"lang":"en","type":"article","venue":"International journal of electrical and computer engineering systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Sarcasm; Sentiment analysis; Computer science; Data science; Social media; Resource (disambiguation); Artificial intelligence; Field (mathematics); World Wide Web; Linguistics; Irony","score_opus":0.009508061209681142,"score_gpt":0.2385907849055376,"score_spread":0.22908272369585644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391102777","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19134551,0.4668996,0.1898224,0.008251003,0.0055484804,0.001623211,0.012882096,0.0066196225,0.11700802],"genre_scores_gemma":[0.4176718,0.35139498,0.15587047,0.004657781,0.0053271633,0.0017125952,0.025292782,0.0014052221,0.036667235],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99646145,0.00093767646,0.00035857334,0.0006774508,0.0014063504,0.00015852162],"domain_scores_gemma":[0.9891304,0.0059361965,0.0008866324,0.0004169665,0.003418134,0.00021182113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043102265,0.0016695751,0.0014522757,0.008149786,0.0008050289,0.0022983055,0.0009880522,0.0009096373,0.0033696105],"category_scores_gemma":[0.013997045,0.00049881777,0.0012394878,0.005510335,0.00053434644,0.0030988231,0.0009021139,0.0008979034,0.003698619],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023558833,0.00011287182,0.017428802,0.006183471,0.00022510695,0.00013870369,0.0011185831,0.0005966752,0.0069225146,0.0018218223,0.04678097,0.9184348],"study_design_scores_gemma":[0.00007940973,0.000872394,0.12867785,0.009397031,0.00096059986,0.0030840558,0.008665049,0.030927729,0.029776204,0.009458276,0.77772146,0.00037998025],"about_ca_topic_score_codex":0.0014256911,"about_ca_topic_score_gemma":0.0022882037,"teacher_disagreement_score":0.008149786,"about_ca_system_score_codex":0.00061885995,"about_ca_system_score_gemma":0.00082893687,"threshold_uncertainty_score":0.022794902},"labels":[],"label_agreement":null},{"id":"W4391140106","doi":"10.23977/jaip.2024.070101","title":"Application of Deep Learning in Cross-Lingual Sentiment Analysis for Natural Language Processing","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Natural language processing; Sentiment analysis; Computer science; Artificial intelligence; Natural (archaeology); Deep learning; History; Archaeology","score_opus":0.03160256699623334,"score_gpt":0.39883897351690345,"score_spread":0.36723640652067013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391140106","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.084151015,0.0021025992,0.8936394,0.0025508874,0.00055122474,0.00024627856,0.0012780122,0.0025241396,0.01295652],"genre_scores_gemma":[0.65381145,0.0016959942,0.33433107,0.0007924849,0.00029236556,0.00026752477,0.002705594,0.00024103156,0.0058625108],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987835,0.00048681154,0.00013192027,0.00022609926,0.00026418385,0.000107389],"domain_scores_gemma":[0.9981395,0.00073657616,0.00017236221,0.0001936605,0.0006934549,0.00006438709],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002469136,0.0008439377,0.0004993182,0.0015615239,0.00057537126,0.0017590123,0.0005703339,0.0006803922,0.002573469],"category_scores_gemma":[0.005934635,0.00025390185,0.0007913366,0.0013593134,0.00041058916,0.002169156,0.001544727,0.0015265965,0.0012277991],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031063167,0.00036057073,0.0121292835,0.0005583484,0.00036352873,0.00036926506,0.0008174569,0.051282395,0.0342887,0.016314741,0.017749395,0.86545575],"study_design_scores_gemma":[0.000017577198,0.00010094481,0.0044631124,0.000104199615,0.00008872738,0.00010660769,0.0004475268,0.93651915,0.013647703,0.031041833,0.013427059,0.00003558451],"about_ca_topic_score_codex":0.0033187664,"about_ca_topic_score_gemma":0.0044226632,"teacher_disagreement_score":0.0033187664,"about_ca_system_score_codex":0.0010101208,"about_ca_system_score_gemma":0.0009833842,"threshold_uncertainty_score":0.013058186},"labels":[],"label_agreement":null},{"id":"W4391385354","doi":"10.2196/47508","title":"Public Opinion About COVID-19 on a Microblog Platform in China: Topic Modeling and Multidimensional Sentiment Analysis of Social Media","year":2024,"lang":"en","type":"article","venue":"Journal of Medical Internet Research","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"National Office for Philosophy and Social Sciences","keywords":"Microblogging; Social media; Latent Dirichlet allocation; Public opinion; Sentiment analysis; Topic model; Computer science; Government (linguistics); Pandemic; Data science; Coronavirus disease 2019 (COVID-19); Information retrieval; Artificial intelligence; Political science; World Wide Web; Politics; Medicine; Law","score_opus":0.15276021654031322,"score_gpt":0.4323322924645032,"score_spread":0.27957207592419,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391385354","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99601173,0.00006649506,0.0027767539,0.00024194623,0.000016848233,0.000027395046,0.0002718612,0.00001821862,0.0005686919],"genre_scores_gemma":[0.99777967,0.00007626267,0.0010001722,0.000022273662,0.00003408109,0.000026910964,0.00051943504,0.000004639776,0.000536491],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995875,0.000114630355,0.000031959575,0.0000970209,0.000081869824,0.00008705635],"domain_scores_gemma":[0.9989349,0.00046555547,0.00018388839,0.000051864943,0.00027482433,0.000089003646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014440102,0.0005525447,0.00039236163,0.001642309,0.0005898651,0.00074104394,0.0004299597,0.00051227055,0.00076662947],"category_scores_gemma":[0.0024223733,0.0001872185,0.0008867461,0.0009047583,0.00029228764,0.0009432624,0.0005099674,0.00044045653,0.00018683563],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041388528,0.00050691207,0.8400788,0.0002706141,0.00025935113,0.0008358371,0.0040390007,0.03510087,0.009041249,0.001685294,0.0055070356,0.10226116],"study_design_scores_gemma":[0.000013108179,0.00010807021,0.38399634,0.000026266021,0.000109139706,0.00006824397,0.0023876436,0.6099933,0.0016483902,0.0006701606,0.0009400884,0.000039226234],"about_ca_topic_score_codex":0.034277234,"about_ca_topic_score_gemma":0.027829487,"teacher_disagreement_score":0.034277234,"about_ca_system_score_codex":0.0011750712,"about_ca_system_score_gemma":0.00072688604,"threshold_uncertainty_score":0.06815541},"labels":[],"label_agreement":null},{"id":"W4391548924","doi":"10.1109/cist56084.2023.10409979","title":"Analyzing US Airline Customer Sentiment on Twitter using Multinomial Logistic Regression and Feature Reduction","year":2023,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université TÉLUQ","funders":"","keywords":"Multinomial logistic regression; Computer science; Logistic regression; Feature (linguistics); Sentiment analysis; Reduction (mathematics); Artificial intelligence; Machine learning; Mathematics","score_opus":0.06576273397583307,"score_gpt":0.3337705562856941,"score_spread":0.268007822309861,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391548924","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6804687,0.00045296716,0.3124974,0.0006784925,0.000120784614,0.0002001798,0.0016776411,0.0020156282,0.0018882311],"genre_scores_gemma":[0.88714415,0.00022128265,0.10674066,0.00007265255,0.000096005126,0.00019556808,0.0029928905,0.00006293961,0.0024738903],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99942327,0.00020048722,0.000053907683,0.000095576666,0.00014320033,0.00008360572],"domain_scores_gemma":[0.9988563,0.0005436879,0.00016027418,0.00007411098,0.00033777818,0.000027868056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012389731,0.0007826858,0.0007226666,0.0012935725,0.0003881386,0.00051725865,0.0004621278,0.0003595873,0.0013400478],"category_scores_gemma":[0.0036200772,0.00018816361,0.0009844187,0.001168148,0.00012522867,0.0006080826,0.00042948328,0.0006416112,0.0010634967],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011571841,0.0008743134,0.092699744,0.00028959606,0.00032930085,0.00061433093,0.00034497306,0.1135348,0.045419365,0.0011033022,0.010121546,0.73351157],"study_design_scores_gemma":[0.000012772201,0.00010069787,0.013050052,0.000007957258,0.000025919424,0.000054507636,0.00010291984,0.98162997,0.0039558113,0.00026183084,0.00078284706,0.000014707823],"about_ca_topic_score_codex":0.008421686,"about_ca_topic_score_gemma":0.008613186,"teacher_disagreement_score":0.008421686,"about_ca_system_score_codex":0.00034090303,"about_ca_system_score_gemma":0.00045773853,"threshold_uncertainty_score":0.016745329},"labels":[],"label_agreement":null},{"id":"W4391572539","doi":"10.54254/2755-2721/38/20230559","title":"Exploration of movie evaluation analysis and data preprocessing impact based on RNN technology","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Punctuation; Recurrent neural network; Preprocessor; Artificial intelligence; Word embedding; Lexical analysis; Machine learning; Data pre-processing; Natural language processing; Artificial neural network; Embedding","score_opus":0.03480916320377801,"score_gpt":0.32080088080215646,"score_spread":0.28599171759837844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391572539","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.547441,0.0018061243,0.4330051,0.0010948855,0.00010471829,0.00042781717,0.0010552743,0.002411764,0.012653211],"genre_scores_gemma":[0.85190403,0.0005748391,0.14437905,0.000060644536,0.000033227327,0.00013411278,0.0010269118,0.0001289172,0.001758216],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899286,0.00045737065,0.00005664233,0.0001491749,0.00028467938,0.000059242342],"domain_scores_gemma":[0.99810493,0.0011092951,0.0001220948,0.000109364315,0.0005090599,0.000045291337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00199572,0.0008451194,0.00040469534,0.0014425259,0.00023722395,0.0010651721,0.0004187392,0.00027093652,0.0013638864],"category_scores_gemma":[0.00652372,0.00018094612,0.00051380316,0.0009234899,0.00016869635,0.0015506883,0.00035531446,0.000556328,0.0003624634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009847961,0.00036077623,0.03214989,0.00082779,0.00025331596,0.0005361031,0.00057477306,0.079539984,0.057401236,0.006255621,0.004638009,0.8164777],"study_design_scores_gemma":[0.000017342794,0.00021884317,0.019053549,0.000045973495,0.000065400985,0.00010984864,0.00030854152,0.9547095,0.020184368,0.0026734392,0.002586515,0.000026776492],"about_ca_topic_score_codex":0.0047508962,"about_ca_topic_score_gemma":0.006899188,"teacher_disagreement_score":0.0047508962,"about_ca_system_score_codex":0.00075173035,"about_ca_system_score_gemma":0.00050780404,"threshold_uncertainty_score":0.010554552},"labels":[],"label_agreement":null},{"id":"W4391999582","doi":"10.6025/stm/2023/4/17-30","title":"Fostering Sentiments Assessing the Prolific Pursuits of the United Arab Emirates in Sentiment Analysis Research","year":2023,"lang":"en","type":"book-chapter","venue":"DLINE eBooks","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Zayed University; Qatar University; American University of Beirut; University of Wollongong; York University; University of Stirling","keywords":"Sentiment analysis; Political science; History; Computer science; Artificial intelligence","score_opus":0.17472482878336013,"score_gpt":0.3852310606665974,"score_spread":0.21050623188323728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391999582","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.845451,0.008160187,0.0021096882,0.0033629392,0.00028794562,0.000059334867,0.00078596483,0.0000632447,0.13971968],"genre_scores_gemma":[0.96872216,0.0078616105,0.0052065053,0.00034020861,0.0004083691,0.000047691145,0.00076626183,0.000034506706,0.01661275],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99934417,0.00021420952,0.000035108587,0.000035013392,0.0003239703,0.00004757427],"domain_scores_gemma":[0.997598,0.0010964088,0.00046784326,0.000046480378,0.0006622055,0.00012909308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019056767,0.00025562107,0.00015871777,0.004135303,0.0006041408,0.003176733,0.00014633455,0.00024353454,0.0025459079],"category_scores_gemma":[0.0045328857,0.00007722427,0.00015131106,0.004907375,0.00029882698,0.0012090172,0.00068982155,0.00028502484,0.0008201427],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017345037,0.00015042564,0.306891,0.0011623921,0.00012256547,0.00053868414,0.014753247,0.00062645023,0.010987877,0.009721704,0.037671816,0.6172003],"study_design_scores_gemma":[0.0000101217365,0.00024805116,0.83336407,0.0009787406,0.000109298904,0.00091139,0.028452255,0.0042555034,0.004940431,0.0038036597,0.12288516,0.00004133622],"about_ca_topic_score_codex":0.0010600464,"about_ca_topic_score_gemma":0.0029739917,"teacher_disagreement_score":0.004135303,"about_ca_system_score_codex":0.0007398599,"about_ca_system_score_gemma":0.00048238022,"threshold_uncertainty_score":0.010078311},"labels":[],"label_agreement":null},{"id":"W4392336917","doi":"10.11591/ijeecs.v34.i1.pp497-507","title":"Sentiment analysis and classification of Ghanaian football tweets from the 2022 FIFA World Cup","year":2024,"lang":"en","type":"article","venue":"Indonesian Journal of Electrical Engineering and Computer Science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Sentiment analysis; Football; Computer science; Artificial intelligence; Lexicon; Benchmark (surveying); Context (archaeology); Natural language processing; Machine learning; Encoder; Social media; Microblogging; World Wide Web; Geography","score_opus":0.009615410328893273,"score_gpt":0.22986416718087466,"score_spread":0.2202487568519814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392336917","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9877071,0.00021335669,0.0015210386,0.00021261105,0.00010113148,0.00007659591,0.007901158,0.00014395246,0.0021230408],"genre_scores_gemma":[0.96927357,0.00022600203,0.0050865235,0.000046050212,0.00007186369,0.0000748134,0.022716144,0.000020592159,0.0024844913],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99984777,0.000025486546,0.000014477325,0.000024427003,0.0000456636,0.00004221236],"domain_scores_gemma":[0.9997148,0.00007021967,0.000049287115,0.000019119703,0.000115823794,0.000030676027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031262625,0.00039944437,0.00018085245,0.0011562114,0.0003252209,0.0002595655,0.00012677837,0.00025442455,0.000978598],"category_scores_gemma":[0.00082939793,0.00007098311,0.00020782542,0.00086545595,0.00016962235,0.00026705727,0.00024313037,0.00025064085,0.00049249135],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027663573,0.0008186118,0.4217188,0.0008228239,0.00021078778,0.002316704,0.0025678794,0.0127541525,0.08546737,0.0012826467,0.060963016,0.40831083],"study_design_scores_gemma":[0.0000788137,0.00062859966,0.77495015,0.00013095708,0.00009811255,0.00066238636,0.00723791,0.13158064,0.044661902,0.0008009298,0.03911845,0.00005118019],"about_ca_topic_score_codex":0.008845373,"about_ca_topic_score_gemma":0.015520498,"teacher_disagreement_score":0.008845373,"about_ca_system_score_codex":0.00040552564,"about_ca_system_score_gemma":0.00024866406,"threshold_uncertainty_score":0.017587721},"labels":[],"label_agreement":null},{"id":"W4392344560","doi":"10.18280/isi.290138","title":"Sentiment Analysis Methods for Arabic Content on Social Media: A Systematic Review","year":2024,"lang":"fr","type":"review","venue":"Ingénierie des systèmes d information","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Qassim University","keywords":"Sentiment analysis; Lexicon; Computer science; Variety (cybernetics); Natural language processing; Pronunciation; Linguistics; Artificial intelligence; Grammar; Syntax; Social media; World Wide Web","score_opus":0.1415636493640359,"score_gpt":0.38445898429580405,"score_spread":0.24289533493176815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392344560","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001260622,0.99520737,0.0008693014,0.0006242889,0.0002174939,0.0004954285,0.00038812216,0.000020524083,0.0009168628],"genre_scores_gemma":[0.010033171,0.9847778,0.003165425,0.000558912,0.00015664053,0.00076711935,0.00027917136,0.000017300263,0.00024448556],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9954175,0.0017230109,0.001259696,0.00037563036,0.0011141528,0.000110038156],"domain_scores_gemma":[0.97529125,0.017963834,0.0030135687,0.00032975143,0.003173438,0.00022819659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0067998986,0.0014741078,0.0034297393,0.015054659,0.00092383835,0.002629726,0.0012224703,0.0011843396,0.004646188],"category_scores_gemma":[0.025945306,0.0006984972,0.0051469775,0.008377407,0.0011851653,0.0033943758,0.0014953684,0.0010674492,0.0008984456],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016682617,0.00005843925,0.0013677898,0.53826815,0.0016968463,0.00014819003,0.0010100533,0.00017031113,0.0006480327,0.000936275,0.008652438,0.44687662],"study_design_scores_gemma":[0.00012901191,0.00037232597,0.010130768,0.7633746,0.014053416,0.000957556,0.0026212723,0.0005825633,0.00090950023,0.0024689017,0.204255,0.00014494648],"about_ca_topic_score_codex":0.0032933706,"about_ca_topic_score_gemma":0.010311448,"teacher_disagreement_score":0.015054659,"about_ca_system_score_codex":0.0018867503,"about_ca_system_score_gemma":0.0072822245,"threshold_uncertainty_score":0.035961688},"labels":[],"label_agreement":null},{"id":"W4392359194","doi":"10.18280/ria.380107","title":"Textual Analysis for Public Sentiment Toward National Police Using CRISP-DM Framework","year":2024,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sentiment analysis; Computer science; Data science; Natural language processing","score_opus":0.1397947219469857,"score_gpt":0.3642212094894472,"score_spread":0.2244264875424615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392359194","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3731097,0.00074896397,0.6070953,0.0011284217,0.00011897302,0.000814459,0.005440822,0.0009512347,0.010592073],"genre_scores_gemma":[0.81500274,0.00032281413,0.17739725,0.0000971533,0.00010853811,0.0004510284,0.0039772517,0.000032659897,0.0026106036],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.999119,0.0002669747,0.00010588635,0.00019079838,0.00024026177,0.000077115845],"domain_scores_gemma":[0.9985176,0.00079970795,0.00018523238,0.00006525009,0.00038894083,0.000043231204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013468856,0.00036270206,0.0003266073,0.0031576725,0.00046521728,0.001266006,0.00044470053,0.0004181275,0.0023930306],"category_scores_gemma":[0.0040814527,0.00012168522,0.000921895,0.0017645211,0.00035080846,0.0009796624,0.00050735986,0.0005615622,0.00064241147],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007271145,0.000720367,0.07388814,0.0012837455,0.0002482008,0.001251489,0.0032324246,0.050138894,0.03470846,0.041793987,0.013296875,0.77871025],"study_design_scores_gemma":[0.000022507129,0.00017279318,0.030943885,0.00008762957,0.00009404078,0.00027505014,0.002188981,0.93718845,0.007864163,0.012780389,0.008338797,0.00004333942],"about_ca_topic_score_codex":0.003972308,"about_ca_topic_score_gemma":0.002993755,"teacher_disagreement_score":0.003972308,"about_ca_system_score_codex":0.0009142491,"about_ca_system_score_gemma":0.00080215844,"threshold_uncertainty_score":0.0080055},"labels":[],"label_agreement":null},{"id":"W4392385006","doi":"10.18280/ria.380119","title":"Analysis of Consumer Sentiments towards Online Shopping Using Context-Free Grammar and Deep Learning","year":2024,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Rajamangala University of Technology Srivijaya","keywords":"Context (archaeology); Grammar; Computer science; Natural language processing; Psychology; Linguistics; Artificial intelligence; Advertising; Business; History","score_opus":0.06030503151958704,"score_gpt":0.3163233252277128,"score_spread":0.25601829370812573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392385006","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93866765,0.00040255784,0.05705683,0.00028098363,0.00004415241,0.00008201645,0.0008733283,0.00044855606,0.0021439204],"genre_scores_gemma":[0.97136253,0.00018179102,0.025639163,0.00009090856,0.000019086623,0.00004681888,0.0016549971,0.00002134215,0.000983389],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963105,0.00011865613,0.000025316407,0.00008320713,0.00009584264,0.00004604443],"domain_scores_gemma":[0.9992493,0.00039152266,0.000078518475,0.00004659498,0.00020348438,0.000030670373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006064386,0.0005154393,0.00034286611,0.001065996,0.00018724172,0.00042846854,0.00022549782,0.00039425417,0.00051076035],"category_scores_gemma":[0.001990852,0.00011939143,0.0006815179,0.0005499324,0.00022841431,0.0005469043,0.000384269,0.0005417425,0.00023665983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000902764,0.0010257639,0.1395132,0.00043584823,0.00050455093,0.0010320129,0.0011754006,0.1563898,0.06942816,0.0037055826,0.007357261,0.6185297],"study_design_scores_gemma":[0.000021831722,0.00025611834,0.050586052,0.000024767383,0.00007063494,0.00015388754,0.000251046,0.93355775,0.009299348,0.0037784032,0.0019716679,0.000028410814],"about_ca_topic_score_codex":0.0061600287,"about_ca_topic_score_gemma":0.007997223,"teacher_disagreement_score":0.0061600287,"about_ca_system_score_codex":0.00057006517,"about_ca_system_score_gemma":0.0004205239,"threshold_uncertainty_score":0.012248337},"labels":[],"label_agreement":null},{"id":"W4392655881","doi":"10.47392/irjaeh.2024.0029","title":"A Comparative Study on Machine Learning Approaches for Sentiment Analysis","year":2024,"lang":"en","type":"article","venue":"International Research Journal on Advanced Engineering Hub (IRJAEH)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Sentiment analysis; Computer science; Artificial intelligence; Natural language processing; Machine learning","score_opus":0.14146353702295014,"score_gpt":0.42063778960006276,"score_spread":0.2791742525771126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392655881","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1507419,0.14129817,0.63665664,0.005679766,0.0017405347,0.0007108132,0.0008156947,0.0016132199,0.060743198],"genre_scores_gemma":[0.6070961,0.05686306,0.32560438,0.0009774541,0.0013130222,0.00042279973,0.001382248,0.00024654894,0.006094366],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9929147,0.0028714335,0.0005531297,0.00057848985,0.002862864,0.00021936526],"domain_scores_gemma":[0.98441607,0.010948323,0.00054950046,0.00048291706,0.0034436039,0.00015959944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008883031,0.0010562676,0.0010179324,0.0057108277,0.00066828314,0.0027059359,0.00087099633,0.0010964762,0.0016578631],"category_scores_gemma":[0.01704845,0.00030581857,0.0015905647,0.006136184,0.00054254866,0.0041591497,0.0005702497,0.0013079696,0.00096584926],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003757053,0.00028873628,0.011317977,0.0016263201,0.0005113218,0.000143039,0.0007000885,0.012100379,0.003098839,0.01533529,0.007176728,0.9473256],"study_design_scores_gemma":[0.00010515482,0.0014719496,0.047378175,0.0019025679,0.000617577,0.0009940257,0.0030871388,0.77415,0.012958168,0.037943434,0.11918052,0.00021130187],"about_ca_topic_score_codex":0.0017189264,"about_ca_topic_score_gemma":0.0016331418,"teacher_disagreement_score":0.008883031,"about_ca_system_score_codex":0.0012210156,"about_ca_system_score_gemma":0.00075028074,"threshold_uncertainty_score":0.046978533},"labels":[],"label_agreement":null},{"id":"W4392656786","doi":"10.1515/lingvan-2023-0051","title":"The role of syntax in hashtag popularity","year":2024,"lang":"en","type":"article","venue":"Linguistics Vanguard","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Popularity; Computer science; Syntax; Natural language processing; Semantics (computer science); CLARITY; Artificial intelligence; Linguistics; Information retrieval; Psychology; Programming language; Social psychology","score_opus":0.008434858255253173,"score_gpt":0.26520548807037014,"score_spread":0.25677062981511695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392656786","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9446638,0.00035035188,0.028467415,0.0013823048,0.000049888444,0.000053456784,0.00035064653,0.00019668211,0.024485542],"genre_scores_gemma":[0.99769044,0.00006143825,0.0016242778,0.000039167033,0.00002182978,0.000012021863,0.00007463452,0.000046141504,0.0004298891],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99626416,0.0022292582,0.00026006094,0.00045695165,0.00057899667,0.00021059132],"domain_scores_gemma":[0.9358897,0.045051794,0.008424304,0.0029447079,0.006134855,0.0015546893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052093877,0.00029297062,0.00043243705,0.002090381,0.0013489157,0.005713111,0.000570661,0.0007019028,0.0055639567],"category_scores_gemma":[0.056841373,0.0005787715,0.00037764636,0.0018794423,0.00341001,0.0127615845,0.002620632,0.001144081,0.0008906251],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00173226,0.00034047884,0.4803508,0.00054182607,0.00035409807,0.0009996953,0.02701298,0.007989755,0.03247224,0.31528756,0.0043226634,0.1285957],"study_design_scores_gemma":[0.00023819761,0.000727519,0.47349265,0.00031489017,0.00044207636,0.0019535446,0.019183282,0.08089787,0.012496199,0.3956291,0.014173573,0.00045116714],"about_ca_topic_score_codex":0.0019219997,"about_ca_topic_score_gemma":0.0015773644,"teacher_disagreement_score":0.005713111,"about_ca_system_score_codex":0.0012587041,"about_ca_system_score_gemma":0.0007868107,"threshold_uncertainty_score":0.02755022},"labels":[],"label_agreement":null},{"id":"W4392812451","doi":"10.1080/08839514.2024.2321555","title":"Sentiment Analysis of Short Texts Using SVMs and VSMs-Based Multiclass Semantic Classification","year":2024,"lang":"en","type":"article","venue":"Applied Artificial Intelligence","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bishop's University","funders":"Islamic Azad University","keywords":"Computer science; Support vector machine; Sentiment analysis; Artificial intelligence; Natural language processing; Multiclass classification; Machine learning; Information retrieval","score_opus":0.09074272334814584,"score_gpt":0.34321166855949553,"score_spread":0.25246894521134966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392812451","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13477223,0.0016561674,0.8537193,0.0006410329,0.0004929287,0.0003290563,0.00087156944,0.0021557587,0.0053619565],"genre_scores_gemma":[0.78733575,0.000808015,0.20383315,0.00018306328,0.00031760716,0.0003286662,0.0022912326,0.000081054925,0.0048214723],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904925,0.00021275213,0.00013634535,0.00019423688,0.0003264846,0.000080985665],"domain_scores_gemma":[0.9991555,0.00022541321,0.00012439457,0.000058904196,0.0004041781,0.000031663625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010314166,0.00079379114,0.00088548934,0.0021502338,0.0003167762,0.0012512285,0.00058774796,0.0005982598,0.0017030842],"category_scores_gemma":[0.0023518451,0.00016257526,0.0010590057,0.00134554,0.00023095243,0.0012406791,0.00046345766,0.0006922883,0.0013294125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044913474,0.00033557284,0.01045085,0.00038502336,0.0002774824,0.000191857,0.0002981867,0.03045538,0.02687029,0.005020179,0.007686681,0.9175794],"study_design_scores_gemma":[0.00001570356,0.00024264214,0.005387645,0.000042887823,0.00007050449,0.00011755058,0.00020986274,0.977018,0.008161032,0.004540306,0.004166884,0.000027007305],"about_ca_topic_score_codex":0.0013936795,"about_ca_topic_score_gemma":0.0012962278,"teacher_disagreement_score":0.0021502338,"about_ca_system_score_codex":0.0004922445,"about_ca_system_score_gemma":0.00049300055,"threshold_uncertainty_score":0.0056973696},"labels":[],"label_agreement":null},{"id":"W4392862147","doi":"10.48175/ijarsct-15750","title":"Large Language Model for Chatbot","year":2024,"lang":"en","type":"article","venue":"International Journal of Advanced Research in Science Communication and Technology","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Chatbot; Computer science; Natural language processing; Programming language","score_opus":0.07212575835808421,"score_gpt":0.47717783576525385,"score_spread":0.40505207740716964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392862147","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024049452,0.0006948627,0.932182,0.0031451946,0.00035003707,0.00064919924,0.005982381,0.0058238064,0.027123086],"genre_scores_gemma":[0.6971279,0.0005509906,0.24153267,0.0010338988,0.00039519957,0.002500083,0.009005923,0.0006004017,0.047252983],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99791116,0.0008393073,0.0001689129,0.00047591096,0.00037565708,0.00022897458],"domain_scores_gemma":[0.9954727,0.0028936658,0.00024863245,0.00043532567,0.00078246754,0.00016712128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028276837,0.0008898973,0.0008882123,0.0014407206,0.00124834,0.0024308932,0.0023637086,0.0021299305,0.019412376],"category_scores_gemma":[0.0075167255,0.0005195898,0.0014193273,0.0010623331,0.0010834462,0.0034559648,0.0018727311,0.0020258615,0.008156515],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00077156525,0.00043178175,0.0035937643,0.00052668387,0.00015147304,0.001499669,0.0015928285,0.27654687,0.0044949884,0.55949557,0.040552597,0.11034221],"study_design_scores_gemma":[0.000047327714,0.000044348817,0.00018624829,0.000017676139,0.000021848318,0.00011040594,0.00007450731,0.9164409,0.0004071567,0.07197362,0.010657789,0.000018170453],"about_ca_topic_score_codex":0.012590852,"about_ca_topic_score_gemma":0.010283894,"teacher_disagreement_score":0.019412376,"about_ca_system_score_codex":0.0022249816,"about_ca_system_score_gemma":0.0020202124,"threshold_uncertainty_score":0.06494087},"labels":[],"label_agreement":null},{"id":"W4392906277","doi":"10.32920/25418146.v1","title":"The Drivers of Polarity in Sentiments on Social Media: an Exploratory Study on the 2021 Canadian Federal Election","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Social media; Sentiment analysis; Exploratory analysis; Politics; Political science; Exploratory research; Polarization (electrochemistry); Polarity (international relations); Public relations; Test (biology); Psychology; Social psychology; Sociology; Computer science; Social science; Data science; Law","score_opus":0.05312067712459841,"score_gpt":0.30148985112165555,"score_spread":0.24836917399705713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392906277","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99493116,0.000078721205,0.000036097917,0.0005045829,0.000010337898,0.000064001906,0.000536047,0.0000024188528,0.003836593],"genre_scores_gemma":[0.9962059,0.00023127977,0.00012002585,0.00014911212,0.000010207149,0.00006945152,0.00045368637,0.0000062038516,0.002754077],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989427,0.00014083035,0.000028442882,0.00008239205,0.0003993431,0.00040641593],"domain_scores_gemma":[0.99677294,0.0006518329,0.00050002884,0.00006812702,0.0015721065,0.00043496533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015092281,0.00030344402,0.00036703347,0.0020685557,0.00619203,0.0029069963,0.0006430853,0.0005384414,0.002105054],"category_scores_gemma":[0.0044216556,0.00028373778,0.0003327841,0.0028464561,0.0013511652,0.0011086594,0.0012711376,0.001041831,0.00030241412],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002158939,0.00028582738,0.7275772,0.0001927322,0.000046260397,0.00097409607,0.23500969,0.00014090842,0.0017083351,0.0014882962,0.006906872,0.025453854],"study_design_scores_gemma":[0.0000038526914,0.000046483256,0.69180715,0.00006582208,0.000012851445,0.00004640601,0.2987615,0.00028325405,0.00018508261,0.00006603699,0.008692995,0.00002858653],"about_ca_topic_score_codex":0.9186199,"about_ca_topic_score_gemma":0.95417905,"teacher_disagreement_score":0.08138013,"about_ca_system_score_codex":0.015102629,"about_ca_system_score_gemma":0.014132794,"threshold_uncertainty_score":0.16371876},"labels":[],"label_agreement":null},{"id":"W4392914248","doi":"10.32920/25418146","title":"The Drivers of Polarity in Sentiments on Social Media: an Exploratory Study on the 2021 Canadian Federal Election","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Social media; Sentiment analysis; Exploratory analysis; Politics; Exploratory research; Political science; Polarity (international relations); Polarization (electrochemistry); Public relations; Psychology; Social psychology; Advertising; Sociology; Business; Data science; Computer science; Social science; Law","score_opus":0.05312067712459841,"score_gpt":0.30148985112165555,"score_spread":0.24836917399705713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392914248","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99493116,0.000078721205,0.000036097917,0.0005045829,0.000010337898,0.000064001906,0.000536047,0.0000024188528,0.003836593],"genre_scores_gemma":[0.9962059,0.00023127977,0.00012002585,0.00014911212,0.000010207149,0.00006945152,0.00045368637,0.0000062038516,0.002754077],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989427,0.00014083035,0.000028442882,0.00008239205,0.0003993431,0.00040641593],"domain_scores_gemma":[0.99677294,0.0006518329,0.00050002884,0.00006812702,0.0015721065,0.00043496533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015092281,0.00030344402,0.00036703347,0.0020685557,0.00619203,0.0029069963,0.0006430853,0.0005384414,0.002105054],"category_scores_gemma":[0.0044216556,0.00028373778,0.0003327841,0.0028464561,0.0013511652,0.0011086594,0.0012711376,0.001041831,0.00030241412],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002158939,0.00028582738,0.7275772,0.0001927322,0.000046260397,0.00097409607,0.23500969,0.00014090842,0.0017083351,0.0014882962,0.006906872,0.025453854],"study_design_scores_gemma":[0.0000038526914,0.000046483256,0.69180715,0.00006582208,0.000012851445,0.00004640601,0.2987615,0.00028325405,0.00018508261,0.00006603699,0.008692995,0.00002858653],"about_ca_topic_score_codex":0.9186199,"about_ca_topic_score_gemma":0.95417905,"teacher_disagreement_score":0.08138013,"about_ca_system_score_codex":0.015102629,"about_ca_system_score_gemma":0.014132794,"threshold_uncertainty_score":0.16371876},"labels":[],"label_agreement":null},{"id":"W4392985066","doi":"10.23977/jaip.2024.070114","title":"Graph Convolutional Networks for Aspect-Based Sentiment Analysis","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sentiment analysis; Computer science; Graph; Artificial intelligence; Natural language processing; Data science; Theoretical computer science","score_opus":0.05102887343383029,"score_gpt":0.35596288285447353,"score_spread":0.30493400942064325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392985066","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04997789,0.0011643369,0.9380387,0.0006467968,0.00015807668,0.00007077,0.0007061013,0.0032820497,0.005955279],"genre_scores_gemma":[0.7595486,0.0013976158,0.22600771,0.00032882366,0.00012109743,0.00012601823,0.0023833176,0.00027229675,0.00981453],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998723,0.000022419492,0.000007734799,0.000036315683,0.00003335885,0.000027778477],"domain_scores_gemma":[0.99979645,0.00006153841,0.000033520995,0.00002836297,0.000067293375,0.00001283041],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003076313,0.0007381929,0.0003090715,0.00076098996,0.0002740682,0.00058542355,0.0006997352,0.00062301883,0.0022351067],"category_scores_gemma":[0.00095702754,0.00028821322,0.0006490561,0.0008746602,0.0003024692,0.0010074367,0.0004677151,0.00095295184,0.0007303715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003004094,0.0001635815,0.0039678006,0.00021987992,0.00029160903,0.00026499634,0.0001504462,0.3700214,0.05268213,0.04721038,0.017632196,0.5070952],"study_design_scores_gemma":[0.000003335309,0.0000143477555,0.0005342577,0.0000061450296,0.000017687875,0.0000164543,0.000007096466,0.9837114,0.0025095714,0.0116360625,0.0015381831,0.0000054970233],"about_ca_topic_score_codex":0.011278371,"about_ca_topic_score_gemma":0.016620768,"teacher_disagreement_score":0.011278371,"about_ca_system_score_codex":0.0009908259,"about_ca_system_score_gemma":0.0005617895,"threshold_uncertainty_score":0.022425413},"labels":[],"label_agreement":null},{"id":"W4393018768","doi":"10.1049/cit2.12300","title":"GP‐FMLNet: A feature matrix learning network enhanced by glyph and phonetic information for Chinese sentiment analysis","year":2024,"lang":"en","type":"article","venue":"CAAI Transactions on Intelligence Technology","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Glyph (data visualization); Computer science; Feature (linguistics); Artificial intelligence; Sentiment analysis; Natural language processing; Pattern recognition (psychology); Visualization; Linguistics","score_opus":0.004678158519829099,"score_gpt":0.26967863644860096,"score_spread":0.26500047792877185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393018768","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18123195,0.0012930024,0.7994212,0.0011521355,0.000484666,0.00029135938,0.0014379434,0.008259852,0.0064278734],"genre_scores_gemma":[0.8068009,0.0005347095,0.17773177,0.0007272496,0.00024582475,0.0003009872,0.0035219064,0.00020063075,0.009935955],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998006,0.000041428222,0.000011089786,0.0000675649,0.000043772372,0.000035616904],"domain_scores_gemma":[0.99974495,0.00009087583,0.000022719109,0.000023775228,0.00010008838,0.000017498032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005230628,0.001024937,0.0005646217,0.000842054,0.00045356216,0.0005079173,0.0011225096,0.00084778544,0.0024458892],"category_scores_gemma":[0.0014757602,0.00028783703,0.0006386915,0.0008642985,0.00029482835,0.001098336,0.0007766385,0.0010841943,0.0008545439],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038610696,0.0002784084,0.00409099,0.00015499884,0.00015268227,0.00023586563,0.00012116984,0.17506793,0.014461162,0.0025252437,0.017890375,0.78463507],"study_design_scores_gemma":[0.000012604651,0.000049162663,0.00040794731,0.0000053756967,0.000016317992,0.0000136751605,0.000011336868,0.9960376,0.0014268067,0.0011190204,0.0008941561,0.0000059483796],"about_ca_topic_score_codex":0.010148828,"about_ca_topic_score_gemma":0.009098071,"teacher_disagreement_score":0.010148828,"about_ca_system_score_codex":0.0007427828,"about_ca_system_score_gemma":0.0006451882,"threshold_uncertainty_score":0.02017951},"labels":[],"label_agreement":null},{"id":"W4393423660","doi":"10.5281/zenodo.6523151","title":"Dataset: Characterizing Anti-Asian Rhetoric During The COVID-19 Pandemic: A Sentiment Analysis Case Study on Twitter","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Rhetoric; Coronavirus disease 2019 (COVID-19); Pandemic; Sentiment analysis; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; History; Computer science; Natural language processing; Linguistics; Virology; Biology; Medicine; Philosophy","score_opus":0.09524620877496874,"score_gpt":0.3257131129063965,"score_spread":0.23046690413142773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393423660","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026193518,0.00024408303,0.00081929093,0.0009325026,0.00027394746,0.00023265391,0.965888,0.0012617691,0.004154351],"genre_scores_gemma":[0.01576704,0.00011803551,0.0023510705,0.0002021254,0.000059284153,0.00031716833,0.9787319,0.000057063167,0.002396295],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99937755,0.00013055536,0.000090382186,0.00011888856,0.00019131466,0.00009125557],"domain_scores_gemma":[0.9988248,0.00034502672,0.0001387834,0.00017917922,0.00037165912,0.0001405281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000719984,0.0013521796,0.0005050443,0.001968718,0.0009699298,0.00088105403,0.0012028144,0.0016962828,0.0054637375],"category_scores_gemma":[0.0022188702,0.00020568237,0.00062516134,0.001788232,0.0003574324,0.00076447084,0.0011200556,0.0009800993,0.00826467],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034559958,0.00041623882,0.01440424,0.0010633375,0.00007521616,0.00058248977,0.00035466457,0.0017902448,0.0036500953,0.0008907156,0.9553425,0.021084666],"study_design_scores_gemma":[0.0005509105,0.0003405178,0.09113103,0.0003296761,0.00010718338,0.000970858,0.0020486997,0.01758016,0.0077935793,0.0018048952,0.87717384,0.00016875278],"about_ca_topic_score_codex":0.017764857,"about_ca_topic_score_gemma":0.045992196,"teacher_disagreement_score":0.017764857,"about_ca_system_score_codex":0.0011901339,"about_ca_system_score_gemma":0.00081249047,"threshold_uncertainty_score":0.035322905},"labels":[],"label_agreement":null},{"id":"W4393550040","doi":"10.5281/zenodo.10443022","title":"Bubble reachers and uncivil discourse in polarized online public sphere comments dataset","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto; McGill University","funders":"","keywords":"Public sphere; Bubble; Computer science; Political science","score_opus":0.08010044930994445,"score_gpt":0.30997817989707865,"score_spread":0.2298777305871342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393550040","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011613883,0.00017851389,0.00044888465,0.00029801117,0.00010422727,0.00010917707,0.98250246,0.00041421357,0.004330603],"genre_scores_gemma":[0.008878627,0.00007144104,0.001363246,0.000079604724,0.000028440705,0.00035296584,0.98622406,0.000061287436,0.0029404033],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987638,0.00033224307,0.0001272157,0.00024119458,0.00036927994,0.0001662725],"domain_scores_gemma":[0.99680114,0.0010932416,0.00034052038,0.00046950084,0.0010347317,0.0002608344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009035252,0.00094508723,0.00048345784,0.0033985225,0.0009278021,0.0014405581,0.00095897535,0.0012912884,0.017027982],"category_scores_gemma":[0.006046184,0.00020314417,0.0005830103,0.0035104274,0.0003285495,0.0009082646,0.0017522125,0.0010398766,0.026481813],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002215854,0.00021271166,0.012095443,0.0010522933,0.00004633746,0.000172292,0.0005534492,0.00050994445,0.00070487155,0.001303064,0.96052754,0.022600468],"study_design_scores_gemma":[0.00013464844,0.00009130558,0.06939343,0.0006094599,0.000045105324,0.000306503,0.0028116738,0.0039826096,0.0021438578,0.0014239496,0.9189701,0.00008733853],"about_ca_topic_score_codex":0.011536842,"about_ca_topic_score_gemma":0.03309461,"teacher_disagreement_score":0.017027982,"about_ca_system_score_codex":0.0011771825,"about_ca_system_score_gemma":0.0013645269,"threshold_uncertainty_score":0.05696422},"labels":[],"label_agreement":null},{"id":"W4393594508","doi":"10.36548/jaicn.2024.1.007","title":"YouTube Comment Sentiment Classification System","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence and Capsule Networks","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Sentiment analysis; Computer science; Information retrieval; Natural language processing","score_opus":0.05232969544101895,"score_gpt":0.2966720594464385,"score_spread":0.24434236400541953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393594508","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24163555,0.0023339724,0.02892973,0.0020853286,0.0024598655,0.0058212387,0.54299724,0.049388666,0.124348454],"genre_scores_gemma":[0.30342072,0.0013988691,0.059213467,0.0006700074,0.00087387394,0.003971612,0.56552535,0.0010465576,0.06387957],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993567,0.000055155506,0.00010572516,0.0001578699,0.00022778602,0.000096894946],"domain_scores_gemma":[0.99854493,0.00014150394,0.000130166,0.00006541051,0.0010378673,0.00008013591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054085226,0.001518414,0.0007287751,0.006605348,0.0010330997,0.00095036224,0.00064367056,0.00066400954,0.015825214],"category_scores_gemma":[0.0024931582,0.00017225886,0.00060890586,0.0024780969,0.00015200448,0.0011958389,0.0007768728,0.0004740644,0.0151617965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015948402,0.0003037383,0.03921546,0.002250478,0.00017075328,0.0016711017,0.00065522845,0.0009815112,0.026791427,0.0017984292,0.64538276,0.27918413],"study_design_scores_gemma":[0.00033516108,0.0006694262,0.21445948,0.0007302002,0.0004476266,0.0017962538,0.003009396,0.24863619,0.057360772,0.0026372394,0.4695935,0.0003247825],"about_ca_topic_score_codex":0.019916235,"about_ca_topic_score_gemma":0.024459647,"teacher_disagreement_score":0.019916235,"about_ca_system_score_codex":0.0011465925,"about_ca_system_score_gemma":0.0008178054,"threshold_uncertainty_score":0.052940607},"labels":[],"label_agreement":null},{"id":"W4393651146","doi":"10.5281/zenodo.6523152","title":"Dataset: Characterizing Anti-Asian Rhetoric During The COVID-19 Pandemic: A Sentiment Analysis Case Study on Twitter","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Pandemic; Rhetoric; Sentiment analysis; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Geography; Computer science; Biology; Natural language processing; Virology; Linguistics; Medicine; Philosophy; Outbreak","score_opus":0.09524620877496874,"score_gpt":0.3257131129063965,"score_spread":0.23046690413142773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393651146","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026193518,0.00024408303,0.00081929093,0.0009325026,0.00027394746,0.00023265391,0.965888,0.0012617691,0.004154351],"genre_scores_gemma":[0.01576704,0.00011803551,0.0023510705,0.0002021254,0.000059284153,0.00031716833,0.9787319,0.000057063167,0.002396295],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99937755,0.00013055536,0.000090382186,0.00011888856,0.00019131466,0.00009125557],"domain_scores_gemma":[0.9988248,0.00034502672,0.0001387834,0.00017917922,0.00037165912,0.0001405281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000719984,0.0013521796,0.0005050443,0.001968718,0.0009699298,0.00088105403,0.0012028144,0.0016962828,0.0054637375],"category_scores_gemma":[0.0022188702,0.00020568237,0.00062516134,0.001788232,0.0003574324,0.00076447084,0.0011200556,0.0009800993,0.00826467],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034559958,0.00041623882,0.01440424,0.0010633375,0.00007521616,0.00058248977,0.00035466457,0.0017902448,0.0036500953,0.0008907156,0.9553425,0.021084666],"study_design_scores_gemma":[0.0005509105,0.0003405178,0.09113103,0.0003296761,0.00010718338,0.000970858,0.0020486997,0.01758016,0.0077935793,0.0018048952,0.87717384,0.00016875278],"about_ca_topic_score_codex":0.017764857,"about_ca_topic_score_gemma":0.045992196,"teacher_disagreement_score":0.017764857,"about_ca_system_score_codex":0.0011901339,"about_ca_system_score_gemma":0.00081249047,"threshold_uncertainty_score":0.035322905},"labels":[],"label_agreement":null},{"id":"W4393760110","doi":"10.5281/zenodo.8039458","title":"Dataset for: Do you Cite What you Tweet? Investigating the relationship between tweeting and citing research articles","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Social media; Computer science; Information retrieval; Internet privacy; Data science; World Wide Web","score_opus":0.19761510178362998,"score_gpt":0.3459268780040485,"score_spread":0.1483117762204185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393760110","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010946459,0.00007634357,0.00025780898,0.00018148734,0.00006811352,0.00005203057,0.9967218,0.0003817763,0.001165911],"genre_scores_gemma":[0.0008594806,0.000034861656,0.00081436365,0.000054993037,0.000011249997,0.0001272879,0.9969234,0.00004164316,0.0011327069],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989374,0.0002237892,0.00015014771,0.00025181964,0.0002931401,0.00014357806],"domain_scores_gemma":[0.9980564,0.00051336054,0.00019272708,0.00043666514,0.00058080297,0.00022005379],"candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0013243197,0.0017155071,0.0009349087,0.00246118,0.0009732864,0.0015853122,0.002095494,0.0019131374,0.02730317],"category_scores_gemma":[0.0047433083,0.00036494495,0.0010761413,0.0034754733,0.00037334973,0.00091799017,0.0014682785,0.0017008325,0.04574145],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012085269,0.00008381103,0.0012451559,0.00043604127,0.00003357892,0.0000498046,0.000050379713,0.00040624122,0.00037128566,0.00064833515,0.99249035,0.004064239],"study_design_scores_gemma":[0.00044715437,0.000059013313,0.009627171,0.00020894619,0.0000501979,0.000176194,0.00021764004,0.0018866566,0.0010222256,0.0017082349,0.98454696,0.00004970377],"about_ca_topic_score_codex":0.014175144,"about_ca_topic_score_gemma":0.040466547,"teacher_disagreement_score":0.9986757,"about_ca_system_score_codex":0.001296778,"about_ca_system_score_gemma":0.001542971,"threshold_uncertainty_score":0.09133816},"labels":[],"label_agreement":null},{"id":"W4393881749","doi":"10.5281/zenodo.8039457","title":"Dataset for: Do you Cite What you Tweet? Investigating the relationship between tweeting and citing research articles","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Social media; Data science; Computer science; World Wide Web","score_opus":0.19761510178362998,"score_gpt":0.3459268780040485,"score_spread":0.1483117762204185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393881749","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010946459,0.00007634357,0.00025780898,0.00018148734,0.00006811352,0.00005203057,0.9967218,0.0003817763,0.001165911],"genre_scores_gemma":[0.0008594806,0.000034861656,0.00081436365,0.000054993037,0.000011249997,0.0001272879,0.9969234,0.00004164316,0.0011327069],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9989374,0.0002237892,0.00015014771,0.00025181964,0.0002931401,0.00014357806],"domain_scores_gemma":[0.9980564,0.00051336054,0.00019272708,0.00043666514,0.00058080297,0.00022005379],"candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0013243197,0.0017155071,0.0009349087,0.00246118,0.0009732864,0.0015853122,0.002095494,0.0019131374,0.02730317],"category_scores_gemma":[0.0047433083,0.00036494495,0.0010761413,0.0034754733,0.00037334973,0.00091799017,0.0014682785,0.0017008325,0.04574145],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012085269,0.00008381103,0.0012451559,0.00043604127,0.00003357892,0.0000498046,0.000050379713,0.00040624122,0.00037128566,0.00064833515,0.99249035,0.004064239],"study_design_scores_gemma":[0.00044715437,0.000059013313,0.009627171,0.00020894619,0.0000501979,0.000176194,0.00021764004,0.0018866566,0.0010222256,0.0017082349,0.98454696,0.00004970377],"about_ca_topic_score_codex":0.014175144,"about_ca_topic_score_gemma":0.040466547,"teacher_disagreement_score":0.9986757,"about_ca_system_score_codex":0.001296778,"about_ca_system_score_gemma":0.001542971,"threshold_uncertainty_score":0.09133816},"labels":[],"label_agreement":null},{"id":"W4394648832","doi":"10.48550/arxiv.2010.09574","title":"Machine Learning Evaluation of the Echo-Chamber Effect in Medical Forums","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Dalhousie University","funders":"","keywords":"Echo (communications protocol); Class (philosophy); Core (optical fiber); Computer science; Artificial intelligence; Unit (ring theory); Machine learning; Psychology; Mathematics education; Computer security; Telecommunications","score_opus":0.08552527401373115,"score_gpt":0.23473940637754548,"score_spread":0.1492141323638143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394648832","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94748247,0.00053934066,0.04332944,0.00070301804,0.00023239931,0.00021595396,0.00056324987,0.00041083305,0.0065232604],"genre_scores_gemma":[0.9915645,0.000046948106,0.007400248,0.000035366207,0.00007305628,0.000034320463,0.00040029845,0.000010506593,0.00043480296],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9912969,0.0058336966,0.00042446895,0.00069992745,0.0014277471,0.0003172745],"domain_scores_gemma":[0.901781,0.08133458,0.0052607935,0.0028093553,0.0069321855,0.0018820439],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019803792,0.0006741722,0.00062252145,0.0018483081,0.0005625791,0.0016080536,0.00065882015,0.0013433531,0.0013158213],"category_scores_gemma":[0.05662324,0.00013480986,0.0005540739,0.0007955419,0.0006519532,0.0016322061,0.0011427145,0.001114029,0.00043003925],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0054915785,0.0032007345,0.35441205,0.0006509288,0.0008247468,0.00026025373,0.0012239965,0.22207876,0.012786787,0.005561796,0.007863253,0.38564515],"study_design_scores_gemma":[0.00006707555,0.0010205348,0.055713,0.000037614638,0.00008423708,0.000057461908,0.00020410187,0.93423194,0.005630615,0.0020179579,0.0008910881,0.000044378525],"about_ca_topic_score_codex":0.0014697068,"about_ca_topic_score_gemma":0.0014222242,"teacher_disagreement_score":0.019803792,"about_ca_system_score_codex":0.0008748244,"about_ca_system_score_gemma":0.0005013537,"threshold_uncertainty_score":0.104733706},"labels":[],"label_agreement":null},{"id":"W4394856705","doi":"10.2196/preprints.59425","title":"Long COVID Discourse in Canada, the United States, and Europe: Topic Modeling and Sentiment Analysis of Twitter Data (Preprint)","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Social media; Timeline; Topic model; Coronavirus disease 2019 (COVID-19); Narrative; Tracking (education); Sentiment analysis; Perception; Public relations; Political science; History; Sociology; Psychology; Computer science; World Wide Web; Medicine; Linguistics; Artificial intelligence","score_opus":0.05456290852496999,"score_gpt":0.31078907949137896,"score_spread":0.256226170966409,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394856705","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9668505,0.00068411336,0.0048206314,0.0022633658,0.000093174946,0.00016961616,0.01984357,0.00026687726,0.005008124],"genre_scores_gemma":[0.9673519,0.00062220334,0.006986883,0.00020872233,0.00008227395,0.00016579364,0.02155533,0.000074416705,0.0029524968],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994741,0.00013714892,0.0000301509,0.00009497782,0.00014365547,0.000119937904],"domain_scores_gemma":[0.99747497,0.0014688513,0.00018462843,0.00007256236,0.0006568533,0.00014211141],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014855589,0.00045942346,0.00034756545,0.0020363233,0.001408301,0.0021329874,0.00045511778,0.0004368542,0.0010067616],"category_scores_gemma":[0.0044705993,0.00015585806,0.0006303498,0.003120486,0.00058244326,0.00076512387,0.0007306168,0.00067098776,0.00039166282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010648285,0.00038847272,0.69316095,0.0009070112,0.00048513082,0.0012663248,0.042987272,0.03287719,0.0092242565,0.0091051385,0.08149909,0.1270344],"study_design_scores_gemma":[0.000046054298,0.00007525542,0.56105816,0.00024528243,0.0001817516,0.00015966572,0.051073693,0.32748803,0.0037652932,0.0028202436,0.052922953,0.00016369998],"about_ca_topic_score_codex":0.80689925,"about_ca_topic_score_gemma":0.8162266,"teacher_disagreement_score":0.19310075,"about_ca_system_score_codex":0.006130455,"about_ca_system_score_gemma":0.005248064,"threshold_uncertainty_score":0.3884759},"labels":[],"label_agreement":null},{"id":"W4394897028","doi":"10.1109/access.2024.3386969","title":"Comparative Analysis of Deep Natural Networks and Large Language Models for Aspect-Based Sentiment Analysis","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Systems, Applications & Products in Data Processing (Canada)","funders":"Higher Education Commission, Pakistan","keywords":"Computer science; Natural language processing; Artificial intelligence; Sentiment analysis; Natural language","score_opus":0.02956948399539699,"score_gpt":0.34801256610212167,"score_spread":0.3184430821067247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394897028","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63911897,0.03226871,0.25475538,0.0097183725,0.0020323652,0.00045868332,0.014403444,0.011923562,0.035320465],"genre_scores_gemma":[0.9116504,0.003712063,0.05997138,0.0010295092,0.00029337945,0.00028141416,0.015965696,0.00045152966,0.006644549],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986523,0.00057173317,0.000094798044,0.00028817475,0.00025292282,0.0001400482],"domain_scores_gemma":[0.9954163,0.0031574504,0.00023152771,0.00035264812,0.00071881886,0.00012328329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003462533,0.0022872505,0.00081670104,0.0018069984,0.0004922037,0.001369992,0.0012176811,0.001264407,0.002797876],"category_scores_gemma":[0.010815433,0.00044036316,0.0010941869,0.0012389437,0.00049096573,0.003735649,0.0010096735,0.0019900023,0.0014430899],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012195148,0.00054620206,0.013572248,0.0011044749,0.00079578446,0.00039489145,0.00027904642,0.5635367,0.005038087,0.008431628,0.0353078,0.36977363],"study_design_scores_gemma":[0.000023723496,0.00012342974,0.0012999305,0.000046980276,0.000050105482,0.00003572326,0.00008182605,0.99054784,0.0012722699,0.0042534727,0.0022463012,0.000018449238],"about_ca_topic_score_codex":0.013443817,"about_ca_topic_score_gemma":0.022776308,"teacher_disagreement_score":0.013443817,"about_ca_system_score_codex":0.001840963,"about_ca_system_score_gemma":0.0011665134,"threshold_uncertainty_score":0.026731133},"labels":[],"label_agreement":null},{"id":"W4394912621","doi":"10.5267/j.ijdns.2024.3.006","title":"Sentiment analysis of Saudi e-commerce using naïve bayes algorithm and support vector machine","year":2024,"lang":"en","type":"article","venue":"International Journal of Data and Network Science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"AstraZeneca","keywords":"Naive Bayes classifier; Support vector machine; Bayes' theorem; Computer science; Sentiment analysis; Artificial intelligence; Algorithm; Machine learning; Data mining; Bayesian probability","score_opus":0.036790968575681496,"score_gpt":0.34938712293906016,"score_spread":0.3125961543633787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394912621","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8638054,0.0009406432,0.12539452,0.00042898557,0.00028131527,0.00040053634,0.0013057477,0.00089344906,0.006549355],"genre_scores_gemma":[0.9283412,0.00041921277,0.06703598,0.00006476441,0.00006591535,0.00013105398,0.0020368,0.000024920899,0.0018801739],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992618,0.00019018761,0.00011570453,0.00009546467,0.00027267003,0.00006422411],"domain_scores_gemma":[0.99885774,0.0003166767,0.0000965059,0.00004463701,0.0006546381,0.000029817007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012551108,0.00049806543,0.0005878718,0.0014493128,0.00038754963,0.0008905584,0.0003177877,0.00034668762,0.0010811801],"category_scores_gemma":[0.0024720258,0.00012990342,0.0007666565,0.0007094074,0.00015733422,0.0006102741,0.00020157034,0.00028062778,0.00070056086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017770156,0.00069755013,0.06954656,0.0007895799,0.0003568845,0.00073018,0.0008483369,0.03892362,0.043761373,0.0020894618,0.010852361,0.82962716],"study_design_scores_gemma":[0.00004713932,0.00030090133,0.033110157,0.00006600243,0.00011763569,0.00026289496,0.0008764674,0.94317377,0.017004875,0.0010834453,0.0039130184,0.00004364837],"about_ca_topic_score_codex":0.006127951,"about_ca_topic_score_gemma":0.0049969545,"teacher_disagreement_score":0.006127951,"about_ca_system_score_codex":0.0005078928,"about_ca_system_score_gemma":0.00055436156,"threshold_uncertainty_score":0.01218456},"labels":[],"label_agreement":null},{"id":"W4396758629","doi":"10.1145/3589334.3645668","title":"DualCL: Principled Supervised Contrastive Learning as Mutual Information Maximization for Text Classification","year":2024,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Mutual information; Artificial intelligence; Maximization; Natural language processing; Machine learning; Pattern recognition (psychology); Mathematics","score_opus":0.022431088023166346,"score_gpt":0.2795962578342131,"score_spread":0.25716516981104676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396758629","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009022191,0.00024178458,0.9881968,0.00026839026,0.00003255235,0.00007941034,0.00009655214,0.0010269275,0.001035393],"genre_scores_gemma":[0.415003,0.00030180483,0.5773786,0.00084880705,0.00031830283,0.0006659692,0.00095660606,0.0004307173,0.0040962617],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982911,0.00072253984,0.00006543144,0.0004253754,0.0003922477,0.000103293896],"domain_scores_gemma":[0.9966439,0.002134285,0.00027979407,0.00041243012,0.00040847427,0.00012117828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029213463,0.0010503633,0.001236866,0.0013311811,0.00053670513,0.0014572421,0.0028002453,0.0015037684,0.0025443472],"category_scores_gemma":[0.007787633,0.0005089954,0.00096686574,0.0012083349,0.0015705386,0.0029380177,0.0031690488,0.002606783,0.0012733466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006113952,0.00052385405,0.0025435016,0.00042126968,0.00021050732,0.00022540805,0.0004957951,0.261338,0.024161708,0.07876568,0.014153455,0.61654943],"study_design_scores_gemma":[0.0000187232,0.00006132243,0.00011352151,0.000008286269,0.000009160779,0.00002687554,0.000012719767,0.9731872,0.0020336874,0.023550468,0.00096895755,0.000009153085],"about_ca_topic_score_codex":0.0008793858,"about_ca_topic_score_gemma":0.0012790194,"teacher_disagreement_score":0.0029213463,"about_ca_system_score_codex":0.001119518,"about_ca_system_score_gemma":0.0009842624,"threshold_uncertainty_score":0.015449703},"labels":[],"label_agreement":null},{"id":"W4396912905","doi":"10.48550/arxiv.2405.06692","title":"Analyzing Language Bias Between French and English in Conventional Multilingual Sentiment Analysis Models","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sentiment analysis; Linguistics; Computer science; Natural language processing; Artificial intelligence; Philosophy","score_opus":0.09082824059050736,"score_gpt":0.233674772441511,"score_spread":0.14284653185100366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396912905","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9049248,0.0011571646,0.08213557,0.0016128642,0.00012238002,0.0000896315,0.0017115441,0.00036098942,0.007885084],"genre_scores_gemma":[0.98588204,0.00018632489,0.011040121,0.00021011154,0.000054646935,0.00005096973,0.0016170742,0.00003933478,0.00091928215],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99694353,0.0018684979,0.00013209546,0.0004220412,0.0004484485,0.00018542197],"domain_scores_gemma":[0.991084,0.006015761,0.0007803838,0.0006619717,0.0013125889,0.00014531564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008993691,0.0006031339,0.0004832899,0.0011718342,0.00069959444,0.0018952119,0.00034413757,0.00044372282,0.0013274014],"category_scores_gemma":[0.020160342,0.00015120304,0.00056380965,0.001026906,0.00066444575,0.0014833262,0.0010239171,0.00074777746,0.00047975985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020111394,0.00033719646,0.5385154,0.0005006527,0.0010028388,0.00069143705,0.0048676142,0.04884221,0.014756037,0.026119508,0.016783534,0.3455725],"study_design_scores_gemma":[0.00013679481,0.0006225327,0.2624448,0.00037986782,0.00044231073,0.000814862,0.003525973,0.6275599,0.015772354,0.053054135,0.035069086,0.0001773817],"about_ca_topic_score_codex":0.016209995,"about_ca_topic_score_gemma":0.018371982,"teacher_disagreement_score":0.016209995,"about_ca_system_score_codex":0.001458034,"about_ca_system_score_gemma":0.00077722524,"threshold_uncertainty_score":0.04756373},"labels":[],"label_agreement":null},{"id":"W4396957427","doi":"10.1007/s10515-024-00444-x","title":"A novel automated framework for fine-grained sentiment analysis of application reviews using deep neural networks","year":2024,"lang":"en","type":"article","venue":"Automated Software Engineering","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Sentiment analysis; Computer science; Deep neural networks; Artificial neural network; Artificial intelligence; Natural language processing","score_opus":0.020560672139496184,"score_gpt":0.29690099154942384,"score_spread":0.27634031940992765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396957427","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040480807,0.0006841487,0.9397098,0.00058180006,0.00022442159,0.00028099396,0.0018216833,0.012332534,0.0038837956],"genre_scores_gemma":[0.42792207,0.00052142487,0.5497129,0.00048821062,0.00030481405,0.00041637602,0.00666773,0.0004915414,0.013475019],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992544,0.000103637205,0.000055228178,0.00019838913,0.00027329987,0.00011501235],"domain_scores_gemma":[0.99902046,0.00018485948,0.00017416045,0.00008561469,0.0004777518,0.00005714906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073142146,0.00122447,0.000688973,0.0020676197,0.00040755546,0.0010082513,0.0010683149,0.00071604503,0.0021297622],"category_scores_gemma":[0.0018846993,0.00038420837,0.00072915055,0.0010769432,0.00022424653,0.0012080709,0.0010723074,0.0011814856,0.0018791613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026885082,0.00053483486,0.0064026155,0.00024759953,0.00022762587,0.00022357171,0.0001296382,0.039676484,0.053741563,0.004911233,0.035365716,0.85827035],"study_design_scores_gemma":[0.000012238063,0.000038455764,0.0013772071,0.000013133847,0.000025523812,0.000034886878,0.000024791712,0.984525,0.0073788227,0.0034417,0.003115691,0.000012429908],"about_ca_topic_score_codex":0.008885453,"about_ca_topic_score_gemma":0.023633804,"teacher_disagreement_score":0.008885453,"about_ca_system_score_codex":0.0008886203,"about_ca_system_score_gemma":0.0013667282,"threshold_uncertainty_score":0.017667472},"labels":[],"label_agreement":null},{"id":"W4399075343","doi":"10.1038/s41598-024-58944-5","title":"Predicting multi-label emojis, emotions, and sentiments in code-mixed texts using an emojifying sentiments framework","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Institute for Information and Communications Technology Promotion","keywords":"Emoji; Computer science; Sentiment analysis; Natural language processing; Artificial intelligence; Encoder; Code (set theory); Task (project management); Social media; World Wide Web","score_opus":0.059669544477686645,"score_gpt":0.33874858042534717,"score_spread":0.27907903594766054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399075343","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75411713,0.0018234306,0.21648423,0.0009278261,0.00063165295,0.00027881065,0.010109454,0.0061677922,0.009459702],"genre_scores_gemma":[0.879186,0.0004332232,0.09117911,0.00026249717,0.00030845977,0.00023024094,0.01790286,0.00023507598,0.010262494],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973565,0.00006155139,0.000018299386,0.00008791139,0.00005160469,0.00004491308],"domain_scores_gemma":[0.9993266,0.0002532129,0.00009474398,0.00006397818,0.00019995327,0.00006159396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005379669,0.0015155163,0.00038902528,0.0011764219,0.00039717657,0.0006262509,0.00043679777,0.0007822004,0.0012389673],"category_scores_gemma":[0.001997566,0.00021181912,0.0007045735,0.0005793383,0.00027221948,0.0011141478,0.0005891563,0.00094090437,0.00120472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021974675,0.001203591,0.063081436,0.0010945253,0.0004565285,0.0014108451,0.0013633012,0.062294096,0.18283062,0.0034982949,0.06303309,0.6175362],"study_design_scores_gemma":[0.000035125642,0.0003279978,0.03219986,0.00006030982,0.00012124524,0.0002457668,0.00052965095,0.92744493,0.027370714,0.0040600826,0.0075450903,0.00005920275],"about_ca_topic_score_codex":0.0021820508,"about_ca_topic_score_gemma":0.0060648415,"teacher_disagreement_score":0.0021820508,"about_ca_system_score_codex":0.00034466764,"about_ca_system_score_gemma":0.00030995024,"threshold_uncertainty_score":0.0043387413},"labels":[],"label_agreement":null},{"id":"W4399174169","doi":"10.1145/3660043.3660210","title":"Comprehensive study on deep-learning-based online course review analysis","year":2023,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence; Sentiment analysis; Online learning; Context (archaeology); Transformer; Online course; Machine learning; Multimedia; Mathematics education; Psychology; Engineering","score_opus":0.05386966131133928,"score_gpt":0.3579187554792576,"score_spread":0.3040490941679183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399174169","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90619177,0.013921903,0.06813402,0.0014809136,0.00027494022,0.00019112765,0.0016797777,0.0008318422,0.007293763],"genre_scores_gemma":[0.9676271,0.003113888,0.023290982,0.00016919755,0.00015490307,0.000052344672,0.0026946284,0.000052007803,0.0028448533],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9985682,0.00056028157,0.00012118909,0.00022203354,0.00043485098,0.000093487855],"domain_scores_gemma":[0.99582374,0.0020250455,0.00030410275,0.00028381796,0.0014277444,0.00013554996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019735722,0.0003843071,0.00035019426,0.001378541,0.00024682778,0.0006576148,0.00041426436,0.0003114506,0.00057074067],"category_scores_gemma":[0.0070767533,0.00015792745,0.0005307943,0.0011899179,0.00015404505,0.0013920148,0.0003113449,0.0005014326,0.00037010017],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000368649,0.00070952874,0.0664272,0.0011685351,0.00031412015,0.00046953486,0.000723829,0.0217929,0.02529347,0.0019000319,0.012802359,0.86802983],"study_design_scores_gemma":[0.000028638917,0.0007028423,0.12880182,0.0002528874,0.0002792881,0.00046390935,0.0010333468,0.79617006,0.03734621,0.0020099175,0.03284245,0.00006858068],"about_ca_topic_score_codex":0.008306303,"about_ca_topic_score_gemma":0.010052905,"teacher_disagreement_score":0.008306303,"about_ca_system_score_codex":0.0007054197,"about_ca_system_score_gemma":0.0008813668,"threshold_uncertainty_score":0.01651591},"labels":[],"label_agreement":null},{"id":"W4399267137","doi":"10.1007/978-3-031-61966-3_3","title":"Automatic Verbalizer for Extracting Fine-Grained Customer Opinions from Non-English Social Media Comments","year":2024,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Social media; Computer science; Information retrieval; Sentiment analysis; Natural language processing; Data science; Artificial intelligence; World Wide Web","score_opus":0.049607234966070314,"score_gpt":0.3214743613390805,"score_spread":0.2718671263730102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399267137","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.108279705,0.0013664246,0.78895295,0.0013943387,0.00097105943,0.0011786176,0.023008091,0.048958767,0.02589002],"genre_scores_gemma":[0.24629694,0.0007884964,0.6797619,0.00044612036,0.00062369613,0.0012197539,0.042691268,0.0019819017,0.026190013],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988856,0.00031709502,0.00012812299,0.00026467664,0.00027965612,0.00012485115],"domain_scores_gemma":[0.9969091,0.0013056464,0.0002592489,0.00017221986,0.001262521,0.00009127766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012113601,0.0011461431,0.0006747684,0.0027385813,0.0007149391,0.0018689842,0.0008425355,0.000745507,0.012511567],"category_scores_gemma":[0.003574892,0.00039804305,0.0006588933,0.0020230368,0.00037717988,0.0021310546,0.0015201935,0.0012104968,0.016039003],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059283536,0.00024246876,0.0033324768,0.00091171643,0.00012912096,0.00055936794,0.0013610822,0.0010890818,0.1371934,0.005595424,0.06302204,0.785971],"study_design_scores_gemma":[0.00024618616,0.0008468523,0.02667252,0.00051775493,0.00064661616,0.0018360629,0.0077338107,0.42940614,0.30277705,0.01784762,0.21116383,0.00030549025],"about_ca_topic_score_codex":0.0017347364,"about_ca_topic_score_gemma":0.0036315739,"teacher_disagreement_score":0.012511567,"about_ca_system_score_codex":0.0005315687,"about_ca_system_score_gemma":0.0009942979,"threshold_uncertainty_score":0.041855395},"labels":[],"label_agreement":null},{"id":"W4399267329","doi":"10.1007/978-3-031-61147-6_5","title":"ContentRank: Towards a Scoring and Ranking System for Screen Media Products Using Critical Reception Data","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario College of Art and Design","funders":"","keywords":"Computer science; Ranking (information retrieval); Scoring system; Information retrieval","score_opus":0.10481690206359777,"score_gpt":0.32124945586493325,"score_spread":0.21643255380133547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399267329","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.074811265,0.00095741113,0.7768348,0.00051953876,0.0003475585,0.0012065201,0.031927664,0.099276215,0.01411904],"genre_scores_gemma":[0.17973068,0.0004751803,0.7405311,0.0001605847,0.00027739353,0.00087684765,0.050601486,0.0017957916,0.025550945],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99820995,0.00028170744,0.00013405096,0.00025137913,0.000982928,0.0001399827],"domain_scores_gemma":[0.9969741,0.0008776044,0.0002430034,0.00025583478,0.0014667725,0.00018255234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018308138,0.0017935289,0.0010512978,0.008091583,0.000699082,0.0027111499,0.0014096334,0.0010865404,0.0085640615],"category_scores_gemma":[0.0056281486,0.0004732032,0.00071859075,0.0046479283,0.00030489222,0.0028246052,0.0015612883,0.0009069982,0.012766981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005323531,0.00038370298,0.013415267,0.00043232564,0.00014810826,0.00016070009,0.00021013604,0.006019841,0.025494803,0.0033882977,0.09960176,0.8502127],"study_design_scores_gemma":[0.00019080495,0.00067223836,0.026250701,0.00018812467,0.00024992754,0.0005144144,0.0006222153,0.8081257,0.06553174,0.015766691,0.081664115,0.00022344278],"about_ca_topic_score_codex":0.005587349,"about_ca_topic_score_gemma":0.01130453,"teacher_disagreement_score":0.0085640615,"about_ca_system_score_codex":0.00085286726,"about_ca_system_score_gemma":0.0010892054,"threshold_uncertainty_score":0.028649628},"labels":[],"label_agreement":null},{"id":"W4399393270","doi":"10.36948/ijfmr.2024.v06i03.20551","title":"Product Review Sentiment Analysis","year":2024,"lang":"en","type":"article","venue":"International Journal For Multidisciplinary Research","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Sentiment analysis; Product (mathematics); Computer science; Natural language processing; Mathematics","score_opus":0.13899947781950647,"score_gpt":0.5114749012859422,"score_spread":0.3724754234664357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399393270","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4934135,0.009313305,0.15920891,0.004536926,0.0024272613,0.0035312744,0.100199126,0.0075816712,0.21978803],"genre_scores_gemma":[0.81600696,0.003133372,0.09312201,0.0007547189,0.0011180089,0.0012771653,0.04600243,0.00044828706,0.038137],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978084,0.0002861407,0.00021950199,0.00025547275,0.0013040764,0.00012651618],"domain_scores_gemma":[0.99537104,0.00062538136,0.00072058296,0.00018381042,0.002993329,0.00010571897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017733206,0.00049572065,0.00044567237,0.0050617103,0.00039987886,0.0016384809,0.0003421507,0.000273848,0.0066627525],"category_scores_gemma":[0.0057428116,0.000109114364,0.00048141726,0.0035002672,0.00016061227,0.00074244593,0.0004910439,0.00042092637,0.0054551596],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037112704,0.00018997333,0.04888023,0.0017820892,0.00035171962,0.0003207536,0.00060747453,0.00162982,0.03367798,0.004983396,0.18141574,0.7257897],"study_design_scores_gemma":[0.00009761444,0.00056670845,0.31150633,0.00053906813,0.0005363901,0.001721849,0.0017733631,0.077680625,0.06235522,0.0073476587,0.53570753,0.0001676878],"about_ca_topic_score_codex":0.0016577566,"about_ca_topic_score_gemma":0.0025033026,"teacher_disagreement_score":0.0066627525,"about_ca_system_score_codex":0.00057350454,"about_ca_system_score_gemma":0.00079115405,"threshold_uncertainty_score":0.022289157},"labels":[],"label_agreement":null},{"id":"W4399795905","doi":"10.1016/j.eswa.2024.124523","title":"Label-semantics enhanced multi-layer heterogeneous graph convolutional network for Aspect Sentiment Quadruplet Extraction","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Science and Technology Service Network Plan; Key Science and Technology Program of Shaanxi Province; National Natural Science Foundation of China; Department of Science and Technology of Sichuan Province; Organization Department of Sichuan Provincial Party Committee; Ministry of Science and Technology of the People's Republic of China","keywords":"Computer science; Graph; Artificial intelligence; Semantics (computer science); Layer (electronics); Natural language processing; Theoretical computer science; Programming language; Chemistry","score_opus":0.038061305910356225,"score_gpt":0.32247039089730317,"score_spread":0.28440908498694695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399795905","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18830827,0.0014693422,0.7762946,0.0006478761,0.00040229844,0.0002122442,0.0032749502,0.014025743,0.015364687],"genre_scores_gemma":[0.73768073,0.0007543892,0.23002681,0.00045374146,0.00014148069,0.00014785535,0.010294241,0.00052578485,0.019974928],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985397,0.000013391973,0.00000763163,0.00005107338,0.000038525304,0.000035369063],"domain_scores_gemma":[0.9998505,0.00002943305,0.00001697632,0.00002753858,0.000063716936,0.000011766221],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021659546,0.00091095007,0.00041927546,0.0010348039,0.00040016335,0.00055307825,0.0006630474,0.0006303179,0.0029687423],"category_scores_gemma":[0.0004854732,0.00024364854,0.0006516858,0.0009243283,0.0001966576,0.00093276624,0.0006034784,0.00072698,0.0014693086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005191426,0.0004248995,0.0045902105,0.00024977268,0.00021465178,0.00035981432,0.0001464802,0.04631526,0.12301239,0.007687878,0.030310567,0.7861689],"study_design_scores_gemma":[0.00002085906,0.00007357776,0.0023550799,0.000020901993,0.00009591609,0.00008553063,0.000046899553,0.9584616,0.026276937,0.006268132,0.0062760487,0.000018651672],"about_ca_topic_score_codex":0.010010943,"about_ca_topic_score_gemma":0.026011776,"teacher_disagreement_score":0.010010943,"about_ca_system_score_codex":0.0005592046,"about_ca_system_score_gemma":0.0007241795,"threshold_uncertainty_score":0.019905388},"labels":[],"label_agreement":null},{"id":"W4399829559","doi":"10.18280/isi.290305","title":"ERS – GARNET: An Ensemble Recommendation System for Sentiment Analysis Using Gated Attention-Based Recurrent Networks","year":2024,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sentiment analysis; Computer science; Artificial intelligence","score_opus":0.02793778054021472,"score_gpt":0.28021865428593073,"score_spread":0.252280873745716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399829559","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07921625,0.0026982257,0.8272342,0.0006693334,0.0008179315,0.00038434952,0.007443563,0.074612476,0.006923675],"genre_scores_gemma":[0.3204192,0.0015915696,0.61947197,0.0007367953,0.00044654898,0.00031555857,0.021693207,0.0018037497,0.033521388],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996358,0.00005023819,0.000027884218,0.00011232155,0.00012972938,0.000043943233],"domain_scores_gemma":[0.9995146,0.000111634225,0.000038685976,0.000073796204,0.00023275892,0.000028621713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008159334,0.001566542,0.001146676,0.0016445874,0.0004561802,0.00069551344,0.0015024365,0.00093573116,0.0058110547],"category_scores_gemma":[0.0014147386,0.00065134175,0.00080427754,0.0010869063,0.00015442447,0.001293615,0.0006285384,0.0011520776,0.005171726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00078323716,0.00065108,0.0038111622,0.00024931433,0.00072104763,0.00023929292,0.000088618304,0.039074443,0.041413188,0.0012078236,0.06727265,0.84448814],"study_design_scores_gemma":[0.00007335912,0.00015161157,0.0017692123,0.000023905717,0.00018601703,0.00007293506,0.000023241431,0.96759933,0.017973715,0.0017323026,0.010349148,0.000045216668],"about_ca_topic_score_codex":0.019741124,"about_ca_topic_score_gemma":0.04765122,"teacher_disagreement_score":0.019741124,"about_ca_system_score_codex":0.0005403658,"about_ca_system_score_gemma":0.0006548754,"threshold_uncertainty_score":0.0392524},"labels":[],"label_agreement":null},{"id":"W4399855193","doi":"10.18280/isi.290303","title":"Subject Detection of Algerian Posts for Opinion Analysis","year":2024,"lang":"fr","type":"article","venue":"Ingénierie des systèmes d information","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Subject (documents); Computer science; Political science; Information retrieval; World Wide Web","score_opus":0.02495419215151564,"score_gpt":0.27503363752679166,"score_spread":0.25007944537527604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399855193","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91066283,0.001645355,0.024301343,0.0008646049,0.0005658257,0.00047788513,0.035564423,0.0030536766,0.022863984],"genre_scores_gemma":[0.90605366,0.000758579,0.045043625,0.00015260001,0.00041446087,0.00038634083,0.034115702,0.00013780557,0.012937244],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9996063,0.000070867405,0.000035914265,0.000090343754,0.00012211462,0.00007447502],"domain_scores_gemma":[0.99922836,0.00013464344,0.00011542272,0.00005838911,0.00042014508,0.000043092496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042427305,0.0005306931,0.0002519099,0.0030490102,0.00037373343,0.00055163173,0.00019213131,0.0002967205,0.0025863317],"category_scores_gemma":[0.001052712,0.000060891904,0.00037313622,0.0012001061,0.00015084102,0.00048836845,0.00033897752,0.0002514794,0.0018669147],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001022717,0.00052128674,0.15488033,0.0014724771,0.00018124148,0.0010044615,0.0020241968,0.0014369462,0.12231521,0.0018655895,0.066942796,0.64633274],"study_design_scores_gemma":[0.000053486438,0.00057820935,0.6687139,0.00020430643,0.0001944394,0.0010064287,0.0038698274,0.09497809,0.10012119,0.002244977,0.12795514,0.000079986334],"about_ca_topic_score_codex":0.0038708171,"about_ca_topic_score_gemma":0.010279532,"teacher_disagreement_score":0.0038708171,"about_ca_system_score_codex":0.0003951674,"about_ca_system_score_gemma":0.00027494258,"threshold_uncertainty_score":0.008652151},"labels":[],"label_agreement":null},{"id":"W4399897544","doi":"10.18280/ria.380315","title":"TED Talks Comments Sentiment Classification Using Machine Learning Algorithms","year":2024,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Sentiment analysis; Artificial intelligence; Machine learning; Algorithm","score_opus":0.08639332210848887,"score_gpt":0.3269384908722326,"score_spread":0.2405451687637437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399897544","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7338374,0.0017475361,0.23367392,0.0009325824,0.0006330717,0.001082071,0.005248967,0.0030020173,0.019842332],"genre_scores_gemma":[0.8732296,0.00061983604,0.11555532,0.0001083621,0.00022857664,0.0004380754,0.005253644,0.0000657401,0.004500819],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984956,0.00043545905,0.00017430368,0.00020059991,0.0005819051,0.00011210508],"domain_scores_gemma":[0.9972389,0.0010846196,0.0003379833,0.000112597256,0.0011636179,0.00006230938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019646399,0.00081691437,0.00063376536,0.0037520987,0.0004553988,0.0012187771,0.00046084588,0.00050811825,0.0014030254],"category_scores_gemma":[0.0053805625,0.00013696098,0.00074540684,0.0017431695,0.00018380911,0.0010199589,0.0004727861,0.000508527,0.0014057811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009320545,0.00051432144,0.055946056,0.00060612155,0.00023104735,0.0003148495,0.00088064396,0.020227822,0.024689836,0.0022573746,0.016932925,0.87646705],"study_design_scores_gemma":[0.000042784086,0.00035850122,0.05988045,0.00015380642,0.00010587871,0.0002469928,0.0015408539,0.9011196,0.020601396,0.0025065327,0.013381631,0.00006162442],"about_ca_topic_score_codex":0.0024746351,"about_ca_topic_score_gemma":0.0025571208,"teacher_disagreement_score":0.0037520987,"about_ca_system_score_codex":0.0008011337,"about_ca_system_score_gemma":0.00044698425,"threshold_uncertainty_score":0.0103901625},"labels":[],"label_agreement":null},{"id":"W4400153820","doi":"10.1007/978-3-031-60916-9_5","title":"Methodology","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in social networks","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Philosophy","score_opus":0.059188291345251595,"score_gpt":0.31749383131323256,"score_spread":0.25830553996798095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400153820","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011885272,0.0015569142,0.32037598,0.0036493484,0.0027015985,0.020596653,0.032857656,0.0041821995,0.6021943],"genre_scores_gemma":[0.07816935,0.0024139907,0.27934593,0.0073730587,0.0010372909,0.037467577,0.041689564,0.003035577,0.5494677],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99533737,0.0011713514,0.00030081757,0.001333539,0.001493814,0.00036309444],"domain_scores_gemma":[0.99422616,0.0012964678,0.00025653982,0.0013833982,0.0024789732,0.0003583763],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0052227634,0.001158051,0.00073774764,0.0032850106,0.0019695144,0.0044123414,0.0023254512,0.0014715063,0.23679923],"category_scores_gemma":[0.015799843,0.0006106796,0.0008916287,0.0020870108,0.0010235367,0.0016785605,0.0029604028,0.0013927614,0.10384552],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005953998,0.0005400179,0.005928884,0.0018165244,0.00007745527,0.00037613692,0.0022352585,0.0015005697,0.0038353761,0.16034415,0.22703081,0.59571946],"study_design_scores_gemma":[0.0000981016,0.0001644294,0.0018161931,0.0005108575,0.000045964785,0.00023829908,0.00083642534,0.0014716834,0.0023793492,0.0381848,0.9542237,0.000030270852],"about_ca_topic_score_codex":0.002956123,"about_ca_topic_score_gemma":0.0038679743,"teacher_disagreement_score":0.76320076,"about_ca_system_score_codex":0.0017394628,"about_ca_system_score_gemma":0.006595459,"threshold_uncertainty_score":0.7921723},"labels":[],"label_agreement":null},{"id":"W4400488140","doi":"10.1109/access.2024.3426329","title":"Uncovering Concerns of Citizens Through Machine Learning and Social Network Sentiment Analysis","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Latent Dirichlet allocation; Computer science; Artificial intelligence; Machine learning; Cluster analysis; Topic model; Software deployment; Empowerment; Data science; Political science","score_opus":0.03607952095741991,"score_gpt":0.3371834822340259,"score_spread":0.301103961276606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400488140","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.952735,0.0006472912,0.025659574,0.0020556229,0.00011625901,0.0003322751,0.005914294,0.0002147189,0.012324778],"genre_scores_gemma":[0.9762231,0.00048500672,0.016719548,0.000244142,0.00014583918,0.00021792337,0.003528134,0.000037738675,0.002398582],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99861705,0.00059755344,0.000122023084,0.0002159365,0.0003213326,0.00012617851],"domain_scores_gemma":[0.9934069,0.0036428145,0.0013600129,0.00024698538,0.001168691,0.00017456293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021892325,0.00050297583,0.0003524077,0.004644038,0.00071716844,0.0014772486,0.0002843699,0.0005296102,0.001306822],"category_scores_gemma":[0.006675142,0.00012276375,0.00042771868,0.0026447074,0.00038021477,0.0017974713,0.00080869056,0.00063060573,0.0007339421],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041540313,0.00032051903,0.549365,0.001088101,0.00021221806,0.0008200065,0.021853158,0.0042580487,0.024249474,0.005770856,0.019814026,0.37183324],"study_design_scores_gemma":[0.00003056706,0.00028701665,0.6675919,0.00042183843,0.00017564646,0.00046417725,0.05801873,0.1937736,0.010011095,0.0111790355,0.057885066,0.00016138513],"about_ca_topic_score_codex":0.0061906017,"about_ca_topic_score_gemma":0.011636503,"teacher_disagreement_score":0.0061906017,"about_ca_system_score_codex":0.000932158,"about_ca_system_score_gemma":0.0006299774,"threshold_uncertainty_score":0.012309134},"labels":[],"label_agreement":null},{"id":"W4400779789","doi":"10.22148/001c.118497","title":"Revisiting Weimar Film Reviewers’ Sentiments: Integrating Lexicon-Based Sentiment Analysis with Large Language Models","year":2024,"lang":"en","type":"article","venue":"Journal of Cultural Analytics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Lexicon; Sentiment analysis; Natural language processing; Computer science; Linguistics; Artificial intelligence; Philosophy","score_opus":0.0232972015043026,"score_gpt":0.3005641542562081,"score_spread":0.2772669527519055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400779789","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.116636425,0.0008011655,0.8423432,0.00501838,0.0008724097,0.0006386467,0.00275353,0.01582944,0.015106831],"genre_scores_gemma":[0.53740263,0.0005221339,0.44654906,0.00091940316,0.00042801318,0.00036155002,0.004298964,0.0020252108,0.007493012],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99786156,0.0011123857,0.00015413963,0.0003238539,0.00046566786,0.00008238801],"domain_scores_gemma":[0.9928839,0.0039279205,0.00056618283,0.00069876853,0.0017336466,0.00018948478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042354087,0.0010939763,0.0006869272,0.0027498964,0.0007227093,0.0041568163,0.0009225655,0.0007023561,0.0031144887],"category_scores_gemma":[0.01841103,0.0005858243,0.0011777327,0.0013957043,0.0006215539,0.0036485167,0.0017200721,0.0017802971,0.0033355262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055995357,0.00035209476,0.025364235,0.0010966538,0.00060126005,0.0013004233,0.006086429,0.04210194,0.05320316,0.01837301,0.06303407,0.7879269],"study_design_scores_gemma":[0.000044103108,0.00010840422,0.008440372,0.00014058026,0.00015049445,0.00029000157,0.0018431549,0.9150753,0.014623717,0.02572784,0.0334318,0.00012413313],"about_ca_topic_score_codex":0.0052828523,"about_ca_topic_score_gemma":0.010614185,"teacher_disagreement_score":0.0052828523,"about_ca_system_score_codex":0.001082245,"about_ca_system_score_gemma":0.0012391242,"threshold_uncertainty_score":0.022399187},"labels":[],"label_agreement":null},{"id":"W4401042968","doi":"10.18653/v1/2024.semeval-1.164","title":"PetKaz at SemEval-2024 Task 3: Advancing Emotion Classification with an LLM for Emotion-Cause Pair Extraction in Conversations","year":2024,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"SemEval; Task (project management); Computer science; Natural language processing; Artificial intelligence; Extraction (chemistry); Emotion detection; Speech recognition; Emotion recognition; Chromatography; Engineering","score_opus":0.04026484820025493,"score_gpt":0.3169309115703094,"score_spread":0.27666606337005445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401042968","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17660342,0.006400274,0.4884029,0.005375158,0.008197852,0.0024417033,0.07316368,0.1951281,0.04428692],"genre_scores_gemma":[0.36518484,0.0008002135,0.44075635,0.0019314842,0.0012275971,0.0020352977,0.14145118,0.010261196,0.036351707],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9963814,0.0014113591,0.0001456968,0.0010437121,0.00061417796,0.00040354594],"domain_scores_gemma":[0.99644464,0.0013617923,0.000119760945,0.0007886596,0.00092747214,0.00035767828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004917699,0.004457501,0.001541849,0.0018367935,0.0014892394,0.0037160995,0.0022754322,0.003413423,0.022552919],"category_scores_gemma":[0.011701989,0.0006541512,0.0025314328,0.00079088495,0.0006260044,0.0032316088,0.0045445086,0.0037536197,0.02244961],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002005498,0.0007474292,0.0048872135,0.0011280916,0.0005631194,0.000685909,0.0008229715,0.013343963,0.041873973,0.004189592,0.4549071,0.47484517],"study_design_scores_gemma":[0.0007502874,0.0011218302,0.015096282,0.00026241015,0.00038183952,0.0013692968,0.0011535031,0.6963119,0.074568614,0.02019227,0.18847677,0.00031507062],"about_ca_topic_score_codex":0.0065382738,"about_ca_topic_score_gemma":0.010864224,"teacher_disagreement_score":0.022552919,"about_ca_system_score_codex":0.00127831,"about_ca_system_score_gemma":0.0016471847,"threshold_uncertainty_score":0.07544696},"labels":[],"label_agreement":null},{"id":"W4401043067","doi":"10.18653/v1/2024.starsem-1.12","title":"Identifying Emotional and Polar Concepts via Synset Translation","year":2024,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Machine Intelligence Institute","keywords":"Translation (biology); Computer science; Polar; Physics; Chemistry","score_opus":0.037674560793783014,"score_gpt":0.31975742537918395,"score_spread":0.28208286458540094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401043067","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19383661,0.0026258042,0.6022558,0.001217291,0.0021090317,0.0018401513,0.105708584,0.022479834,0.06792692],"genre_scores_gemma":[0.2228149,0.0012452077,0.6059273,0.0005096692,0.00041676845,0.0022506153,0.15216954,0.0036204138,0.011045577],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983525,0.00033625375,0.00033723694,0.00048473533,0.0004030872,0.00008614152],"domain_scores_gemma":[0.99546695,0.0015976942,0.00050377246,0.0006686458,0.0016033294,0.0001596583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014180094,0.001609705,0.0007192379,0.0075901356,0.001374991,0.0019750502,0.0006864805,0.0007285137,0.01546838],"category_scores_gemma":[0.007962432,0.0007282138,0.0013259805,0.0050167516,0.00075972057,0.004451151,0.0029813757,0.001200037,0.009692878],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009969824,0.00041371983,0.015923217,0.0052462756,0.00028936705,0.0025747935,0.0072891875,0.0027420183,0.15922444,0.061189488,0.14901753,0.5950931],"study_design_scores_gemma":[0.00027074415,0.0005092648,0.029481193,0.0013423498,0.00038673537,0.0046355855,0.006410433,0.07376811,0.10160052,0.12677293,0.6544822,0.00033995992],"about_ca_topic_score_codex":0.0011257784,"about_ca_topic_score_gemma":0.0029010384,"teacher_disagreement_score":0.01546838,"about_ca_system_score_codex":0.00081508205,"about_ca_system_score_gemma":0.0013647745,"threshold_uncertainty_score":0.051746905},"labels":[],"label_agreement":null},{"id":"W4401115349","doi":"10.1371/journal.pdig.0000545","title":"Evaluating automatic annotation of lexicon-based models for stance detection of M-pox tweets from May 1st to Sep 5th, 2022","year":2024,"lang":"en","type":"article","venue":"PLOS Digital Health","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada); Regional Municipality of Niagara; Brock University; Response Biomedical (Canada); University of Toronto; York University","funders":"International Development Research Centre; Styrelsen för Internationellt Utvecklingssamarbete","keywords":"Lexicon; Annotation; Computer science; Artificial intelligence; Natural language processing; Labeled data; Transformer; Social media; Machine learning; Engineering","score_opus":0.10976796167818852,"score_gpt":0.38802400234011997,"score_spread":0.27825604066193144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401115349","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89823174,0.0019293169,0.060910612,0.0010526433,0.0004904957,0.00043457642,0.010672005,0.0147134885,0.011565005],"genre_scores_gemma":[0.92210704,0.00034400815,0.045077365,0.00029491016,0.000086154556,0.00025836227,0.027452072,0.00033918582,0.0040408666],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987255,0.00048450317,0.000099670804,0.0004328718,0.00015484425,0.00010253729],"domain_scores_gemma":[0.9969317,0.0019141458,0.00018429502,0.00030672236,0.0005470906,0.00011604711],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024563768,0.0013852293,0.0005829828,0.001897368,0.00055617816,0.0012464551,0.0011142674,0.0010865373,0.001553255],"category_scores_gemma":[0.0069748145,0.00036099125,0.0007872799,0.0007354551,0.0004339803,0.0015814677,0.0008319573,0.0010217528,0.002026986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003534436,0.0019746118,0.13095671,0.001463465,0.00080002897,0.0008593812,0.0016166378,0.22100255,0.03198283,0.0034911386,0.055240788,0.5470774],"study_design_scores_gemma":[0.000073992436,0.0002640422,0.01238644,0.00006558806,0.00008276682,0.00009840743,0.00035892316,0.9728889,0.007940767,0.0011225536,0.0046780985,0.000039551287],"about_ca_topic_score_codex":0.023342539,"about_ca_topic_score_gemma":0.043855093,"teacher_disagreement_score":0.023342539,"about_ca_system_score_codex":0.0016799929,"about_ca_system_score_gemma":0.0012405484,"threshold_uncertainty_score":0.046413362},"labels":[],"label_agreement":null},{"id":"W4401223289","doi":"10.1007/978-3-031-64273-9_23","title":"A Tweet Data Analysis for Detecting Emerging Operational Risks","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Data science; Risk analysis (engineering); Business","score_opus":0.15741350926402276,"score_gpt":0.3609078461877813,"score_spread":0.20349433692375857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401223289","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027103964,0.0028374663,0.93562543,0.0011402968,0.0008798464,0.000449382,0.0075832647,0.0060321093,0.018348236],"genre_scores_gemma":[0.11828404,0.0028113679,0.8249201,0.00039414,0.0008311684,0.00046657992,0.008295509,0.0006362799,0.04336073],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994985,0.00008374153,0.000037276146,0.00009600473,0.0002487764,0.00003568133],"domain_scores_gemma":[0.998998,0.0005935244,0.00007486431,0.00008964716,0.00020714565,0.00003688401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006836844,0.0008570783,0.0005816272,0.0030714485,0.00043378674,0.0016682484,0.00063091924,0.00084361003,0.006626417],"category_scores_gemma":[0.0023079147,0.00028503625,0.0008972736,0.003051323,0.00024825425,0.0014927044,0.000661432,0.0010258133,0.005219123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016460042,0.00014776865,0.0039995695,0.00038234045,0.00013363644,0.00029388705,0.0002037551,0.006768656,0.03248398,0.011679819,0.054135464,0.88960654],"study_design_scores_gemma":[0.000051905045,0.00042939564,0.016495828,0.00024259884,0.00027320583,0.0016843599,0.0004977866,0.7194328,0.05244315,0.0457146,0.16256496,0.000169323],"about_ca_topic_score_codex":0.0017080155,"about_ca_topic_score_gemma":0.0029511394,"teacher_disagreement_score":0.006626417,"about_ca_system_score_codex":0.00038333435,"about_ca_system_score_gemma":0.0003960004,"threshold_uncertainty_score":0.022167623},"labels":[],"label_agreement":null},{"id":"W4401632311","doi":"10.22215/etd/2023-16105","title":"Location-Based Sentiment Analysis using Bayesian Networks on COVID-19 Twitter Data","year":2023,"lang":"en","type":"dissertation","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Carleton University","funders":"","keywords":"Microblogging; Sentiment analysis; Coronavirus disease 2019 (COVID-19); Social media; Naive Bayes classifier; Pandemic; Bayesian probability; Computer science; Classifier (UML); World Wide Web; Data science; Information retrieval; Artificial intelligence; Medicine","score_opus":0.09698141526409826,"score_gpt":0.375991622430788,"score_spread":0.2790102071666897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401632311","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7991979,0.0016038177,0.16893809,0.0030347118,0.0005792293,0.00042872704,0.014535054,0.0012652491,0.010417164],"genre_scores_gemma":[0.95020205,0.00043993827,0.03540926,0.00015061865,0.0002463374,0.00011719001,0.010174617,0.000047825375,0.0032122405],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902534,0.00036425394,0.00007679723,0.00023614573,0.00017655153,0.00012099104],"domain_scores_gemma":[0.99826837,0.0009861342,0.00023437712,0.00008754392,0.00034058135,0.000082961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016029448,0.00075932423,0.00060599745,0.0019400673,0.00057439186,0.0010705384,0.0005957175,0.000659653,0.0016217745],"category_scores_gemma":[0.005188689,0.00029247135,0.00084190717,0.0012285929,0.0002656603,0.0011986626,0.0006618608,0.0010444059,0.001316532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013619582,0.00060172006,0.2460054,0.00043677734,0.0005163017,0.0010476074,0.0015515005,0.281368,0.009570033,0.008502458,0.040327616,0.40871066],"study_design_scores_gemma":[0.000012899302,0.000053949014,0.022530211,0.00003501862,0.000025292484,0.000056667363,0.0004145007,0.97102225,0.0005805556,0.0027950145,0.0024517602,0.000021859407],"about_ca_topic_score_codex":0.028296584,"about_ca_topic_score_gemma":0.039811086,"teacher_disagreement_score":0.028296584,"about_ca_system_score_codex":0.0011704792,"about_ca_system_score_gemma":0.0005813515,"threshold_uncertainty_score":0.056263745},"labels":[],"label_agreement":null},{"id":"W4401731837","doi":"10.1016/j.jretconser.2024.104040","title":"Research on the impact of streamers’ linguistic emotional valence on live streaming performance in live streaming shopping environments","year":2024,"lang":"en","type":"article","venue":"Journal of Retailing and Consumer Services","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Live streaming; Emotional valence; Valence (chemistry); Streaming current; Video streaming; Psychology; Computer science; Multimedia; Cognition; Materials science; Physics; Neuroscience; Real-time computing; Nanotechnology","score_opus":0.043175092385143846,"score_gpt":0.33229858424488223,"score_spread":0.2891234918597384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401731837","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99336034,0.00008084075,0.00041071567,0.00010631009,0.0000198572,0.000010327287,0.00006309287,0.0000058959376,0.005942705],"genre_scores_gemma":[0.9984444,0.00008956775,0.000251686,0.000039725746,0.00002396444,0.0000061769574,0.00007143818,0.000005899183,0.0010671136],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99963546,0.0001297072,0.000021012364,0.000050331877,0.00011403809,0.00004942158],"domain_scores_gemma":[0.9962541,0.0017176835,0.0007076268,0.00008436587,0.00087198516,0.00036431514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070197636,0.00019161521,0.00013699062,0.00035991744,0.00033372614,0.0015353346,0.00018685666,0.00032018655,0.0031930946],"category_scores_gemma":[0.005024858,0.00008894876,0.00016290206,0.00039671056,0.000264949,0.0008411334,0.00035179898,0.00045447293,0.00047092137],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025149614,0.0010996267,0.77304876,0.00045609285,0.00034233218,0.0008579146,0.017490406,0.001099979,0.05908264,0.0024804706,0.0030403521,0.13848643],"study_design_scores_gemma":[0.0000149982925,0.00040516042,0.97599924,0.000055274315,0.00014644174,0.00014334645,0.012893986,0.0036807046,0.003589038,0.0006046945,0.002436583,0.00003064651],"about_ca_topic_score_codex":0.0015231147,"about_ca_topic_score_gemma":0.00262132,"teacher_disagreement_score":0.0031930946,"about_ca_system_score_codex":0.00033079073,"about_ca_system_score_gemma":0.0001878524,"threshold_uncertainty_score":0.010681927},"labels":[],"label_agreement":null},{"id":"W4402314381","doi":"10.23977/jaip.2024.070308","title":"A Sentiment Analysis Framework Integrating Systemic Functional Grammar and Appraisal Theory","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Systemic functional grammar; Systemic functional linguistics; Computer science; Grammar; Appraisal theory; Natural language processing; Linguistics; Psychology; Philosophy; Neuroscience","score_opus":0.03968067122095253,"score_gpt":0.35484816853118684,"score_spread":0.3151674973102343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402314381","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011274168,0.0003858373,0.95575434,0.0021506848,0.00012724777,0.0002563237,0.00019349344,0.00020851832,0.029649379],"genre_scores_gemma":[0.440056,0.00064883125,0.5515243,0.0005146311,0.00032099494,0.00073755195,0.00036982357,0.00012507432,0.005702769],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982023,0.0009696651,0.000107837746,0.00025666077,0.00036401945,0.00009952715],"domain_scores_gemma":[0.9978725,0.0011331794,0.00019608732,0.00009877132,0.000603908,0.00009564903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048943153,0.0010031674,0.00061908,0.0032770808,0.0011889696,0.0031630564,0.0008804364,0.00083821977,0.003077341],"category_scores_gemma":[0.005421647,0.00030639788,0.0013933695,0.0017532753,0.0038777606,0.003941627,0.0013547144,0.0013097154,0.000742297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000218835,0.000033306005,0.0016772015,0.00018401719,0.00003844903,0.0002613853,0.0032477544,0.00699478,0.001983697,0.92328435,0.003848834,0.05842423],"study_design_scores_gemma":[0.00001654263,0.00004482098,0.0012284655,0.00011544702,0.000034783956,0.00014994094,0.0011203167,0.061627924,0.0004972961,0.91169673,0.023433857,0.000033954602],"about_ca_topic_score_codex":0.002893411,"about_ca_topic_score_gemma":0.0025997828,"teacher_disagreement_score":0.0048943153,"about_ca_system_score_codex":0.0020080225,"about_ca_system_score_gemma":0.0024105946,"threshold_uncertainty_score":0.025883913},"labels":[],"label_agreement":null},{"id":"W4402468717","doi":"10.1016/j.nlp.2024.100105","title":"Personality and emotion—A comprehensive analysis using contextual text embeddings","year":2024,"lang":"en","type":"article","venue":"Natural Language Processing Journal","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"New York Institute of Technology","funders":"","keywords":"Personality; Psychology; Natural language processing; Cognitive psychology; Computer science; Social psychology","score_opus":0.021775286337649503,"score_gpt":0.32170208357638164,"score_spread":0.2999267972387321,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402468717","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91708714,0.0020079527,0.06751524,0.00039950118,0.00029308072,0.00012337093,0.0065495144,0.00069845055,0.005325807],"genre_scores_gemma":[0.9790835,0.00039221862,0.015106227,0.000041776962,0.000121175144,0.000069104644,0.0036660484,0.000040719093,0.0014794188],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943334,0.00016381973,0.00006169572,0.00014791019,0.0001395968,0.0000535894],"domain_scores_gemma":[0.9987457,0.0005116413,0.00022687993,0.00016128123,0.00026361513,0.00009088392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047046776,0.00049288257,0.0002589019,0.001608716,0.00024316399,0.00068484,0.00015185916,0.0002715489,0.0012382034],"category_scores_gemma":[0.002587012,0.000085664826,0.0003864386,0.0012555986,0.00020423625,0.0011276288,0.0005188089,0.0003557176,0.0006475149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013004529,0.00064733176,0.34571272,0.00082498323,0.0004430349,0.0016742594,0.0023729773,0.011102544,0.056396507,0.0044097635,0.015529345,0.55958605],"study_design_scores_gemma":[0.000020176134,0.00085660256,0.70291275,0.00017142681,0.0002852707,0.0028749115,0.0026225704,0.23968942,0.014192589,0.0059430217,0.03032077,0.00011039274],"about_ca_topic_score_codex":0.0007064696,"about_ca_topic_score_gemma":0.0009708618,"teacher_disagreement_score":0.001608716,"about_ca_system_score_codex":0.00016909752,"about_ca_system_score_gemma":0.00012484488,"threshold_uncertainty_score":0.004142165},"labels":[],"label_agreement":null},{"id":"W4402543817","doi":"10.62051/ijcsit.v4n1.15","title":"Transformer-Based Video Comment Analysis","year":2024,"lang":"en","type":"article","venue":"International Journal of Computer Science and Information Technology","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mount Saint Vincent University","funders":"","keywords":"Automatic summarization; Computer science; Transformer; Viewpoints; Preprocessor; Sentiment analysis; Data science; Human multitasking; Data pre-processing; Artificial intelligence; Natural language processing","score_opus":0.007275949513026446,"score_gpt":0.26689190434234783,"score_spread":0.25961595482932137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402543817","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14255159,0.0005375517,0.83003604,0.00060401857,0.00029756152,0.00072514726,0.00823853,0.008072147,0.008937469],"genre_scores_gemma":[0.76662993,0.00041211353,0.21109205,0.00014365031,0.00024862197,0.0003881082,0.011469175,0.0003542964,0.009262024],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991918,0.0002122687,0.00005595514,0.00019767456,0.00026031947,0.000081988655],"domain_scores_gemma":[0.99725395,0.0007963135,0.00023962901,0.0002023604,0.0014136117,0.000094031515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010028406,0.0007929761,0.00038258065,0.0027013856,0.00033465802,0.0007971522,0.0007437135,0.00045525446,0.0040844157],"category_scores_gemma":[0.004843876,0.00014016742,0.0007975579,0.0015088373,0.00024705284,0.0013905552,0.00066004094,0.00064360554,0.0026960736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012719469,0.00029275,0.021447405,0.00073289475,0.00022829058,0.0005908436,0.0015006498,0.04793763,0.075617954,0.008833492,0.025165657,0.81638044],"study_design_scores_gemma":[0.000028541046,0.000187005,0.010161175,0.00002916431,0.000079697464,0.00017748964,0.000856824,0.9466892,0.023598868,0.0052501825,0.012891035,0.00005089894],"about_ca_topic_score_codex":0.007203087,"about_ca_topic_score_gemma":0.008344458,"teacher_disagreement_score":0.007203087,"about_ca_system_score_codex":0.00076439325,"about_ca_system_score_gemma":0.00066442386,"threshold_uncertainty_score":0.0143223405},"labels":[],"label_agreement":null},{"id":"W4402683397","doi":"10.18653/v1/2024.sighan-1.13","title":"ZZU-NLP at SIGHAN-2024 dimABSA Task: Aspect-Based Sentiment Analysis with Coarse-to-Fine In-context Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Artificial intelligence; Task (project management); Natural language processing; Context (archaeology); Sentiment analysis; Engineering; History","score_opus":0.011233202503825571,"score_gpt":0.2495884170763642,"score_spread":0.23835521457253864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402683397","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20303243,0.0060366984,0.48519427,0.0054537207,0.004469081,0.0024525519,0.08818947,0.15637901,0.04879282],"genre_scores_gemma":[0.37838954,0.000924278,0.39632827,0.0019222876,0.00074643735,0.0022320617,0.1836467,0.0048727402,0.030937811],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99818367,0.00060813676,0.00008930379,0.00068457134,0.000279482,0.00015480263],"domain_scores_gemma":[0.99821246,0.00065400556,0.00006846625,0.00046008144,0.00044783595,0.00015724763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024821032,0.0035856087,0.0016223189,0.0013035492,0.0013797331,0.0023962213,0.002271815,0.002969019,0.019681575],"category_scores_gemma":[0.0068476456,0.0008162584,0.0019801937,0.0011245518,0.00044869757,0.0033847338,0.0031580566,0.0033503694,0.020080306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013062926,0.00088113925,0.00603926,0.0008286353,0.0003695637,0.00079746475,0.0003061317,0.017682467,0.023798471,0.0033203103,0.39269575,0.55197465],"study_design_scores_gemma":[0.0005588975,0.00039422663,0.0070339954,0.00009336765,0.0001399687,0.00044303364,0.00037771103,0.8680361,0.021956258,0.013267613,0.0875783,0.000120477845],"about_ca_topic_score_codex":0.012147593,"about_ca_topic_score_gemma":0.019021316,"teacher_disagreement_score":0.019681575,"about_ca_system_score_codex":0.0012455656,"about_ca_system_score_gemma":0.0015979694,"threshold_uncertainty_score":0.06584144},"labels":[],"label_agreement":null},{"id":"W4402693486","doi":"10.2196/57395","title":"Public Health Discussions on Social Media: Evaluating Automated Sentiment Analysis Methods","year":2024,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Preprint; Social media; Sentiment analysis; Public health; Data science; Computer science; Political science; World Wide Web; Medicine; Artificial intelligence; Nursing","score_opus":0.40492036512750224,"score_gpt":0.5863962860619636,"score_spread":0.18147592093446135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402693486","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93978673,0.0014009305,0.029009134,0.0016917861,0.00045280435,0.0071808943,0.0049297865,0.0016366779,0.013911303],"genre_scores_gemma":[0.885984,0.00081731455,0.09988896,0.0007458047,0.00039781138,0.006395201,0.0042264643,0.00017967979,0.0013646492],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.95097667,0.034900766,0.0036329797,0.0023492055,0.00752842,0.0006118589],"domain_scores_gemma":[0.69073015,0.25630847,0.016272224,0.0059958347,0.028432045,0.0022613613],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.06252015,0.0012572586,0.00088987773,0.006718717,0.0012201407,0.0032958728,0.00155096,0.0014553289,0.002500029],"category_scores_gemma":[0.15169853,0.00047084887,0.001247782,0.003173499,0.00079605484,0.0035890809,0.0026477946,0.0011399465,0.001088261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.010818974,0.005818288,0.25811216,0.009211198,0.0018020382,0.0005445354,0.019544894,0.017874928,0.009215376,0.003119208,0.02130222,0.6426362],"study_design_scores_gemma":[0.0027065806,0.012090588,0.3929783,0.002922264,0.0017111248,0.00061277114,0.020248707,0.50268066,0.020645544,0.011505999,0.031325113,0.0005723084],"about_ca_topic_score_codex":0.0024441052,"about_ca_topic_score_gemma":0.002703019,"teacher_disagreement_score":0.93747985,"about_ca_system_score_codex":0.0020359783,"about_ca_system_score_gemma":0.0014940184,"threshold_uncertainty_score":0.3306421},"labels":[],"label_agreement":null},{"id":"W4402930602","doi":"10.18280/mmep.110927","title":"Enhancing the Diversity of Smart Reply Suggestions: A Novel Approach Combining Text Classification and Post-Processing Techniques for Real Conversations in Bahasa Indonesia","year":2024,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Diversity (politics); Computer science; Data science; Natural language processing; Psychology; Sociology; Anthropology","score_opus":0.04145764755637347,"score_gpt":0.24427962389786895,"score_spread":0.20282197634149549,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402930602","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.554421,0.00063623284,0.43275946,0.0011577008,0.0002627085,0.0003277228,0.0009353703,0.0045512854,0.0049486044],"genre_scores_gemma":[0.8473222,0.00016755056,0.14774425,0.00013195517,0.00018035651,0.00013952749,0.0008596491,0.00016178869,0.0032926733],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889153,0.0004159175,0.0000841796,0.00024781103,0.00024966986,0.000110896246],"domain_scores_gemma":[0.99651223,0.0015930977,0.0004213761,0.00023512571,0.0010566757,0.0001814933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001303364,0.0008503157,0.00074894127,0.001977793,0.00062233966,0.00097001204,0.00071587344,0.00065611483,0.001176682],"category_scores_gemma":[0.0037909173,0.0002201653,0.0006338702,0.0011581205,0.00028045205,0.0012675528,0.0007326936,0.0008477423,0.0011810361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013609737,0.0005979037,0.025445051,0.0004248786,0.00015684885,0.00061682257,0.0031711997,0.011395028,0.08172808,0.0010173537,0.00713536,0.8669505],"study_design_scores_gemma":[0.00005743904,0.00044046066,0.04313969,0.00003564237,0.00024245806,0.0003515827,0.003383359,0.91031194,0.030261768,0.0024088586,0.009270147,0.000096645614],"about_ca_topic_score_codex":0.002672823,"about_ca_topic_score_gemma":0.0043684565,"teacher_disagreement_score":0.002672823,"about_ca_system_score_codex":0.00044420143,"about_ca_system_score_gemma":0.0005326563,"threshold_uncertainty_score":0.0068929195},"labels":[],"label_agreement":null},{"id":"W4403417412","doi":"10.1007/s13278-024-01356-0","title":"Predicting customer sentiment: the fusion of deep learning and a fuzzy system for sentiment analysis of Arabic text","year":2024,"lang":"en","type":"article","venue":"Social Network Analysis and Mining","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Sentiment analysis; Artificial intelligence; Arabic; Natural language processing; Computer science; Deep learning; Fusion; Fuzzy logic; Machine learning; Linguistics; Philosophy","score_opus":0.010048477157166615,"score_gpt":0.25942108108148576,"score_spread":0.24937260392431915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403417412","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54768765,0.0009627294,0.43636438,0.0010129187,0.00033380796,0.0001770888,0.0012348322,0.0025364275,0.009690225],"genre_scores_gemma":[0.92596513,0.0002518306,0.0684233,0.00017183808,0.00010783628,0.000057622696,0.00084739714,0.000037667553,0.0041373507],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981755,0.000031343137,0.000013267406,0.000038650935,0.000051591105,0.0000476275],"domain_scores_gemma":[0.99962294,0.00009488964,0.000037991475,0.000018945957,0.0001969646,0.000028212708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005334531,0.0006046875,0.0004079206,0.0009262784,0.00030964278,0.00065776584,0.00052865926,0.00057280256,0.0020456626],"category_scores_gemma":[0.0010457435,0.0001769971,0.00042791708,0.000600581,0.00013258147,0.0008889777,0.00051369227,0.0006724541,0.00097646937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007415259,0.00081864145,0.016767139,0.0001309885,0.0002237943,0.0001826603,0.0001719396,0.04547934,0.04363136,0.0016261915,0.009982915,0.88024354],"study_design_scores_gemma":[0.000012307604,0.000120537086,0.0038089075,0.000013109226,0.000044261193,0.00003194716,0.00005351964,0.987028,0.007016689,0.0011570065,0.00070012384,0.000013628486],"about_ca_topic_score_codex":0.0064606457,"about_ca_topic_score_gemma":0.009048656,"teacher_disagreement_score":0.0064606457,"about_ca_system_score_codex":0.00048727522,"about_ca_system_score_gemma":0.0004798723,"threshold_uncertainty_score":0.012846053},"labels":[],"label_agreement":null},{"id":"W4403541664","doi":"10.1016/j.eswa.2024.125556","title":"IFusionQuad: A novel framework for improved aspect-based sentiment quadruple analysis in dialogue contexts with advanced feature integration and contextual CloBlock","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Department of Science and Technology of Sichuan Province; Ministry of Science and Technology of the People's Republic of China; Science and Technology Service Network Plan; National Natural Science Foundation of China","keywords":"Computer science; Sentiment analysis; Feature (linguistics); Artificial intelligence; Natural language processing; Human–computer interaction; Linguistics","score_opus":0.011994315867215279,"score_gpt":0.2825469148215361,"score_spread":0.2705525989543208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403541664","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034702057,0.00021195147,0.98158365,0.00005340228,0.000048243244,0.00011163371,0.0004934533,0.012538269,0.0014891261],"genre_scores_gemma":[0.10586624,0.000273101,0.8838072,0.00016820597,0.00009215785,0.0002666472,0.002238669,0.002985413,0.0043024397],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991099,0.00016722846,0.000065489854,0.0002400862,0.00031147103,0.000105766405],"domain_scores_gemma":[0.99936277,0.00018655848,0.000057754776,0.00012932095,0.00021035412,0.000053169064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009949565,0.0013187533,0.0010506565,0.0020410644,0.00078820845,0.0023341752,0.0017034303,0.00074155536,0.0072361412],"category_scores_gemma":[0.003156442,0.00065373204,0.0013510173,0.0013351891,0.0005562381,0.00272866,0.0027768973,0.0015836864,0.0032791328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006553744,0.00021760479,0.0040474334,0.0007717777,0.00022807765,0.0006379356,0.001657604,0.012713649,0.063555315,0.035937604,0.029346239,0.85023135],"study_design_scores_gemma":[0.00007756401,0.00016847123,0.0029531508,0.00017868949,0.00017988241,0.000545198,0.00066300656,0.79438394,0.03664649,0.057713617,0.1062924,0.00019751849],"about_ca_topic_score_codex":0.00515497,"about_ca_topic_score_gemma":0.009550104,"teacher_disagreement_score":0.0072361412,"about_ca_system_score_codex":0.0004767358,"about_ca_system_score_gemma":0.0010664373,"threshold_uncertainty_score":0.024207354},"labels":[],"label_agreement":null},{"id":"W4403798860","doi":"10.21015/vtse.v12i3.1907","title":"Optimisation of Sentiment Analysis for E-Commerce","year":2024,"lang":"en","type":"article","venue":"VFAST Transactions on Software Engineering","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Sentiment analysis; E-commerce; Computer science; Business; Natural language processing; World Wide Web","score_opus":0.01685957247153525,"score_gpt":0.24834989948205471,"score_spread":0.23149032701051947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403798860","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.083325565,0.0021302886,0.8911812,0.000855491,0.0005140178,0.0004159663,0.0006614979,0.0029804758,0.017935488],"genre_scores_gemma":[0.5618669,0.0009772994,0.42799175,0.0002998917,0.00021733997,0.00034563424,0.0015767128,0.00041304986,0.006311442],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994301,0.00019075992,0.000050909908,0.00007837332,0.00018619442,0.00006360996],"domain_scores_gemma":[0.99913955,0.00032903044,0.0001045674,0.00004490959,0.0003626302,0.000019221437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012602834,0.00086660375,0.0007750796,0.0010246094,0.00029545865,0.0011117968,0.00049511954,0.0005943873,0.004398417],"category_scores_gemma":[0.0030197548,0.00035903053,0.0010258987,0.0008338852,0.00025177374,0.000776455,0.00041993085,0.000618294,0.0022199757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034814992,0.00030026512,0.004287008,0.00055217254,0.0001872491,0.00015841886,0.00013759521,0.1910266,0.047480866,0.004508139,0.013007658,0.73800594],"study_design_scores_gemma":[0.000024065468,0.00009798743,0.0017115041,0.000026376758,0.000026586575,0.00004210169,0.00007018552,0.9863729,0.0053415126,0.002579023,0.003695046,0.000012669716],"about_ca_topic_score_codex":0.0021165854,"about_ca_topic_score_gemma":0.00173926,"teacher_disagreement_score":0.004398417,"about_ca_system_score_codex":0.0005175728,"about_ca_system_score_gemma":0.00050011725,"threshold_uncertainty_score":0.014714122},"labels":[],"label_agreement":null},{"id":"W4403892528","doi":"10.1111/1911-3846.12986","title":"Discretionary dissemination on Twitter","year":2024,"lang":"en","type":"article","venue":"Contemporary Accounting Research","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Peking University; Brock University; Chinese University of Hong Kong; Queen's University; Ministry of Education, India; University of Toronto; Houston Advanced Research Center","keywords":"Business; Internet privacy; Political science; Computer science","score_opus":0.09388043192403439,"score_gpt":0.40795991861100683,"score_spread":0.31407948668697244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403892528","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.936528,0.00082933914,0.013482144,0.00372313,0.00025925724,0.00010331983,0.009172173,0.00047934699,0.03542327],"genre_scores_gemma":[0.99398524,0.00019521918,0.0014904693,0.00012623798,0.00015807251,0.000029232833,0.0012867018,0.000032104395,0.0026966403],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99916756,0.00030036416,0.00006183121,0.0001375776,0.0002479022,0.00008481894],"domain_scores_gemma":[0.99127144,0.0049864766,0.0020088614,0.0006700625,0.0008106341,0.0002524338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074919435,0.0002099087,0.00024123625,0.0019236249,0.0004997537,0.0014066899,0.00030165078,0.0004560526,0.004523003],"category_scores_gemma":[0.010654179,0.00013719023,0.00017594242,0.002380923,0.00031309185,0.0018966168,0.0008138256,0.0005540559,0.0015058002],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013113361,0.00023860783,0.48208553,0.000919827,0.00020026928,0.0012747123,0.009017951,0.014415023,0.029129354,0.028101351,0.042599637,0.39070645],"study_design_scores_gemma":[0.000062265426,0.0003202797,0.58745426,0.00038550093,0.00016485523,0.0012418409,0.008396046,0.21524313,0.021260967,0.026953937,0.13828214,0.00023483817],"about_ca_topic_score_codex":0.0026733342,"about_ca_topic_score_gemma":0.002815068,"teacher_disagreement_score":0.004523003,"about_ca_system_score_codex":0.00051585905,"about_ca_system_score_gemma":0.00023016454,"threshold_uncertainty_score":0.015130997},"labels":[],"label_agreement":null},{"id":"W4403899590","doi":"10.18280/mmep.111028","title":"Enhancing Decision Making Through Aspect Based Sentiment Analysis Using Deep Learning Models","year":2024,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Sentiment analysis; Deep learning; Machine learning; Natural language processing; Data science","score_opus":0.04212953272632698,"score_gpt":0.26651260085680506,"score_spread":0.2243830681304781,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403899590","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.088365644,0.00037055204,0.90439963,0.0008588046,0.00018561231,0.00006285142,0.00018614039,0.0006458507,0.004924869],"genre_scores_gemma":[0.8414512,0.000429671,0.15408295,0.0003392853,0.00014480935,0.00005046196,0.00033199898,0.00008524748,0.0030843057],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996037,0.00009928607,0.000029525914,0.00006636555,0.00014568785,0.000055431883],"domain_scores_gemma":[0.9986908,0.00062891486,0.00014687543,0.000072624905,0.00040859633,0.00005219758],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009049558,0.00064947316,0.0005458918,0.0005609184,0.0002384212,0.0013698087,0.00051860616,0.0005916122,0.0018797357],"category_scores_gemma":[0.0033471484,0.00023960185,0.0005107309,0.00051854906,0.00022307283,0.0016666728,0.0006690357,0.0014596323,0.0006360333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004477481,0.00074703083,0.0056608734,0.00029254874,0.00029369205,0.00015079885,0.00023879932,0.21594496,0.06522555,0.013878986,0.009587406,0.68753165],"study_design_scores_gemma":[0.000005770292,0.000027218266,0.00032110052,0.000010020816,0.00002423007,0.000008748008,0.000019379915,0.9878731,0.0040997434,0.0069991313,0.0006064466,0.0000051356706],"about_ca_topic_score_codex":0.001545559,"about_ca_topic_score_gemma":0.0029913425,"teacher_disagreement_score":0.0018797357,"about_ca_system_score_codex":0.0004814271,"about_ca_system_score_gemma":0.0005338849,"threshold_uncertainty_score":0.00628829},"labels":[],"label_agreement":null},{"id":"W4404051437","doi":"10.5539/ijel.v14n6p167","title":"How Covid-19 Has Changed Safety in the Car Transportation Sector: A Corpus-Assisted Analysis of Non-Financial Reports","year":2024,"lang":"en","type":"article","venue":"International Journal of English Linguistics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Università Degli Studi di Modena e Reggio Emila","keywords":"Coronavirus disease 2019 (COVID-19); Business; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Financial sector; Finance; Virology; Medicine; Internal medicine","score_opus":0.03639661884125522,"score_gpt":0.30408628491195605,"score_spread":0.2676896660707008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404051437","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89933604,0.0077788183,0.00826196,0.008337502,0.00045184375,0.0006389699,0.021825083,0.0002708065,0.053098902],"genre_scores_gemma":[0.93889403,0.004474757,0.021174893,0.0007044183,0.00017321318,0.000980542,0.026378354,0.00041332,0.0068065384],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9929894,0.0036765807,0.000798338,0.00066936284,0.0015069416,0.00035940664],"domain_scores_gemma":[0.94299114,0.044962592,0.0037718865,0.0022577608,0.005516877,0.00049962156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010568564,0.00044579367,0.00036184024,0.009793578,0.0034684334,0.0058817514,0.0009775888,0.0014292247,0.0031944776],"category_scores_gemma":[0.037979092,0.00036890004,0.00034748556,0.012294102,0.0033902745,0.0038077775,0.004123111,0.0020301628,0.0009847821],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018836309,0.00017055286,0.031262375,0.0035606525,0.00004590254,0.003954383,0.7976314,0.0005428376,0.007026217,0.029916123,0.031887267,0.09381397],"study_design_scores_gemma":[0.000030934145,0.000044655495,0.07734949,0.0033296372,0.00005999743,0.0010369746,0.48229992,0.002224277,0.0034012764,0.0030277092,0.4270939,0.00010116213],"about_ca_topic_score_codex":0.0339386,"about_ca_topic_score_gemma":0.048708454,"teacher_disagreement_score":0.0339386,"about_ca_system_score_codex":0.004358223,"about_ca_system_score_gemma":0.004625446,"threshold_uncertainty_score":0.067482114},"labels":[],"label_agreement":null},{"id":"W4404145943","doi":"10.1007/978-3-031-73125-9_9","title":"Analyzing E-Commerce Dynamics: Customer Satisfaction, Revenue Prediction, and Sentiment Analysis in Retail","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Revenue; Business; Dynamics (music); Customer satisfaction; Marketing; Advertising; Psychology; Finance","score_opus":0.012118078154177155,"score_gpt":0.23169969888726524,"score_spread":0.2195816207330881,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404145943","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48018295,0.057497613,0.3767432,0.010259661,0.0014259297,0.0001360655,0.0016531217,0.0014547097,0.07064673],"genre_scores_gemma":[0.8788891,0.016434303,0.067952015,0.00051261345,0.0010998026,0.000065555476,0.0014666404,0.00020572299,0.033374295],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998659,0.00003194038,0.000008213269,0.000033742715,0.000048009733,0.00001217188],"domain_scores_gemma":[0.99962604,0.00024871185,0.000029882201,0.000015750375,0.000062199244,0.000017408305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045509002,0.00045003812,0.00040637545,0.0006807749,0.00022189636,0.0013316503,0.00043947957,0.00052005704,0.0027365386],"category_scores_gemma":[0.001282474,0.00027582952,0.00038838273,0.0016558187,0.00034502172,0.002077742,0.00035273237,0.00075745926,0.001044901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030439877,0.00038404675,0.03286794,0.00039026988,0.0001737208,0.0002834596,0.000495765,0.036036428,0.007427722,0.027866565,0.041670464,0.8520992],"study_design_scores_gemma":[0.0000186204,0.00019929136,0.052701805,0.00018395018,0.00009454001,0.0004728971,0.0006353051,0.82238287,0.005601965,0.09051598,0.027125344,0.00006737411],"about_ca_topic_score_codex":0.0023264496,"about_ca_topic_score_gemma":0.002983843,"teacher_disagreement_score":0.0027365386,"about_ca_system_score_codex":0.00036449294,"about_ca_system_score_gemma":0.00022994718,"threshold_uncertainty_score":0.009154618},"labels":[],"label_agreement":null},{"id":"W4404518565","doi":"10.1609/aies.v7i1.31645","title":"Legitimating Emotion Tracking Technologies in Driver Monitoring Systems","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI/ACM Conference on AI Ethics and Society","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; Western University","funders":"Social Sciences and Humanities Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Tracking (education); Tracking system; Computer science; Psychology; Political science; Human–computer interaction; Artificial intelligence; Kalman filter","score_opus":0.08309830349651891,"score_gpt":0.33121492414239206,"score_spread":0.24811662064587314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404518565","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5009057,0.0016220979,0.32555485,0.01905286,0.00043771527,0.00047289187,0.0001890892,0.0014561887,0.15030861],"genre_scores_gemma":[0.9737839,0.000219832,0.020144196,0.0005136897,0.00010607848,0.00006603104,0.00006838199,0.000101987636,0.0049959486],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99076414,0.0056534624,0.00042964157,0.0008109367,0.0019162359,0.0004256215],"domain_scores_gemma":[0.9777394,0.01606234,0.002143614,0.0015455681,0.0022490092,0.00026008923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00979071,0.0004509004,0.00030794204,0.0013125502,0.002356597,0.0080978805,0.00079657853,0.0022874791,0.0028961336],"category_scores_gemma":[0.033692468,0.00045718037,0.00036870668,0.0005434756,0.0047715344,0.010910527,0.0036870383,0.0023929316,0.001168069],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000868369,0.00042641832,0.018656528,0.000590115,0.000074705575,0.0015278391,0.110440835,0.0064358055,0.06312962,0.50833017,0.008069291,0.28145027],"study_design_scores_gemma":[0.0001961933,0.00064746913,0.016275827,0.0009158677,0.0002547091,0.0012827246,0.05200224,0.2041487,0.093882434,0.35291824,0.27717155,0.00030408756],"about_ca_topic_score_codex":0.0015323026,"about_ca_topic_score_gemma":0.0012475942,"teacher_disagreement_score":0.00979071,"about_ca_system_score_codex":0.0024738137,"about_ca_system_score_gemma":0.0010874843,"threshold_uncertainty_score":0.051778793},"labels":[],"label_agreement":null},{"id":"W4404792810","doi":"10.18653/v1/2024.findings-emnlp.653","title":"Make Compound Sentences Simple to Analyze: Learning to Split Sentences for Aspect-based Sentiment Analysis","year":2024,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea","keywords":"Computer science; Simple (philosophy); Natural language processing; Artificial intelligence; Sentiment analysis; Compound","score_opus":0.023975259307059463,"score_gpt":0.3112394222057942,"score_spread":0.28726416289873474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404792810","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043105017,0.00067181716,0.94413424,0.00058165606,0.00022299144,0.00041327687,0.0010961713,0.0073033147,0.0024715904],"genre_scores_gemma":[0.2485424,0.0005633577,0.73857224,0.00055240025,0.00031376773,0.0005424743,0.006845838,0.00084525335,0.0032223098],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99932027,0.00023481167,0.00006494519,0.00020631863,0.00012193563,0.000051711115],"domain_scores_gemma":[0.998103,0.0009638463,0.0001940272,0.00020958386,0.00041580203,0.00011361466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016185228,0.001777492,0.0006092312,0.0015548585,0.0005058793,0.001066355,0.00085974723,0.000780598,0.0040008556],"category_scores_gemma":[0.004861319,0.0004410988,0.0014921458,0.0007966191,0.00053475786,0.0027390278,0.0012010754,0.0018342608,0.0037558114],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047846715,0.00031138177,0.0057578753,0.0004926696,0.0001894207,0.00043923955,0.0012943568,0.010712834,0.079641856,0.00793611,0.026181635,0.86656415],"study_design_scores_gemma":[0.00012314116,0.00045573083,0.004748602,0.0001133398,0.0002609448,0.0005142059,0.0008302586,0.87851006,0.036458865,0.05168954,0.026219552,0.000075805845],"about_ca_topic_score_codex":0.0010917728,"about_ca_topic_score_gemma":0.0027271293,"teacher_disagreement_score":0.0040008556,"about_ca_system_score_codex":0.0004656183,"about_ca_system_score_gemma":0.00088431855,"threshold_uncertainty_score":0.013384163},"labels":[],"label_agreement":null},{"id":"W4405452664","doi":"10.5267/j.ijdns.2024.8.002","title":"Sentiment analysis of social media discourse on public perception of online courier services in Saudi Arabia using machine learning","year":2024,"lang":"en","type":"article","venue":"International Journal of Data and Network Science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Social media; Computer science; Support vector machine; Sentiment analysis; Decision tree; Artificial intelligence; Machine learning; Crawling; The Internet; World Wide Web","score_opus":0.047521537584923175,"score_gpt":0.3643711926511336,"score_spread":0.31684965506621043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405452664","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9985655,0.000053323896,0.000104170795,0.00013416266,0.0000065204554,0.000013053944,0.00020523492,0.000003078506,0.00091486936],"genre_scores_gemma":[0.998679,0.00009818614,0.00023814302,0.000041844552,0.000012590966,0.0000127452895,0.00031064652,0.000002426492,0.00060433865],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995316,0.0001875661,0.000051318373,0.000040514104,0.00011200649,0.00007699951],"domain_scores_gemma":[0.996326,0.0015091153,0.0007488895,0.00006415339,0.0012088943,0.0001429218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010103115,0.00019489638,0.00018703847,0.0013872063,0.00065760256,0.0012306765,0.00012227634,0.00027083504,0.0011753018],"category_scores_gemma":[0.0028968772,0.00008463524,0.0001903358,0.0012160249,0.0004954696,0.00055111357,0.00050606835,0.00035008916,0.00028567287],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010286432,0.000360438,0.67982274,0.00059106667,0.00015292013,0.0014191316,0.17726992,0.0011148601,0.01911494,0.0016955717,0.005630869,0.11179888],"study_design_scores_gemma":[0.000015040852,0.0001950518,0.80234456,0.00013238266,0.00006321218,0.00021666689,0.17248042,0.007038116,0.0054229004,0.00025889077,0.01179398,0.000038853123],"about_ca_topic_score_codex":0.02608681,"about_ca_topic_score_gemma":0.024316356,"teacher_disagreement_score":0.02608681,"about_ca_system_score_codex":0.0012526326,"about_ca_system_score_gemma":0.00051712384,"threshold_uncertainty_score":0.05186993},"labels":[],"label_agreement":null},{"id":"W4405674556","doi":"10.18280/ria.380602","title":"A Suitable Technique for Enhancing Arabic-Language Consumer Sentiment Analysis Using Natural Language Processing and Stacking Machine Learning Model","year":2024,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Stacking; Natural language processing; Arabic; Artificial intelligence; Sentiment analysis; Natural language; Natural (archaeology); Linguistics; Chemistry; Geology","score_opus":0.030286896695408003,"score_gpt":0.3174996180808306,"score_spread":0.2872127213854226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405674556","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018916255,0.00018185562,0.9745568,0.0002522928,0.00016171456,0.00017686348,0.0003547335,0.00311617,0.00228333],"genre_scores_gemma":[0.22731288,0.00033187083,0.76396143,0.00021171663,0.00013746138,0.00047332112,0.0015771097,0.00023897811,0.0057553262],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946254,0.000099241195,0.0000576253,0.0001407931,0.0001823392,0.00005750727],"domain_scores_gemma":[0.999406,0.000148625,0.000058167985,0.000063184954,0.00030662416,0.00001743624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086699444,0.0011308043,0.00048179162,0.0014146031,0.0005275058,0.0007442644,0.0005469001,0.0005008665,0.003932416],"category_scores_gemma":[0.0023585733,0.00026936203,0.0014107111,0.0010116878,0.00028924,0.0011539706,0.0005476995,0.0010500737,0.0027517094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025547197,0.0002029318,0.0030009302,0.00030124513,0.00013499298,0.0004150755,0.00044666376,0.034757607,0.061601847,0.0092340475,0.010987288,0.8786618],"study_design_scores_gemma":[0.000020351297,0.00018322562,0.004571174,0.00004251205,0.000091225236,0.00032977437,0.00023188654,0.91411626,0.049495097,0.0104464805,0.020408135,0.00006397354],"about_ca_topic_score_codex":0.00303291,"about_ca_topic_score_gemma":0.003179006,"teacher_disagreement_score":0.003932416,"about_ca_system_score_codex":0.000419872,"about_ca_system_score_gemma":0.00075923384,"threshold_uncertainty_score":0.013155222},"labels":[],"label_agreement":null},{"id":"W4405722158","doi":"10.23977/jeis.2024.090322","title":"Sentiment Analysis Based on 100 Doctoral Acknowledgments","year":2024,"lang":"en","type":"article","venue":"Journal of Electronics and Information Science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sentiment analysis; Computer science; Data science; Natural language processing","score_opus":0.013824415636765978,"score_gpt":0.2898354311878551,"score_spread":0.27601101555108914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405722158","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9265537,0.002532902,0.017778572,0.00115787,0.00076502154,0.0007801787,0.024408037,0.00055857876,0.025465176],"genre_scores_gemma":[0.8941872,0.0016320504,0.035179872,0.00032810195,0.0005297249,0.0025076577,0.048617825,0.00027596534,0.016741682],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.99766254,0.00063660106,0.0003060738,0.00035027976,0.0009008471,0.00014357119],"domain_scores_gemma":[0.9937738,0.0030075705,0.0006607172,0.00021082714,0.0021636137,0.0001834502],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0017375966,0.0005404683,0.00039369866,0.0035237751,0.0010608849,0.00094193453,0.0003588466,0.00049874146,0.0029759153],"category_scores_gemma":[0.009594442,0.00016099344,0.00045251162,0.0037734914,0.0005083469,0.0007597842,0.0008964788,0.00047272665,0.001705945],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018839532,0.0005046699,0.054490242,0.0057398425,0.00022819359,0.00295864,0.04009304,0.0027669352,0.14256345,0.004242134,0.11751303,0.62701595],"study_design_scores_gemma":[0.000114081755,0.0006251376,0.3363452,0.0011639968,0.0002469028,0.0029523908,0.05768077,0.031632464,0.04913317,0.0031340157,0.51674104,0.00023085732],"about_ca_topic_score_codex":0.0011447704,"about_ca_topic_score_gemma":0.0022953125,"teacher_disagreement_score":0.9982624,"about_ca_system_score_codex":0.00074982573,"about_ca_system_score_gemma":0.0005714799,"threshold_uncertainty_score":0.009955406},"labels":[],"label_agreement":null},{"id":"W4405795043","doi":"10.12928/telkomnika.v23i1.26377","title":"Enhanced sentiment analysis and emotion detection in movie reviews using support vector machine algorithm","year":2024,"lang":"en","type":"article","venue":"TELKOMNIKA (Telecommunication Computing Electronics and Control)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sentiment analysis; Support vector machine; Emotion detection; Computer science; Artificial intelligence; Algorithm; Pattern recognition (psychology); Emotion recognition","score_opus":0.013003564303256198,"score_gpt":0.2798707231855362,"score_spread":0.26686715888227996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405795043","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44252282,0.0013557583,0.5488828,0.0003402318,0.0002645453,0.0003295778,0.0010316934,0.002630512,0.0026421011],"genre_scores_gemma":[0.77826834,0.00039551794,0.21723232,0.00006582212,0.00013392363,0.0002191208,0.0019367242,0.00004289447,0.001705318],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992642,0.00018134531,0.00010332933,0.0001409516,0.00023451206,0.0000756601],"domain_scores_gemma":[0.99909747,0.00028757937,0.000113939015,0.000041264757,0.00043440217,0.000025421461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009211813,0.00076245226,0.0008285077,0.0014452348,0.00025800566,0.0007307791,0.00040771766,0.00045931115,0.0008026758],"category_scores_gemma":[0.002378807,0.0001773835,0.000636928,0.00097646005,0.00010622325,0.0005815277,0.00030666322,0.00052720547,0.00063951075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007285534,0.0004277952,0.020176819,0.0003355791,0.00021174258,0.00032141816,0.00027950155,0.026765706,0.061348286,0.00097178295,0.0064970595,0.8819358],"study_design_scores_gemma":[0.00002177289,0.0002210734,0.009409348,0.000018093033,0.000043498774,0.000124651,0.000112276495,0.9727088,0.0150828725,0.0005796364,0.0016583992,0.000019564073],"about_ca_topic_score_codex":0.0015516941,"about_ca_topic_score_gemma":0.001277141,"teacher_disagreement_score":0.0015516941,"about_ca_system_score_codex":0.00023154244,"about_ca_system_score_gemma":0.00034014822,"threshold_uncertainty_score":0.004871726},"labels":[],"label_agreement":null},{"id":"W4405825087","doi":"10.1142/s1793962325500217","title":"An optimized Indian-General-Elections-Based social science data prediction using multiscale dense nested parallel MobileNetV3 mantis search attention network","year":2024,"lang":"en","type":"article","venue":"Advances in Complex Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Mantis; Computer science; Artificial intelligence; Machine learning; Biology; Ecology","score_opus":0.08170705247905108,"score_gpt":0.38198646279442583,"score_spread":0.30027941031537475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405825087","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2611286,0.00047483138,0.7277452,0.00094406464,0.00013719767,0.000101098296,0.000712792,0.0015402094,0.0072161187],"genre_scores_gemma":[0.95711166,0.0001594487,0.037270423,0.00012407791,0.000042447347,0.00010959511,0.00075642875,0.000056451056,0.0043695862],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997712,0.00004018369,0.000009349677,0.000086376385,0.00004165933,0.000051236915],"domain_scores_gemma":[0.9997019,0.00011055942,0.00003677599,0.000022391743,0.00009689598,0.000031417854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004626173,0.0005995081,0.00071785296,0.00067360577,0.00045175225,0.000768928,0.0012710679,0.0006891037,0.0016557155],"category_scores_gemma":[0.0012765311,0.00033519097,0.00081627973,0.000659094,0.0003303788,0.00072286977,0.00087806286,0.0008234038,0.00037243863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006171436,0.0000444383,0.004788097,0.000026994943,0.000030520274,0.000072149465,0.000040104336,0.95277476,0.0008440942,0.0025692855,0.0013734295,0.037374485],"study_design_scores_gemma":[8.7330363e-7,0.0000025390275,0.00012527885,6.3353104e-7,0.0000016365716,0.0000023921389,0.000002996154,0.99949527,0.000050164344,0.00026237228,0.00005496004,8.926631e-7],"about_ca_topic_score_codex":0.045438036,"about_ca_topic_score_gemma":0.03645667,"teacher_disagreement_score":0.045438036,"about_ca_system_score_codex":0.0011826764,"about_ca_system_score_gemma":0.0012017573,"threshold_uncertainty_score":0.09034717},"labels":[],"label_agreement":null},{"id":"W4406000332","doi":"10.2196/51154","title":"Investigating Reddit Data on Type 2 Diabetes Management During the COVID-19 Pandemic Using Latent Dirichlet Allocation Topic Modeling and Valence Aware Dictionary for Sentiment Reasoning Analysis: Content Analysis","year":2025,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Regional Municipality of Waterloo; University of Toronto; McMaster University; Public Health Ontario; University Health Network","funders":"","keywords":"Sentiment analysis; Pandemic; Latent Dirichlet allocation; Type 2 diabetes; Social media; Context (archaeology); Coronavirus disease 2019 (COVID-19); Anxiety; Topic model; Coping (psychology); Psychology; Medicine; Computer science; Diabetes mellitus; Disease; Clinical psychology; Artificial intelligence; Psychiatry; World Wide Web; Geography; Pathology; Infectious disease (medical specialty)","score_opus":0.2402302599110167,"score_gpt":0.43633762475109356,"score_spread":0.19610736484007685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406000332","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7413805,0.0015769853,0.008081059,0.0020075352,0.0007204798,0.00075010234,0.23344775,0.001314748,0.010720764],"genre_scores_gemma":[0.72174424,0.0007811648,0.027641824,0.0005890308,0.00047038362,0.0017626127,0.2401324,0.00025281258,0.006625563],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987116,0.00052135467,0.00012059633,0.0002342818,0.00028861876,0.00012364837],"domain_scores_gemma":[0.9916521,0.005913811,0.00080055185,0.00040394257,0.00091734284,0.00031233704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022706566,0.0005633284,0.00035717673,0.0033533354,0.0005344748,0.0008972107,0.00037945653,0.000806756,0.0023232289],"category_scores_gemma":[0.009731729,0.00013975437,0.0006101022,0.0022626035,0.00034404045,0.0010397161,0.0011433994,0.0008194994,0.0014483554],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0031918422,0.0011358955,0.42549726,0.0074324855,0.00041742114,0.0031061566,0.025400393,0.009373427,0.029300636,0.0043453807,0.24287617,0.24792293],"study_design_scores_gemma":[0.00017236598,0.0006038875,0.72545934,0.0010153386,0.00018382855,0.00091819477,0.021338696,0.06601197,0.008448859,0.0030342087,0.17261764,0.00019565236],"about_ca_topic_score_codex":0.004426707,"about_ca_topic_score_gemma":0.00813626,"teacher_disagreement_score":0.004426707,"about_ca_system_score_codex":0.00078081415,"about_ca_system_score_gemma":0.00041461442,"threshold_uncertainty_score":0.012008488},"labels":[],"label_agreement":null},{"id":"W4406194178","doi":"10.1016/j.eswa.2024.126287","title":"Public opinion prediction on social media by using machine learning methods","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Humanities and Social Science Fund of Ministry of Education of China; Fundamental Research Funds for the Central Universities; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Computer science; Public opinion; Social media; Artificial intelligence; Machine learning; Sentiment analysis; Support vector machine; Data science; World Wide Web; Political science","score_opus":0.07570998929517869,"score_gpt":0.35653529430967523,"score_spread":0.28082530501449654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406194178","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6609391,0.0014932271,0.31202394,0.0015914425,0.0006801953,0.0002619486,0.005018922,0.0026960138,0.015295214],"genre_scores_gemma":[0.9598538,0.00035754257,0.03308657,0.000093952156,0.0006181388,0.00008288071,0.0030271376,0.000049107068,0.002830938],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993505,0.0001801117,0.00004077092,0.00012783664,0.00020548931,0.000095366486],"domain_scores_gemma":[0.99684685,0.0016631208,0.00034020748,0.0001289548,0.00093145133,0.00008941779],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009946352,0.0007832648,0.00060382823,0.0033723162,0.0004221033,0.0013854034,0.0003986546,0.00065573293,0.0022855296],"category_scores_gemma":[0.0046611964,0.00019023675,0.00069962844,0.0015711774,0.00018451783,0.0017007653,0.0003898227,0.0008398479,0.0020459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010305155,0.0014427279,0.12702398,0.0003045928,0.00058339915,0.00053522136,0.00027934354,0.07446167,0.01679552,0.0040274044,0.03163201,0.74188364],"study_design_scores_gemma":[0.000012018126,0.00005376666,0.0075192563,0.000013396568,0.00004960951,0.00003009187,0.0000659204,0.9867443,0.0023043838,0.002234635,0.00096239493,0.000010273904],"about_ca_topic_score_codex":0.0043688817,"about_ca_topic_score_gemma":0.005576352,"teacher_disagreement_score":0.0043688817,"about_ca_system_score_codex":0.0005344768,"about_ca_system_score_gemma":0.00033549804,"threshold_uncertainty_score":0.0086869},"labels":[],"label_agreement":null},{"id":"W4406220446","doi":"10.18280/ts.410621","title":"Image Content Analysis for Social Media Public Opinion Monitoring and Response Strategies","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Department of Education, Fujian Province","keywords":"Public opinion; Content analysis; Content (measure theory); Social media; Image (mathematics); Computer science; Artificial intelligence; Political science; Sociology; Mathematics; Social science; World Wide Web; Law","score_opus":0.12222324152381396,"score_gpt":0.3276902268296781,"score_spread":0.2054669853058641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406220446","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46215087,0.0021618605,0.5005338,0.0010304953,0.0003510031,0.00083889806,0.0060262913,0.005023567,0.021883199],"genre_scores_gemma":[0.8835661,0.000589172,0.10617307,0.00012185432,0.0003325752,0.0003103083,0.0025278826,0.00015811708,0.0062210015],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994117,0.00012050008,0.00003573074,0.00010270653,0.00022744625,0.000101940896],"domain_scores_gemma":[0.99850947,0.0004866682,0.00018655659,0.0000852337,0.0006737037,0.00005841107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075381674,0.0006272383,0.0005455077,0.0045886487,0.00041477772,0.0012336452,0.0003645197,0.00054128625,0.0036523463],"category_scores_gemma":[0.0025205703,0.00015126851,0.00050941453,0.0020210007,0.0002274004,0.0010251006,0.00036813208,0.00043394993,0.0022111908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00088672346,0.00048898463,0.026597561,0.0004302109,0.00018684292,0.00022229574,0.00035979867,0.0063657677,0.1534744,0.0033473354,0.013709703,0.79393035],"study_design_scores_gemma":[0.000058711692,0.00066170446,0.10215141,0.0000890816,0.00039440463,0.0004804261,0.0011808394,0.7129235,0.15788516,0.006146288,0.017925888,0.00010263375],"about_ca_topic_score_codex":0.002380659,"about_ca_topic_score_gemma":0.0026318368,"teacher_disagreement_score":0.0045886487,"about_ca_system_score_codex":0.0005421604,"about_ca_system_score_gemma":0.00041101364,"threshold_uncertainty_score":0.0122182965},"labels":[],"label_agreement":null},{"id":"W4406499798","doi":"10.1109/cascon62161.2024.10838185","title":"Sentiment Analysis with LLMs: Evaluating QLoRA Fine-Tuning, Instruction Strategies, and Prompt Sensitivity","year":2024,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Sensitivity (control systems); Computer science; Sentiment analysis; Artificial intelligence; Engineering","score_opus":0.0271359459686862,"score_gpt":0.29918075553766765,"score_spread":0.27204480956898147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406499798","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70985055,0.005080078,0.18755893,0.001542185,0.0008243271,0.00077388174,0.00243196,0.085120335,0.0068177585],"genre_scores_gemma":[0.8959363,0.0003993406,0.095930725,0.0006840522,0.0000783864,0.00047795865,0.0029026256,0.0010136754,0.0025768336],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99802506,0.00084432104,0.00017433273,0.000527483,0.00026535633,0.00016355787],"domain_scores_gemma":[0.99354124,0.00417571,0.00026305197,0.00086413376,0.00090476015,0.00025102703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044219433,0.0020157374,0.0011765643,0.0008746706,0.00048760118,0.0015327572,0.0019063742,0.0014820448,0.0035854625],"category_scores_gemma":[0.022962987,0.00057059166,0.0008965416,0.0005399869,0.00052515167,0.0031438507,0.0015059607,0.002460624,0.0021078566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004596216,0.0016267356,0.025247462,0.0013166096,0.00058229145,0.00044846826,0.0012060613,0.3269033,0.03358744,0.0024597058,0.023148187,0.5788775],"study_design_scores_gemma":[0.00019166533,0.00042585735,0.0017639027,0.000039433886,0.00007564546,0.00005475312,0.0002325795,0.9831166,0.0098777125,0.0013391726,0.0028388607,0.000043798107],"about_ca_topic_score_codex":0.008273629,"about_ca_topic_score_gemma":0.010585735,"teacher_disagreement_score":0.008273629,"about_ca_system_score_codex":0.0013613487,"about_ca_system_score_gemma":0.0014113663,"threshold_uncertainty_score":0.023385763},"labels":[],"label_agreement":null},{"id":"W4406800538","doi":"10.18280/isi.300109","title":"Travel Vlog Reviews: Support Vector Machine Performance in Sentiment Classification","year":2025,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universitas Katolik Indonesia Atma Jaya; Universitas Indonesia","keywords":"Support vector machine; Computer science; Sentiment analysis; Artificial intelligence; Machine learning; Data mining; Information retrieval","score_opus":0.02369826723897463,"score_gpt":0.2651353012611111,"score_spread":0.24143703402213648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406800538","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8665702,0.0035668954,0.109419376,0.0016531682,0.00047251314,0.0001610309,0.0011206685,0.0026726916,0.014363468],"genre_scores_gemma":[0.96612227,0.00027824726,0.031309955,0.00006543802,0.00006433815,0.000026911213,0.00077271526,0.000045702578,0.0013142969],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981725,0.00080465694,0.0001160363,0.00021350382,0.00058518327,0.000108073385],"domain_scores_gemma":[0.9954947,0.0025715064,0.00033420915,0.0002934657,0.0011846296,0.00012152376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033261166,0.0007210576,0.0006506636,0.0012524382,0.00034028303,0.0012854702,0.0004387301,0.00068009464,0.0013972591],"category_scores_gemma":[0.012689514,0.00014624151,0.00027453448,0.0012507454,0.00018422774,0.0011888229,0.00043371416,0.0007946297,0.00094446517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015537153,0.00045483967,0.036366396,0.00031998518,0.00019833233,0.00012354883,0.00022350498,0.113377124,0.012135694,0.0020269728,0.016541671,0.81667817],"study_design_scores_gemma":[0.000016261223,0.00023407312,0.00434351,0.000016512971,0.0000130920935,0.000035906,0.000068163485,0.98908293,0.0041674,0.0006943395,0.0013165786,0.000011246015],"about_ca_topic_score_codex":0.0043088333,"about_ca_topic_score_gemma":0.0033686066,"teacher_disagreement_score":0.0043088333,"about_ca_system_score_codex":0.000503976,"about_ca_system_score_gemma":0.0004557273,"threshold_uncertainty_score":0.017590404},"labels":[],"label_agreement":null},{"id":"W4406864519","doi":"10.1016/j.neucom.2025.129472","title":"Large language model augmented syntax-aware domain adaptation method for aspect-based sentiment analysis","year":2025,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Syntax; Sentiment analysis; Adaptation (eye); Domain (mathematical analysis); Natural language processing; Domain adaptation; Artificial intelligence; Language model; Mathematics","score_opus":0.017749170902370798,"score_gpt":0.32042968186323456,"score_spread":0.3026805109608638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406864519","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034487158,0.00035225373,0.9597819,0.00021463561,0.000104225845,0.0000876471,0.0002173511,0.003273842,0.0014810457],"genre_scores_gemma":[0.65528625,0.000505692,0.33367053,0.00047332342,0.00016651461,0.0004053814,0.002588876,0.00057969324,0.006323857],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964345,0.00011024889,0.000021649004,0.00011533353,0.00007196316,0.000037286387],"domain_scores_gemma":[0.9996141,0.0001266997,0.00004467294,0.000070275375,0.00011903528,0.000025139627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007043351,0.0009366178,0.0005657379,0.0007151752,0.00026460693,0.0005823889,0.00081279245,0.00051410985,0.0013751521],"category_scores_gemma":[0.0016710596,0.00026292854,0.0011201911,0.00062719284,0.0003662432,0.0011892273,0.0011065761,0.0014255173,0.0012879628],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030355994,0.00029355133,0.0034770407,0.00016212373,0.00025260402,0.00045608217,0.00039248855,0.18067251,0.09411335,0.006821314,0.014691712,0.6983637],"study_design_scores_gemma":[0.000010548135,0.00003828282,0.00063210167,0.0000060226835,0.000025138039,0.000060317165,0.000044672033,0.9870206,0.0061509283,0.003959089,0.0020379464,0.000014361054],"about_ca_topic_score_codex":0.0015545246,"about_ca_topic_score_gemma":0.002416914,"teacher_disagreement_score":0.0015545246,"about_ca_system_score_codex":0.00040487616,"about_ca_system_score_gemma":0.0005770714,"threshold_uncertainty_score":0.0046002865},"labels":[],"label_agreement":null},{"id":"W4407134373","doi":"10.1016/j.neucom.2025.129589","title":"A novel large language model enhanced joint learning framework for fine-grained sentiment analysis on drug reviews","year":2025,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Joint (building); Artificial intelligence; Sentiment analysis; Natural language processing; Machine learning","score_opus":0.025895812070929055,"score_gpt":0.3168668132941357,"score_spread":0.2909710012232066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407134373","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023288207,0.0008355015,0.9685857,0.00051179837,0.00018301497,0.00012372434,0.00067957275,0.0039705094,0.0018219259],"genre_scores_gemma":[0.45714533,0.0006698409,0.5228908,0.0007702554,0.00045334513,0.00038639377,0.0038826766,0.0004399307,0.013361311],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937797,0.0001494985,0.000045769073,0.00014722439,0.00017787449,0.00010165039],"domain_scores_gemma":[0.99933165,0.00019889437,0.00007045849,0.00006490729,0.00027925987,0.00005487459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001181138,0.0010840823,0.0011993176,0.001435355,0.00056129816,0.0010441487,0.00148015,0.00093322096,0.0027021773],"category_scores_gemma":[0.0018048477,0.00040912165,0.0012833024,0.0013536259,0.00031045298,0.0014056232,0.0014927349,0.0014221754,0.0019864503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052457134,0.0008521132,0.004272985,0.0002365186,0.0004998678,0.00035601144,0.00017491292,0.14045693,0.0393519,0.008411139,0.028004328,0.7768587],"study_design_scores_gemma":[0.000014210547,0.000040824576,0.0003775247,0.0000041606727,0.00003512324,0.000029458053,0.000014791942,0.9941366,0.0017284161,0.002146501,0.0014609396,0.000011533399],"about_ca_topic_score_codex":0.008638422,"about_ca_topic_score_gemma":0.019456316,"teacher_disagreement_score":0.008638422,"about_ca_system_score_codex":0.00053545326,"about_ca_system_score_gemma":0.0015763629,"threshold_uncertainty_score":0.01717627},"labels":[],"label_agreement":null},{"id":"W4407140610","doi":"10.1177/10946705241307678","title":"Using Traditional Text Analysis and Large Language Models in Service Failure and Recovery","year":2025,"lang":"en","type":"article","venue":"Journal of Service Research","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Service recovery; Service (business); Computer science; Natural language processing; Psychology; Linguistics; Business; Service quality; Marketing","score_opus":0.10950270752738463,"score_gpt":0.3834387842433052,"score_spread":0.2739360767159206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407140610","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015358245,0.0008469641,0.96724,0.003297943,0.0002476573,0.00051981816,0.0037380354,0.0042138314,0.0045376397],"genre_scores_gemma":[0.20011197,0.0013898446,0.77950925,0.0010658326,0.00049666816,0.002162744,0.008746597,0.0017071383,0.0048098667],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99113864,0.005643964,0.0006537208,0.001129616,0.0012057169,0.0002283303],"domain_scores_gemma":[0.9508981,0.041023493,0.0021857994,0.0022046852,0.0032196192,0.0004682392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010789482,0.0018258231,0.00095183484,0.0058783568,0.0015786205,0.0063592303,0.0018766936,0.0012494101,0.008314214],"category_scores_gemma":[0.045143947,0.0007370762,0.0022022182,0.0039863307,0.0017582236,0.010439104,0.0026982313,0.0033705381,0.0059848134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006333618,0.0006139298,0.013117557,0.002315616,0.0005146539,0.0007671525,0.011044347,0.043377668,0.011322593,0.117432,0.05110684,0.7477543],"study_design_scores_gemma":[0.00009621342,0.00015491754,0.0045607854,0.0007651602,0.00015394698,0.00026547056,0.0058214515,0.58960223,0.0077789086,0.3282424,0.0623528,0.00020568892],"about_ca_topic_score_codex":0.006195661,"about_ca_topic_score_gemma":0.005636756,"teacher_disagreement_score":0.010789482,"about_ca_system_score_codex":0.002490939,"about_ca_system_score_gemma":0.0023896405,"threshold_uncertainty_score":0.057060897},"labels":[],"label_agreement":null},{"id":"W4407363206","doi":"10.1063/5.0253591","title":"Applying text mining for case analysis and vaccination status level in Ontario","year":2025,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Data science","score_opus":0.05155853182401921,"score_gpt":0.2940275355920508,"score_spread":0.2424690037680316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407363206","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9810929,0.00018780198,0.00083446136,0.0010051103,0.0000182463,0.00010163633,0.0065758205,0.000024283605,0.010159796],"genre_scores_gemma":[0.9896925,0.00025313877,0.0010684732,0.00008066617,0.000010167634,0.000056658017,0.002284647,0.000011412464,0.0065424247],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992094,0.000088356515,0.0000676289,0.00010212652,0.00032694175,0.00020543954],"domain_scores_gemma":[0.995693,0.000992524,0.00061854644,0.00008984498,0.0021187435,0.00048742443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008156399,0.00010646196,0.00017662722,0.0019368919,0.00187023,0.0010621545,0.0006200543,0.000270889,0.0028587517],"category_scores_gemma":[0.005596937,0.00020269628,0.0002982785,0.00413745,0.0004425799,0.00037245374,0.00058690016,0.00029320113,0.00024062842],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022352004,0.00006473515,0.9590111,0.000113129565,0.00006450737,0.0007805088,0.0054577203,0.0008181616,0.0016316203,0.0006612273,0.006688396,0.0244856],"study_design_scores_gemma":[0.000007538171,0.000020229329,0.98182917,0.000023107377,0.000033417917,0.00007278729,0.008804207,0.0019252651,0.0005578004,0.00017652843,0.006539889,0.000009964876],"about_ca_topic_score_codex":0.97534704,"about_ca_topic_score_gemma":0.9857232,"teacher_disagreement_score":0.024652958,"about_ca_system_score_codex":0.021568296,"about_ca_system_score_gemma":0.024662549,"threshold_uncertainty_score":0.15648967},"labels":[],"label_agreement":null},{"id":"W4407406870","doi":"10.1016/j.eswa.2025.126785","title":"Comprehensive analysis of Transformer networks in identifying informative sentences containing customer needs","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Computer science; Transformer; Natural language processing; Artificial intelligence","score_opus":0.02054406995083354,"score_gpt":0.29838290153105684,"score_spread":0.2778388315802233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407406870","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5808621,0.0026493848,0.4002051,0.0007355031,0.00011856151,0.00024108801,0.0032142862,0.0015284058,0.01044552],"genre_scores_gemma":[0.9402591,0.0006990587,0.052658033,0.000060887418,0.000074427575,0.00007841793,0.0035717979,0.00007582781,0.002522449],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994273,0.00021894753,0.00003604556,0.00010442011,0.00015669277,0.00005655014],"domain_scores_gemma":[0.9970937,0.002003371,0.0001506484,0.00011374098,0.0005785003,0.000060096398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014779373,0.0006169657,0.00033088063,0.0021765148,0.00049589574,0.00075655465,0.00037067485,0.00054778834,0.001624916],"category_scores_gemma":[0.004646757,0.00018933522,0.00040347865,0.0012083442,0.00019973484,0.0017075205,0.00044814724,0.0005358311,0.00059048325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001397384,0.00056439824,0.034246262,0.0007447315,0.00039757363,0.001468325,0.0010068694,0.0935083,0.08340752,0.01694161,0.017689211,0.7486278],"study_design_scores_gemma":[0.000022760867,0.00015666166,0.012950931,0.00003589684,0.000240845,0.0003781183,0.0003477995,0.95945364,0.013810947,0.008383139,0.0041971775,0.000022135087],"about_ca_topic_score_codex":0.0027022671,"about_ca_topic_score_gemma":0.0067869327,"teacher_disagreement_score":0.0027022671,"about_ca_system_score_codex":0.00043421227,"about_ca_system_score_gemma":0.000665741,"threshold_uncertainty_score":0.0078161955},"labels":[],"label_agreement":null},{"id":"W4408015791","doi":"10.1007/978-981-96-1483-7_30","title":"Discovering Causal Relationships in Noisy Web Data for Sentiment Classification Using Attention Mechanisms","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université TÉLUQ","funders":"","keywords":"Computer science; Sentiment analysis; Noisy data; Artificial intelligence; Natural language processing; Information retrieval; Data mining; Data science","score_opus":0.09704578524984683,"score_gpt":0.3170682906530183,"score_spread":0.22002250540317145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408015791","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12685357,0.0016434562,0.86509514,0.0014487649,0.00014598457,0.00018077303,0.0010044832,0.0012248501,0.002402991],"genre_scores_gemma":[0.85778993,0.0010507865,0.13692772,0.00022294572,0.0004091049,0.00020560515,0.0016770902,0.00010084876,0.0016159316],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99844617,0.00060822885,0.00013027184,0.00042190248,0.00026581535,0.00012765544],"domain_scores_gemma":[0.9844589,0.013018765,0.0009455298,0.0007816278,0.0006185535,0.00017659337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041435333,0.0008021023,0.0010549317,0.0031803227,0.0007596449,0.0021136065,0.0012718637,0.0011814467,0.0032363217],"category_scores_gemma":[0.020521846,0.0006162261,0.0013619219,0.002662305,0.00071709533,0.0045289174,0.0013654954,0.002457501,0.00066321896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00094239006,0.0012636866,0.060874753,0.0007870885,0.0008696346,0.000989348,0.0007804273,0.11463988,0.020297036,0.082767524,0.012518851,0.7032695],"study_design_scores_gemma":[0.000024505984,0.000047416423,0.0041042874,0.000040667102,0.00013130881,0.00006967264,0.000072697934,0.92340803,0.00198319,0.06918897,0.0009117968,0.000017445822],"about_ca_topic_score_codex":0.0029754117,"about_ca_topic_score_gemma":0.0031117569,"teacher_disagreement_score":0.0041435333,"about_ca_system_score_codex":0.0011366453,"about_ca_system_score_gemma":0.0009802314,"threshold_uncertainty_score":0.02191335},"labels":[],"label_agreement":null},{"id":"W4408463245","doi":"10.1007/s41701-025-00185-6","title":"Attitude in Reported and Non-reported News: A Critique of Sentiment Analysis in Corpus Pragmatics","year":2025,"lang":"en","type":"article","venue":"Corpus Pragmatics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Simon Fraser University","funders":"Australian Research Data Commons; Simon Fraser University; University of Sydney","keywords":"Pragmatics; Sentiment analysis; Linguistics; Corpus linguistics; Psychology; Computer science; Natural language processing; Philosophy","score_opus":0.01697193502643659,"score_gpt":0.30662836815915295,"score_spread":0.2896564331327164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408463245","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12736501,0.03681369,0.46170214,0.19468081,0.006474646,0.0017138809,0.0042542526,0.00082459155,0.16617101],"genre_scores_gemma":[0.8906721,0.0060205995,0.08166127,0.010167201,0.0029227398,0.0030652552,0.0010329108,0.0006542145,0.0038037861],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.863775,0.100130245,0.0052709556,0.0076098605,0.022462048,0.0007518464],"domain_scores_gemma":[0.5063269,0.42110816,0.012422917,0.017401295,0.041386068,0.0013546125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1601577,0.0010373628,0.0020741068,0.0137945535,0.006742345,0.0119091235,0.004423451,0.0022096026,0.0033258402],"category_scores_gemma":[0.34974143,0.0011983304,0.0012503577,0.01359198,0.03068677,0.014865241,0.005770417,0.0044778353,0.00076291885],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005898843,0.00011917331,0.014414001,0.005951683,0.00081621186,0.00042154195,0.09167764,0.0016074664,0.0022484425,0.6527308,0.039995093,0.189428],"study_design_scores_gemma":[0.00031827224,0.00018123213,0.04366848,0.0075390413,0.00058385404,0.00074701174,0.058285292,0.026314888,0.0045332387,0.5998558,0.25758323,0.00038977613],"about_ca_topic_score_codex":0.03605639,"about_ca_topic_score_gemma":0.028361676,"teacher_disagreement_score":0.1601577,"about_ca_system_score_codex":0.011453082,"about_ca_system_score_gemma":0.0062767514,"threshold_uncertainty_score":0.847005},"labels":[],"label_agreement":null},{"id":"W4408733649","doi":"10.4018/joeuc.371759","title":"The Optimization of Advertising Content and Prediction of Consumer Response Rate Based on Generative Adversarial Networks","year":2025,"lang":"en","type":"article","venue":"Journal of Organizational and End User Computing","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Adversarial system; Generative grammar; Computer science; Content (measure theory); Advertising; Artificial intelligence; Mathematics; Business","score_opus":0.0144510945230814,"score_gpt":0.23067936382787493,"score_spread":0.21622826930479352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408733649","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12113375,0.0005478913,0.87045395,0.0009431275,0.00010116873,0.000088296234,0.00015235429,0.00043935384,0.0061401217],"genre_scores_gemma":[0.9748955,0.0001897572,0.020108014,0.00023527499,0.00006136637,0.00007595766,0.00012609779,0.000051137475,0.0042568664],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994072,0.00024907725,0.00001947613,0.00014205628,0.00009951243,0.00008285714],"domain_scores_gemma":[0.9967699,0.0025489454,0.00022948332,0.000092412345,0.00027268665,0.00008659227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013824162,0.00093692896,0.0007306168,0.00039683728,0.00023532937,0.00064533734,0.00092040474,0.0009301268,0.0018353865],"category_scores_gemma":[0.005337134,0.0004157981,0.00044515196,0.00029216034,0.00079722505,0.0008974215,0.0007473261,0.0013886952,0.0004154197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012878046,0.00005277373,0.0009750045,0.00003570754,0.000026989475,0.000053379776,0.00005099366,0.9719916,0.0019226654,0.0060510323,0.0010815365,0.017629547],"study_design_scores_gemma":[0.000001146178,0.0000050749927,0.000053651318,9.987136e-7,0.0000022502688,0.000003469297,0.0000017079858,0.9989911,0.00013905228,0.00076308136,0.000036961465,0.0000016432198],"about_ca_topic_score_codex":0.003802514,"about_ca_topic_score_gemma":0.0034214274,"teacher_disagreement_score":0.003802514,"about_ca_system_score_codex":0.0010655046,"about_ca_system_score_gemma":0.00037994338,"threshold_uncertainty_score":0.0077308416},"labels":[],"label_agreement":null},{"id":"W4408795518","doi":"10.1109/swc62898.2024.00347","title":"Harnessing Convolutional Neural Networks for Sentiment Analysis of Tweets on the Metaverse","year":2024,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University","funders":"","keywords":"Computer science; Convolutional neural network; Metaverse; Sentiment analysis; Artificial intelligence; Data science; Virtual reality","score_opus":0.03864762836887984,"score_gpt":0.29247245425934476,"score_spread":0.2538248258904649,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408795518","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8022036,0.0008797484,0.17619024,0.0013484857,0.00034835047,0.00012532446,0.001465526,0.0016159776,0.01582263],"genre_scores_gemma":[0.96541405,0.0003471172,0.029792251,0.00012873713,0.00008397697,0.00003095858,0.001045928,0.00004356801,0.003113434],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983764,0.000035057248,0.000010508082,0.000030647047,0.000045379093,0.00004070779],"domain_scores_gemma":[0.9995733,0.00012952286,0.000091046975,0.000034748355,0.00015378596,0.000017569499],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004955702,0.00062556483,0.00017911795,0.0008870934,0.00026404948,0.00067241245,0.00026522978,0.00031592566,0.0010423234],"category_scores_gemma":[0.0015411115,0.00015110907,0.00031509754,0.000557206,0.00022775953,0.0010285763,0.0003996089,0.00048986555,0.0006431383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007684856,0.00036691007,0.071686596,0.00032123763,0.00035569843,0.0005970767,0.0008085716,0.10950658,0.14251742,0.0075501334,0.0120969955,0.6534243],"study_design_scores_gemma":[0.000007953999,0.00008362819,0.012662428,0.00003179849,0.000050215236,0.000059297814,0.00022915752,0.9538744,0.025180852,0.0038618718,0.003941142,0.000017277396],"about_ca_topic_score_codex":0.0059699393,"about_ca_topic_score_gemma":0.010540811,"teacher_disagreement_score":0.0059699393,"about_ca_system_score_codex":0.0005813046,"about_ca_system_score_gemma":0.00036128648,"threshold_uncertainty_score":0.011870384},"labels":[],"label_agreement":null},{"id":"W4408860552","doi":"10.1109/aiim64537.2024.10934642","title":"Cross-domain Sentiment Classification with Prompt Pre-training and Tuning","year":2024,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Training (meteorology); Domain (mathematical analysis); Artificial intelligence; Sentiment analysis; Machine learning; Mathematics","score_opus":0.03392989105792804,"score_gpt":0.3042989666662373,"score_spread":0.27036907560830925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408860552","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0999636,0.000600426,0.875663,0.0004115486,0.0002496054,0.0005182634,0.00053691055,0.01958776,0.002468877],"genre_scores_gemma":[0.5614979,0.00036373903,0.42691788,0.00083057594,0.00013626134,0.00102646,0.0046364567,0.0006549464,0.0039357613],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880147,0.00034271722,0.00009397897,0.0005013663,0.00013334742,0.00012719816],"domain_scores_gemma":[0.9972268,0.0011132896,0.0001969155,0.00068006694,0.0005953374,0.0001876395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003708361,0.002294498,0.0012344662,0.0008241609,0.00052631076,0.0011948239,0.0020563214,0.0015916994,0.0026797939],"category_scores_gemma":[0.008385535,0.0006642665,0.0013876673,0.00081525726,0.0005917391,0.0031437427,0.0022624948,0.0034580461,0.0031394614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012596999,0.0017617053,0.010314049,0.00032143874,0.00018147992,0.00028642788,0.00047485004,0.11457527,0.05543574,0.0025912744,0.01817309,0.79462504],"study_design_scores_gemma":[0.00009989116,0.00028308394,0.0019383031,0.000022604301,0.000043869717,0.000090116344,0.0001346795,0.973272,0.016705243,0.0041024094,0.0032727276,0.000035110566],"about_ca_topic_score_codex":0.0019519513,"about_ca_topic_score_gemma":0.0030595062,"teacher_disagreement_score":0.003708361,"about_ca_system_score_codex":0.0007742794,"about_ca_system_score_gemma":0.0015199829,"threshold_uncertainty_score":0.019611955},"labels":[],"label_agreement":null},{"id":"W4408929846","doi":"10.1016/b978-0-323-95504-1.00338-0","title":"Norms for Word Affective Properties","year":2025,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Word (group theory); Psychology; Linguistics; Computer science; Natural language processing; Cognitive psychology; Philosophy","score_opus":0.02379622259747212,"score_gpt":0.2468400851080367,"score_spread":0.22304386251056457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408929846","genre_codex":"methods","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0067398595,0.002081709,0.9224205,0.0012515609,0.0012057133,0.00011268173,0.0020323521,0.0013723924,0.06278323],"genre_scores_gemma":[0.25037208,0.004238715,0.65790117,0.0007083971,0.0029297199,0.0012944739,0.009603969,0.0018663511,0.07108509],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975903,0.0006698938,0.00031049497,0.00051447103,0.0008230413,0.00009180507],"domain_scores_gemma":[0.99586177,0.0019638462,0.000254013,0.00069228554,0.0011142665,0.000113719245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019931423,0.00096011,0.0006794753,0.0031331074,0.0008343458,0.004209685,0.0011533838,0.0008327945,0.021179464],"category_scores_gemma":[0.012090704,0.0004397291,0.0008845912,0.0028782485,0.001256828,0.0072949184,0.0015058288,0.0020532913,0.0101968665],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004552657,0.000053591913,0.0005803577,0.00021821097,0.000025054935,0.00006969861,0.00028910616,0.0016757571,0.0020271773,0.5844959,0.027373942,0.38314578],"study_design_scores_gemma":[0.000007637971,0.000026313795,0.0007812877,0.00016119216,0.000017534938,0.0002151389,0.00019574801,0.032000694,0.0020351526,0.90056944,0.0639544,0.00003542489],"about_ca_topic_score_codex":0.0011095722,"about_ca_topic_score_gemma":0.0012782594,"teacher_disagreement_score":0.021179464,"about_ca_system_score_codex":0.00080590165,"about_ca_system_score_gemma":0.0005794714,"threshold_uncertainty_score":0.0708524},"labels":[],"label_agreement":null},{"id":"W4409446970","doi":"10.2139/ssrn.5139918","title":"Using Traditional Text Analysis and Large Language Models in Service Failure and Recovery","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Service (business); Natural language processing; Linguistics; Business; Philosophy; Marketing","score_opus":0.024486109327262005,"score_gpt":0.272190565002856,"score_spread":0.247704455675594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409446970","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20342943,0.0013112008,0.7801845,0.003072418,0.00049860135,0.00029716085,0.0031270415,0.002936963,0.0051426217],"genre_scores_gemma":[0.8743248,0.00063688867,0.11808492,0.00031140642,0.0005390697,0.00021708844,0.0028659047,0.000269238,0.0027506535],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981749,0.0008867081,0.00014974663,0.00027066522,0.0003932487,0.00012473235],"domain_scores_gemma":[0.99141276,0.0064939726,0.0005551436,0.00038607817,0.0010018944,0.00015025793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003113984,0.000987386,0.000877702,0.002471003,0.0008078861,0.0021624444,0.00078751997,0.0010975542,0.0024646043],"category_scores_gemma":[0.01132652,0.00031866055,0.0013291738,0.0018445441,0.00047046127,0.0037708392,0.0008509885,0.0017655191,0.001850809],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017826216,0.0011872533,0.03582245,0.00071329455,0.00074735796,0.0008611773,0.00092466996,0.14559501,0.029469034,0.014783993,0.027075432,0.7410377],"study_design_scores_gemma":[0.000021631507,0.00007134495,0.0030273595,0.000023469003,0.00009066527,0.00005549529,0.00023523936,0.9756388,0.0026698767,0.016384201,0.0017596079,0.000022262215],"about_ca_topic_score_codex":0.0041810884,"about_ca_topic_score_gemma":0.004411131,"teacher_disagreement_score":0.0041810884,"about_ca_system_score_codex":0.0009943064,"about_ca_system_score_gemma":0.00090697134,"threshold_uncertainty_score":0.016468525},"labels":[],"label_agreement":null},{"id":"W4409509821","doi":"10.7788/9783412530921.383","title":"Abstracts","year":2025,"lang":"en","type":"book-chapter","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science","score_opus":0.02244145869102004,"score_gpt":0.25032127686872263,"score_spread":0.2278798181777026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409509821","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004321734,0.020690115,0.010433535,0.01844674,0.017612718,0.0003622469,0.036239307,0.0022606782,0.88963294],"genre_scores_gemma":[0.03205171,0.012965688,0.0060185227,0.0036843792,0.005552809,0.0003135459,0.029517937,0.0010751772,0.9088202],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998326,0.0003053709,0.00017826179,0.00040609532,0.0006602329,0.00012403185],"domain_scores_gemma":[0.9964818,0.00068307697,0.00032209302,0.00060323486,0.0013649534,0.00054499187],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001569354,0.00083204376,0.0007500827,0.0042507104,0.0018084524,0.00855482,0.0013275899,0.0018229384,0.46543586],"category_scores_gemma":[0.008784654,0.0003243706,0.0007596702,0.006326844,0.0006801001,0.004429831,0.0032803142,0.0012395043,0.2330553],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009462518,0.000030855954,0.001767178,0.000615939,0.000022633956,0.0001713297,0.0005085984,0.00021917748,0.0003579594,0.030038817,0.73351735,0.23265551],"study_design_scores_gemma":[0.0000059487916,0.000009577118,0.0017377816,0.00018695851,0.000006686512,0.00014346464,0.00027087153,0.00010210675,0.000088370994,0.005728265,0.99171233,0.000007728144],"about_ca_topic_score_codex":0.003942951,"about_ca_topic_score_gemma":0.0055991327,"teacher_disagreement_score":0.53456414,"about_ca_system_score_codex":0.0022103628,"about_ca_system_score_gemma":0.0024177132,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4409555240","doi":"10.1177/30504554251326853","title":"Improving Sentiment Classification Using 0-Shot Generated Labels for Custom Transformer Embeddings","year":2025,"lang":"en","type":"article","venue":"The European Journal on Artificial Intelligence","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Transformer; Computer science; Shot (pellet); Artificial intelligence; Sentiment analysis; Single shot; Pattern recognition (psychology); Natural language processing; Engineering; Materials science; Electrical engineering; Physics; Optics; Voltage","score_opus":0.12290909064669052,"score_gpt":0.3467678807937661,"score_spread":0.22385879014707555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409555240","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21365808,0.0007384615,0.76228017,0.0007772172,0.00045216322,0.00024641526,0.0011494426,0.012386026,0.008312039],"genre_scores_gemma":[0.8241296,0.00020556987,0.16276364,0.0004354199,0.00012953795,0.00017818315,0.0035388991,0.00063469727,0.007984359],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995524,0.000121573874,0.000022773342,0.00014677635,0.00009651308,0.000059918562],"domain_scores_gemma":[0.99871194,0.00057092117,0.000089212685,0.0002741042,0.00029328364,0.00006061099],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010076755,0.001462559,0.00064799766,0.0006515398,0.000369445,0.0009740127,0.0010354705,0.00088261644,0.0032820266],"category_scores_gemma":[0.0048150276,0.0003415377,0.0006608933,0.00039491637,0.00054803456,0.002980636,0.001253772,0.0017380815,0.002738327],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011736455,0.00071211334,0.014266561,0.00036038863,0.00012945231,0.00026309903,0.0005434943,0.09885295,0.055731334,0.010582164,0.02034849,0.79703623],"study_design_scores_gemma":[0.000037462516,0.00020974412,0.0010902947,0.000027931499,0.000037384783,0.000078332916,0.00011193503,0.9688465,0.01586722,0.009605113,0.0040649143,0.000023078312],"about_ca_topic_score_codex":0.0018868076,"about_ca_topic_score_gemma":0.004680323,"teacher_disagreement_score":0.0032820266,"about_ca_system_score_codex":0.00080298714,"about_ca_system_score_gemma":0.00060493965,"threshold_uncertainty_score":0.010979533},"labels":[],"label_agreement":null},{"id":"W4409625260","doi":"10.1007/978-3-031-85908-3_14","title":"Comparing Models for Sentiment Analysis of Tweets in Response to Public Health Announcements During the Pandemic","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Western University","funders":"","keywords":"Sentiment analysis; Pandemic; Coronavirus disease 2019 (COVID-19); Computer science; Natural language processing; Information retrieval; Medicine; Internal medicine","score_opus":0.12101108950349247,"score_gpt":0.35711193597439644,"score_spread":0.23610084647090396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409625260","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8783189,0.0044476725,0.09981863,0.0032570804,0.0009367617,0.00017693624,0.0034362637,0.0011996684,0.008408019],"genre_scores_gemma":[0.9624993,0.00097387587,0.027458072,0.00029044368,0.00036676467,0.00015427555,0.004552327,0.00020591103,0.0034990008],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877447,0.0008333194,0.00006892282,0.00013185429,0.00009618199,0.00009519171],"domain_scores_gemma":[0.9807582,0.018019084,0.00022242972,0.0002448576,0.0006149763,0.00014055384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053758253,0.0012907664,0.00077732233,0.0011141415,0.00044342066,0.0017030531,0.0010413775,0.0013164281,0.0022093325],"category_scores_gemma":[0.012920728,0.00040483466,0.0016934815,0.00078028865,0.0002970463,0.001815917,0.00064386264,0.0015268727,0.0010695272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0045212577,0.0007222338,0.03067114,0.00047602516,0.0016560194,0.00012700155,0.00039391377,0.7611608,0.0028006865,0.0050372565,0.019988086,0.17244557],"study_design_scores_gemma":[0.000032420467,0.00015809677,0.0022088173,0.0000143492525,0.00008480366,0.000008678314,0.00007021137,0.9945304,0.0004090044,0.0020566927,0.00041493413,0.000011744625],"about_ca_topic_score_codex":0.011586124,"about_ca_topic_score_gemma":0.009405643,"teacher_disagreement_score":0.011586124,"about_ca_system_score_codex":0.0015574701,"about_ca_system_score_gemma":0.00065942545,"threshold_uncertainty_score":0.028430462},"labels":[],"label_agreement":null},{"id":"W4409772900","doi":"10.4081/gh.2025.1344","title":"Sentiment analysis using a lexicon-based approach in Lisbon, Portugal","year":2025,"lang":"en","type":"article","venue":"Geospatial health","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Fundação para a Ciência e a Tecnologia; Universidade de Lisboa","keywords":"Lexicon; Sentiment analysis; Portuguese; Context (archaeology); Social media; Computer science; Identification (biology); Facet (psychology); Word (group theory); Natural language processing; Artificial intelligence; Data science; Linguistics; World Wide Web; Psychology; History; Social psychology","score_opus":0.02594728592393899,"score_gpt":0.3242749240264778,"score_spread":0.2983276381025388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409772900","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94467723,0.00085419166,0.007987729,0.0010167205,0.00011289991,0.00022092083,0.0075243395,0.00026250392,0.037343476],"genre_scores_gemma":[0.97243845,0.0005835414,0.00966117,0.00009959471,0.000033320055,0.00012802321,0.006066861,0.000104820654,0.010884153],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999548,0.00019247486,0.0000453988,0.00006194318,0.00008046188,0.00007171117],"domain_scores_gemma":[0.9992656,0.00026964836,0.00010265666,0.000030313202,0.00025326337,0.00007847394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009065817,0.00026884477,0.00034764994,0.0017609735,0.0010402546,0.0016137471,0.00028613352,0.00034792282,0.0029853657],"category_scores_gemma":[0.0018863829,0.000180073,0.00037927786,0.00232925,0.00039838642,0.00047079724,0.0006263231,0.00025498917,0.000848101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026231492,0.0011937974,0.30377454,0.0026295914,0.0001904687,0.014551735,0.088165276,0.012002642,0.04101599,0.0153226275,0.06415842,0.45437178],"study_design_scores_gemma":[0.00010669131,0.0003164637,0.63989985,0.00086313096,0.0001344414,0.0016918948,0.13298923,0.048594743,0.009148238,0.0047301403,0.16142258,0.00010261234],"about_ca_topic_score_codex":0.0679721,"about_ca_topic_score_gemma":0.09787717,"teacher_disagreement_score":0.0679721,"about_ca_system_score_codex":0.0024766403,"about_ca_system_score_gemma":0.0021712573,"threshold_uncertainty_score":0.13515288},"labels":[],"label_agreement":null},{"id":"W4409910542","doi":"10.62517/jbdc.202401417","title":"Fine-Tuning distilBERT for Enhanced Sentiment Classification","year":2024,"lang":"en","type":"article","venue":"Journal of big data and computing.","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Yorkville University","funders":"","keywords":"Sentiment analysis; Computer science; Natural language processing; Artificial intelligence","score_opus":0.1020122111990078,"score_gpt":0.3338315728694674,"score_spread":0.2318193616704596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409910542","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5138174,0.0053130845,0.4227811,0.0027454311,0.0011422149,0.00045831822,0.002019774,0.03224808,0.019474596],"genre_scores_gemma":[0.86034065,0.00041222054,0.12597865,0.0011409667,0.00014299263,0.00021215431,0.0030615625,0.00083125004,0.0078795245],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938464,0.00016017997,0.000031922023,0.00019678529,0.00011741032,0.00010897531],"domain_scores_gemma":[0.9985184,0.00067460525,0.000094840674,0.00019586744,0.00043377708,0.00008249509],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016524311,0.0017709461,0.0008987363,0.001078845,0.00067898433,0.0015963895,0.0018017251,0.0014915265,0.002760665],"category_scores_gemma":[0.006400451,0.0005887906,0.00069209404,0.00057586806,0.0004473295,0.0020714416,0.0011498273,0.0025417407,0.0031007077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013940545,0.0009830536,0.016264567,0.00028621207,0.00030381873,0.00031940686,0.00031523168,0.37648243,0.030637385,0.0026195392,0.032662757,0.5377315],"study_design_scores_gemma":[0.000022815566,0.00008259954,0.00071331026,0.000015813937,0.000016588567,0.00004118604,0.000043375272,0.9907268,0.0052555236,0.00094853766,0.0021175377,0.000015946132],"about_ca_topic_score_codex":0.012257129,"about_ca_topic_score_gemma":0.024976626,"teacher_disagreement_score":0.012257129,"about_ca_system_score_codex":0.0015785589,"about_ca_system_score_gemma":0.0013893691,"threshold_uncertainty_score":0.024371564},"labels":[],"label_agreement":null},{"id":"W4409934889","doi":"10.3390/math13091456","title":"Hybrid Deep Neural Network with Domain Knowledge for Text Sentiment Analysis","year":2025,"lang":"en","type":"article","venue":"Mathematics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Iran Telecommunication Research Center","keywords":"Sentiment analysis; Computer science; Artificial neural network; Natural language processing; Artificial intelligence; Domain (mathematical analysis); Domain knowledge; Mathematics","score_opus":0.013542160479096692,"score_gpt":0.27088373992745,"score_spread":0.25734157944835334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409934889","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.106451795,0.0019117633,0.87397426,0.0012182599,0.00033660707,0.00019798528,0.00090044603,0.0030399216,0.011968845],"genre_scores_gemma":[0.79149985,0.0011655493,0.1940744,0.00064073614,0.00031935517,0.00024045988,0.0020926653,0.00011596706,0.009851074],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976903,0.000052495034,0.000018491783,0.0000624284,0.000064925734,0.000032570395],"domain_scores_gemma":[0.99971277,0.00009601049,0.0000387565,0.000021115378,0.00011752383,0.000013822998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005824657,0.0007555669,0.00039272557,0.0008504992,0.0002613946,0.00069568714,0.00069307885,0.0006118935,0.0016257215],"category_scores_gemma":[0.001327636,0.00022424247,0.0005769444,0.00075722334,0.00023608831,0.0012836966,0.0005461919,0.0010951156,0.00096435787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043283746,0.00047120292,0.0053755566,0.0002809201,0.00027022805,0.00027284585,0.00019738862,0.281737,0.034634195,0.009025763,0.014145866,0.6531561],"study_design_scores_gemma":[0.00000524938,0.000020236299,0.00035937052,0.00000848828,0.00001513344,0.00001340301,0.000013782965,0.99408627,0.0016816305,0.0026799042,0.0011122391,0.0000043515856],"about_ca_topic_score_codex":0.004064007,"about_ca_topic_score_gemma":0.0064826193,"teacher_disagreement_score":0.004064007,"about_ca_system_score_codex":0.000748014,"about_ca_system_score_gemma":0.00060418225,"threshold_uncertainty_score":0.008080661},"labels":[],"label_agreement":null},{"id":"W4410022133","doi":"10.1016/j.dajour.2025.100581","title":"A dual-phase framework for detecting authentic and computer-generated customer reviews using large language models","year":2025,"lang":"en","type":"article","venue":"Decision Analytics Journal","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Toronto Metropolitan University","keywords":"Dual (grammatical number); Computer science; Phase (matter); Natural language processing; Artificial intelligence; Linguistics; Physics","score_opus":0.0690834967072373,"score_gpt":0.3941266481487731,"score_spread":0.3250431514415358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410022133","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048415147,0.0008848186,0.94362307,0.00078608585,0.000122067075,0.00035240003,0.00043036646,0.0038884971,0.0014974616],"genre_scores_gemma":[0.620295,0.00039420841,0.36984667,0.00079826557,0.00027997582,0.00045524203,0.001684848,0.00025199616,0.005993739],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99790394,0.00082747,0.00012986897,0.0005643786,0.00044830042,0.0001259843],"domain_scores_gemma":[0.99470705,0.0027675831,0.0006392286,0.00044366374,0.0012451748,0.0001973244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024452715,0.0013256806,0.0010048513,0.001609794,0.0004226394,0.0017054927,0.0016246492,0.0014264359,0.000919495],"category_scores_gemma":[0.0076514008,0.0006418882,0.0009573204,0.0005363799,0.0007284751,0.0017797566,0.0011318232,0.0018768401,0.001413192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012288736,0.000872735,0.013202921,0.00051648595,0.00029928912,0.0009644289,0.0009420942,0.1466193,0.064905174,0.01066726,0.013185789,0.7465957],"study_design_scores_gemma":[0.00002239812,0.00014030865,0.0009072265,0.000011377086,0.000029883066,0.00017624798,0.000031328473,0.9861033,0.007896894,0.0031021878,0.001552767,0.000026143138],"about_ca_topic_score_codex":0.002960849,"about_ca_topic_score_gemma":0.0047397977,"teacher_disagreement_score":0.002960849,"about_ca_system_score_codex":0.00085902173,"about_ca_system_score_gemma":0.0011133268,"threshold_uncertainty_score":0.012931943},"labels":[],"label_agreement":null},{"id":"W4410042513","doi":"10.1007/978-3-031-82896-6_7","title":"Exploring Emerging NLP and Machine Learning Methods in Climate Change Discourse Analysis on Social Media: A Systematic Literature Review","year":2025,"lang":"en","type":"book-chapter","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Université du Québec à Montréal","funders":"École de technologie supérieure","keywords":"Artificial intelligence; Social media; Natural language processing; Linguistics; Sentiment analysis; Computer science; World Wide Web; Philosophy","score_opus":0.1606860424448461,"score_gpt":0.37994013873605637,"score_spread":0.21925409629121026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410042513","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002242818,0.98671025,0.005401813,0.0019346084,0.0002578197,0.0008946089,0.0004648113,0.000030843632,0.002062466],"genre_scores_gemma":[0.019897282,0.9633292,0.012793512,0.0008233297,0.00019500223,0.0022273653,0.00039694578,0.000031830903,0.00030547104],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.983414,0.009463949,0.0031852082,0.0010309406,0.0026392217,0.0002666825],"domain_scores_gemma":[0.8399911,0.14478742,0.0060951393,0.0014434889,0.007245637,0.00043723374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02808788,0.0010722618,0.0031495837,0.025006337,0.0011584145,0.004988022,0.001578939,0.0016710073,0.004841769],"category_scores_gemma":[0.07788484,0.0007560563,0.002891553,0.021547379,0.0020288252,0.0071553118,0.003027596,0.0019049376,0.0008170678],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007536039,0.00007280193,0.0014469775,0.47417924,0.001235763,0.00021836677,0.0030012778,0.00035631302,0.00044478662,0.0044187424,0.003878122,0.5106722],"study_design_scores_gemma":[0.00004414773,0.0001447282,0.0034638077,0.8578634,0.004335873,0.00035197893,0.005317958,0.0007173368,0.0006120401,0.0049433024,0.12214066,0.000064800624],"about_ca_topic_score_codex":0.0033658848,"about_ca_topic_score_gemma":0.010598363,"teacher_disagreement_score":0.02808788,"about_ca_system_score_codex":0.00325424,"about_ca_system_score_gemma":0.016028764,"threshold_uncertainty_score":0.14854473},"labels":[],"label_agreement":null},{"id":"W4410133712","doi":"10.1007/978-3-031-88653-9_63","title":"Efficient Aspect-Based Sentiment Analysis for Conversational Recommendation Based on a Distilled TinyBERT Model","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Sentiment analysis; Distilled water; Computer science; Natural language processing; Artificial intelligence; Chemistry; Chromatography","score_opus":0.019958410299693776,"score_gpt":0.2508587389444655,"score_spread":0.2309003286447717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410133712","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029899137,0.00031903983,0.9642365,0.0001830944,0.00008952563,0.00009384255,0.0004708965,0.0028964977,0.0018113301],"genre_scores_gemma":[0.59597325,0.00047364895,0.39418557,0.00018116857,0.0001765687,0.00024557754,0.0025415989,0.00040692432,0.005815787],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996699,0.00006554518,0.000027671807,0.0000757119,0.00011339696,0.000047721125],"domain_scores_gemma":[0.9994462,0.00023533653,0.000034497334,0.000064294676,0.00018211933,0.00003737778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051271956,0.00051232404,0.0009428172,0.00083805644,0.00051242905,0.0008832701,0.0009999012,0.00052113086,0.0024625584],"category_scores_gemma":[0.0016677189,0.00032225795,0.0010895027,0.0008774528,0.00023889789,0.0014057993,0.0006684102,0.00078446936,0.0014093522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007793216,0.00035002746,0.0052883504,0.00023474245,0.00032891697,0.00031525872,0.000294046,0.2030736,0.038038325,0.018148428,0.013255972,0.71989304],"study_design_scores_gemma":[0.0000049806295,0.000014232537,0.00018397039,0.0000029958464,0.000018857962,0.000015682272,0.000009826919,0.99572057,0.0010180697,0.002451933,0.00055353733,0.0000053444032],"about_ca_topic_score_codex":0.010125239,"about_ca_topic_score_gemma":0.01752711,"teacher_disagreement_score":0.010125239,"about_ca_system_score_codex":0.00055957254,"about_ca_system_score_gemma":0.00087836874,"threshold_uncertainty_score":0.020132601},"labels":[],"label_agreement":null},{"id":"W4410162610","doi":"10.2139/ssrn.5167493","title":"Can Large Language Models Extract Customer Needs as well as Professional Analysts?","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Business; Computer science; Data science","score_opus":0.010740931560754067,"score_gpt":0.2966830552355032,"score_spread":0.28594212367474914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410162610","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4744355,0.0021699476,0.46421343,0.018234685,0.0009176932,0.00025959493,0.010719056,0.0052163517,0.023833679],"genre_scores_gemma":[0.92681664,0.0006805991,0.057513908,0.0013165389,0.00062642683,0.0001743503,0.007870761,0.00035046338,0.004650355],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990017,0.0004944687,0.00004890029,0.00015899123,0.00017419123,0.000121704856],"domain_scores_gemma":[0.9948495,0.0033028894,0.00031289295,0.00043899266,0.00092933816,0.00016645408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022946924,0.0012416135,0.00071289734,0.0015388078,0.0004066888,0.001969971,0.0007654192,0.001292631,0.003777376],"category_scores_gemma":[0.013271628,0.00043056902,0.000996679,0.0012911803,0.00028028258,0.0048694084,0.000657009,0.0019226968,0.0051736496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017207296,0.0013751975,0.09094764,0.00054096413,0.00089727435,0.00057279266,0.00093343994,0.048946217,0.03324748,0.014004851,0.1176949,0.68911856],"study_design_scores_gemma":[0.00009712305,0.0001624488,0.015954593,0.00008215517,0.0002566523,0.00015877213,0.0008020335,0.9168606,0.0055010053,0.044421755,0.015655534,0.000047201826],"about_ca_topic_score_codex":0.0029324146,"about_ca_topic_score_gemma":0.0059447372,"teacher_disagreement_score":0.003777376,"about_ca_system_score_codex":0.00045454878,"about_ca_system_score_gemma":0.00074113463,"threshold_uncertainty_score":0.012636602},"labels":[],"label_agreement":null},{"id":"W4410416953","doi":"10.54254/2753-8818/2025.22733","title":"Sentiment Analysis Applied on Tweets","year":2025,"lang":"en","type":"article","venue":"Theoretical and Natural Science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Sentiment analysis; Computer science; Information retrieval; Data science; Natural language processing; World Wide Web","score_opus":0.0037776055262624527,"score_gpt":0.2581465589642757,"score_spread":0.25436895343801325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410416953","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55351007,0.0009926499,0.36616567,0.0015157859,0.0014699742,0.0017385915,0.020995721,0.0041622245,0.049449272],"genre_scores_gemma":[0.8545292,0.0005793842,0.12501362,0.00017572298,0.00030434222,0.0007234109,0.009938477,0.00024074005,0.008495056],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991333,0.0002297341,0.0001039606,0.00015729877,0.0002758061,0.0000998334],"domain_scores_gemma":[0.99842834,0.00057994766,0.00014186453,0.00010247053,0.00070970895,0.000037641978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010780761,0.0006417001,0.0003798286,0.0024322674,0.00055027613,0.0014325673,0.000275579,0.0003415426,0.0045712595],"category_scores_gemma":[0.0055039967,0.00013383105,0.0008465552,0.0020241376,0.00023010245,0.0010465283,0.0004845504,0.00066779926,0.0024121166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010498977,0.00032581223,0.05432946,0.0012824531,0.00047315925,0.0007752041,0.0027820147,0.024138268,0.09424472,0.012723382,0.029865595,0.7780101],"study_design_scores_gemma":[0.00007396784,0.0005922173,0.09750513,0.00033997867,0.00025757236,0.0006312468,0.0045858114,0.7098166,0.08200064,0.021796959,0.08224385,0.0001560168],"about_ca_topic_score_codex":0.0028056055,"about_ca_topic_score_gemma":0.0025978892,"teacher_disagreement_score":0.0045712595,"about_ca_system_score_codex":0.00060283166,"about_ca_system_score_gemma":0.00056213065,"threshold_uncertainty_score":0.015292406},"labels":[],"label_agreement":null},{"id":"W4410486517","doi":"10.1145/3717867.3717913","title":"Using Semantically Unrelated and Opposite Terms for In-Context Learning: A Case Study in Identifying Political Aversion in Tweets","year":2025,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"York University; American Political Science Association","keywords":"Context (archaeology); Politics; Computer science; Artificial intelligence; Natural language processing; Cognitive psychology; Psychology; Political science; History","score_opus":0.06307663292143945,"score_gpt":0.36887339335113845,"score_spread":0.305796760429699,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410486517","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94468963,0.00018365192,0.051173102,0.0009413605,0.0000600208,0.00019872663,0.00044315337,0.00046672334,0.001843679],"genre_scores_gemma":[0.9679002,0.00005063544,0.03087457,0.00020125997,0.000028384708,0.00008577981,0.00040932433,0.000078197314,0.0003716562],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99037445,0.007723447,0.00028893515,0.00086382474,0.00054964126,0.00019971568],"domain_scores_gemma":[0.9033193,0.088176295,0.0026342708,0.0037908207,0.0014220431,0.0006572589],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010456561,0.00093589345,0.0006129453,0.00069473934,0.00092628336,0.0020353487,0.001063753,0.0018775013,0.0011645367],"category_scores_gemma":[0.062016077,0.00033164772,0.00068363885,0.0008493197,0.0013932781,0.0038638702,0.0017517288,0.0035222985,0.0006731453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00903106,0.0047081634,0.36974138,0.0022248314,0.00048663892,0.004942074,0.03948435,0.07879404,0.106847495,0.026776833,0.010435859,0.34652728],"study_design_scores_gemma":[0.00047511168,0.0025133123,0.05622733,0.00027392348,0.00031435897,0.0025481638,0.011718602,0.7444654,0.09033698,0.072520584,0.018265538,0.00034062058],"about_ca_topic_score_codex":0.0015602502,"about_ca_topic_score_gemma":0.002167169,"teacher_disagreement_score":0.010456561,"about_ca_system_score_codex":0.00088598934,"about_ca_system_score_gemma":0.0005657109,"threshold_uncertainty_score":0.055300236},"labels":[],"label_agreement":null},{"id":"W4410853196","doi":"10.1109/ecce64574.2025.11013083","title":"A Hybrid Deep Learning Model for Sentiment Analysis of Multilingual Comments on Trending YouTube Videos","year":2025,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Sentiment analysis; Deep learning; Artificial intelligence; Natural language processing; Machine learning","score_opus":0.029478731107586694,"score_gpt":0.32592996722491535,"score_spread":0.29645123611732865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410853196","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5355783,0.0013775068,0.44616255,0.0015839138,0.00043008575,0.00022723812,0.0019345391,0.0034343102,0.0092714485],"genre_scores_gemma":[0.9303853,0.0004648666,0.054753486,0.0004138262,0.00008346124,0.0001555385,0.0023574778,0.00007448228,0.011311567],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986696,0.00001979216,0.000009397963,0.000040687442,0.000029859319,0.000033177384],"domain_scores_gemma":[0.9998115,0.00003335485,0.000017555896,0.0000077154245,0.00011786352,0.000012111818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037793108,0.00083145726,0.0003398721,0.00055044214,0.0002468626,0.00045050707,0.00066024414,0.0005154435,0.0011339448],"category_scores_gemma":[0.00069865107,0.000205409,0.0005044415,0.00038975655,0.00015865512,0.0006324997,0.00041112787,0.0007970268,0.0006694507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006997477,0.00084602565,0.024610098,0.00022133173,0.00037010212,0.00043645297,0.00041879906,0.2671075,0.048846584,0.0025065215,0.019296393,0.6346404],"study_design_scores_gemma":[0.000005934588,0.000035259978,0.0009887298,0.0000073705783,0.000015465426,0.000011554681,0.000029467386,0.9962114,0.0019180338,0.00031603145,0.00045529997,0.0000054413044],"about_ca_topic_score_codex":0.017624538,"about_ca_topic_score_gemma":0.022773912,"teacher_disagreement_score":0.017624538,"about_ca_system_score_codex":0.00069623,"about_ca_system_score_gemma":0.000676701,"threshold_uncertainty_score":0.035043895},"labels":[],"label_agreement":null},{"id":"W4410973968","doi":"10.23977/acss.2025.090213","title":"Review of Public Opinion Sentiment Recognition Technology","year":2025,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Public opinion; Sentiment analysis; Computer science; Data science; Artificial intelligence; Political science; Law; Politics","score_opus":0.03472220651972273,"score_gpt":0.3153636486552712,"score_spread":0.2806414421355485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410973968","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014450899,0.96744955,0.009334491,0.006123257,0.0024941235,0.000042818596,0.00020544365,0.00010560234,0.012799699],"genre_scores_gemma":[0.013280197,0.9725318,0.0055079935,0.0023132588,0.0029501852,0.000061027615,0.00038150462,0.000047947055,0.002926048],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991347,0.00018260667,0.000120709105,0.00018190083,0.00032518874,0.00005495164],"domain_scores_gemma":[0.9964206,0.0018373079,0.0002529111,0.000096458745,0.0013030366,0.000089664376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002266443,0.0007697296,0.0009416411,0.002876285,0.0004516866,0.0022346824,0.0011363736,0.0011853848,0.0041606138],"category_scores_gemma":[0.0050132065,0.0004972047,0.00073870167,0.0031517132,0.0007693736,0.002894348,0.000746264,0.0012723301,0.003011165],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006423836,0.000040494655,0.0008403545,0.008494591,0.000093248396,0.00011257369,0.00014693344,0.00053180836,0.0016953754,0.010400439,0.055556085,0.92202395],"study_design_scores_gemma":[0.000012181021,0.00009032587,0.003923206,0.0054148147,0.00017329662,0.0006095031,0.0002901738,0.0020562534,0.0020387685,0.010610376,0.9747175,0.000063593594],"about_ca_topic_score_codex":0.0020563859,"about_ca_topic_score_gemma":0.002078006,"teacher_disagreement_score":0.0041606138,"about_ca_system_score_codex":0.00091452914,"about_ca_system_score_gemma":0.0015194779,"threshold_uncertainty_score":0.013918638},"labels":[],"label_agreement":null},{"id":"W4411114428","doi":"10.21015/vtse.v13i2.2103","title":"Covid-19 Sentiment Analysis on X (formerly Twitter) Using Machine Learning Classifiers: Performance Comparison and Key Insights","year":2025,"lang":"en","type":"article","venue":"VFAST Transactions on Software Engineering","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Key (lock); Coronavirus disease 2019 (COVID-19); Sentiment analysis; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Computer science; Artificial intelligence; Natural language processing; Data science; Biology; Medicine; Virology","score_opus":0.026503806660967723,"score_gpt":0.27342908676320743,"score_spread":0.2469252801022397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411114428","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91857696,0.0030708462,0.0550595,0.0010257022,0.0007979413,0.0003928652,0.004963604,0.0037644499,0.012348114],"genre_scores_gemma":[0.93727875,0.0009992471,0.04910375,0.00017104579,0.0002595356,0.00017379968,0.008679694,0.000075294134,0.0032588637],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99845994,0.0004787269,0.00019210772,0.00022520387,0.00045253665,0.00019152473],"domain_scores_gemma":[0.9981211,0.00080448587,0.00019482673,0.00013955889,0.00061137654,0.000128716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024882758,0.0008735204,0.0009533783,0.0021404936,0.0004669591,0.0011494084,0.00043202177,0.0006565759,0.0014213849],"category_scores_gemma":[0.0044692624,0.00013679209,0.0006100124,0.0011255494,0.0001442761,0.0011554593,0.0006001699,0.00058855634,0.0015151679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033059733,0.0009528005,0.12779556,0.00096034305,0.0006577324,0.00034318323,0.00043168935,0.02983146,0.034820404,0.0015346385,0.036624342,0.7627419],"study_design_scores_gemma":[0.00008451669,0.0010677647,0.05911518,0.00009215229,0.00015463322,0.0002010648,0.0006105291,0.90765506,0.022713965,0.000915413,0.007326199,0.00006355528],"about_ca_topic_score_codex":0.0042019724,"about_ca_topic_score_gemma":0.0033047586,"teacher_disagreement_score":0.0042019724,"about_ca_system_score_codex":0.0005321707,"about_ca_system_score_gemma":0.00049882225,"threshold_uncertainty_score":0.013159394},"labels":[],"label_agreement":null},{"id":"W4411292526","doi":"10.1145/3744641","title":"Deep Learning in Stance Detection: A Survey","year":2025,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Artificial intelligence; Deep learning; Natural language processing; Machine learning; Computer vision","score_opus":0.06006815134239572,"score_gpt":0.34610166929569103,"score_spread":0.2860335179532953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411292526","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008154187,0.868649,0.09477225,0.00470911,0.0014042999,0.00014364767,0.00071965356,0.00084102055,0.020606784],"genre_scores_gemma":[0.07017825,0.8572925,0.052289013,0.0022832097,0.0019702038,0.00017547418,0.002264735,0.0002254988,0.0133210765],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994868,0.000100804835,0.000050820283,0.00013075804,0.0001838502,0.00004689559],"domain_scores_gemma":[0.998689,0.0008041878,0.000068148714,0.00006211132,0.00033059905,0.000046004374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014014302,0.0011919497,0.0012374836,0.00221069,0.00034787453,0.0015115128,0.0017686337,0.0012050804,0.004247615],"category_scores_gemma":[0.0032943771,0.00057714153,0.0009090001,0.0030189552,0.00047901334,0.002742145,0.0011581013,0.001745998,0.003360682],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046068293,0.00009963885,0.0012634346,0.0022941318,0.00007603081,0.000036879275,0.000064790736,0.0036415888,0.00059202383,0.005550642,0.027182823,0.959152],"study_design_scores_gemma":[0.000069503345,0.0004433345,0.006954167,0.006292568,0.0005166881,0.0008099455,0.0006172786,0.13511921,0.0067736283,0.059248388,0.7829982,0.00015714724],"about_ca_topic_score_codex":0.003540612,"about_ca_topic_score_gemma":0.004009811,"teacher_disagreement_score":0.004247615,"about_ca_system_score_codex":0.0008234555,"about_ca_system_score_gemma":0.0011970829,"threshold_uncertainty_score":0.014209628},"labels":[],"label_agreement":null},{"id":"W4411311119","doi":"10.1007/978-981-96-1758-6_38","title":"A Study on Procrastination Through Sentiment Analysis of Social Media Data Using NLP Techniques","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Becton Dickinson (Canada)","funders":"","keywords":"Sentiment analysis; Procrastination; Natural language processing; Artificial intelligence; Social media; Computer science; Linguistics; Psychology; World Wide Web; Social psychology; Philosophy","score_opus":0.07210833698592739,"score_gpt":0.32710534792235857,"score_spread":0.2549970109364312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411311119","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.913426,0.0015979691,0.058892284,0.00094200956,0.000091120106,0.00018918447,0.0005409978,0.00013529924,0.024185194],"genre_scores_gemma":[0.9666912,0.00097436813,0.026354006,0.00015200164,0.0001085993,0.00008747112,0.00057182775,0.000074180716,0.0049863504],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872774,0.0006104695,0.00007716118,0.00018262284,0.00033203122,0.00006991044],"domain_scores_gemma":[0.9854791,0.012440282,0.0004627186,0.0003611559,0.0011591162,0.00009761011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015764642,0.00024455623,0.0002722867,0.0012087051,0.0007365773,0.0013794347,0.0003428792,0.00040306558,0.0014339937],"category_scores_gemma":[0.011102625,0.0001377124,0.0004159099,0.0022510397,0.0006015913,0.0021517815,0.00033715507,0.0006842633,0.0003277105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00095966895,0.0011732408,0.09994845,0.0013147176,0.00027007412,0.002463535,0.024030002,0.0056220233,0.05174073,0.035761993,0.011645504,0.76507],"study_design_scores_gemma":[0.00013335483,0.0017711393,0.3241968,0.00048190955,0.00058174436,0.004130798,0.039621033,0.41354376,0.076085255,0.03997259,0.09928291,0.00019867017],"about_ca_topic_score_codex":0.0034294345,"about_ca_topic_score_gemma":0.0025323818,"teacher_disagreement_score":0.0034294345,"about_ca_system_score_codex":0.00068330375,"about_ca_system_score_gemma":0.00039251166,"threshold_uncertainty_score":0.008337259},"labels":[],"label_agreement":null},{"id":"W4411380165","doi":"10.1007/s10489-025-06313-8","title":"Fpa-GCN: enhancing aspect sentiment triplet extraction with feature-rich prediction-aware graph convolutional networks","year":2025,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"National Natural Science Foundation of China","keywords":"Computer science; Graph; Artificial intelligence; Sentiment analysis; Pattern recognition (psychology); Feature extraction; Theoretical computer science","score_opus":0.009891657229905912,"score_gpt":0.2519052212767816,"score_spread":0.24201356404687568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411380165","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15988585,0.0020567952,0.7744478,0.00085528585,0.00093598006,0.00033007996,0.006096434,0.033867568,0.021524256],"genre_scores_gemma":[0.61105585,0.00093687617,0.34595695,0.00093427,0.00022308793,0.00019030199,0.014856516,0.00105494,0.024791218],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999859,0.000011959366,0.0000054185675,0.00005327644,0.000039981438,0.000030420366],"domain_scores_gemma":[0.999808,0.000040099567,0.000018801209,0.000038406917,0.0000768681,0.000017822626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023357951,0.00124956,0.0005058909,0.0010465432,0.00037439566,0.00061129546,0.00095777924,0.0005882176,0.003807074],"category_scores_gemma":[0.00070507755,0.00029902934,0.00069685065,0.000993724,0.00020467877,0.00090377347,0.00065290864,0.0010652401,0.0022016377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037105323,0.00039678143,0.0035073357,0.00018463358,0.00020651848,0.00024782008,0.00008067868,0.042294048,0.059301496,0.005291148,0.052282628,0.83583593],"study_design_scores_gemma":[0.000018548128,0.00005718257,0.0016365951,0.000019678924,0.000066974,0.000060944418,0.000025657002,0.97428775,0.0121222865,0.005415347,0.006273504,0.000015563379],"about_ca_topic_score_codex":0.020458423,"about_ca_topic_score_gemma":0.046328176,"teacher_disagreement_score":0.020458423,"about_ca_system_score_codex":0.0006921732,"about_ca_system_score_gemma":0.00075467315,"threshold_uncertainty_score":0.04067868},"labels":[],"label_agreement":null},{"id":"W4411569198","doi":"10.1007/s44443-025-00094-3","title":"Multi-level fusion with fine-grained alignment for multimodal sentiment analysis","year":2025,"lang":"en","type":"article","venue":"Journal of King Saud University - Computer and Information Sciences","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sentiment analysis; Computer science; Fusion; Natural language processing; Artificial intelligence; Linguistics","score_opus":0.0217561221763997,"score_gpt":0.2563737658090947,"score_spread":0.23461764363269502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411569198","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0630478,0.0008217937,0.92631626,0.00033975646,0.00019715684,0.0001982659,0.0006526168,0.00451817,0.0039081965],"genre_scores_gemma":[0.6461033,0.0005030257,0.3445437,0.00029367852,0.00020373889,0.00032346314,0.002614467,0.00046441215,0.0049502654],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930716,0.00013509937,0.00004568734,0.0002315356,0.00017237815,0.000108047025],"domain_scores_gemma":[0.999498,0.00011453596,0.000072845985,0.000055296878,0.00022781495,0.000031542986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010525575,0.0014074523,0.0008381279,0.0019716045,0.0006487797,0.0007725548,0.00075781817,0.0007946569,0.0044285273],"category_scores_gemma":[0.002120192,0.00030343476,0.0013543254,0.0013695244,0.00039788353,0.0015966333,0.0014006377,0.0011762868,0.0017350907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005688983,0.00020595684,0.0034191287,0.00022403145,0.00020227204,0.00023446385,0.00043884147,0.050387867,0.103150554,0.004155791,0.01129667,0.8257156],"study_design_scores_gemma":[0.000020520576,0.00012587207,0.004012194,0.000025544652,0.000087132445,0.00007120242,0.00018392853,0.9605452,0.021979872,0.007560054,0.005355232,0.000033295702],"about_ca_topic_score_codex":0.0046887803,"about_ca_topic_score_gemma":0.0048802416,"teacher_disagreement_score":0.0046887803,"about_ca_system_score_codex":0.00075283076,"about_ca_system_score_gemma":0.0005860477,"threshold_uncertainty_score":0.014814913},"labels":[],"label_agreement":null},{"id":"W4411617968","doi":"10.51847/beesqdvsrx","title":"10.51847/bEESQDVsrx","year":2000,"lang":"en","type":"review","venue":"Time to knit","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Data science; Computer science","score_opus":0.03119814284005192,"score_gpt":0.2655041113680675,"score_spread":0.23430596852801558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411617968","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00090806774,0.004333748,0.0028721422,0.0009055511,0.00076121505,0.0001950578,0.0016820664,0.0019992397,0.98634297],"genre_scores_gemma":[0.0017811734,0.0015880051,0.0012994015,0.00055185385,0.00009170544,0.00008041791,0.0009693403,0.0002645219,0.99337363],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995665,0.000063554355,0.000048207476,0.000106947344,0.00016354823,0.00005107525],"domain_scores_gemma":[0.9991486,0.00021144228,0.000043023807,0.00017203862,0.00028446643,0.00014046325],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00084332994,0.0012084352,0.000850088,0.0027142316,0.0012037359,0.0026719854,0.0017828014,0.0032567002,0.9522635],"category_scores_gemma":[0.0011396789,0.0006327001,0.0007525679,0.0034188044,0.00077841495,0.0024941568,0.0024402752,0.0011305474,0.9591691],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000079746016,0.000080183745,0.00025372373,0.00069361,0.000016458702,0.000104488,0.0000823396,0.00015513519,0.002099238,0.0037506449,0.24285273,0.74983156],"study_design_scores_gemma":[0.000018544331,0.000026272863,0.00031053292,0.00018359852,0.0000058370524,0.00013225696,0.00005878002,0.000075126605,0.0003010569,0.000538107,0.9983423,0.000007591535],"about_ca_topic_score_codex":0.00375976,"about_ca_topic_score_gemma":0.0034545166,"teacher_disagreement_score":0.047736526,"about_ca_system_score_codex":0.0009045873,"about_ca_system_score_gemma":0.00070363673,"threshold_uncertainty_score":0.06809026},"labels":[],"label_agreement":null},{"id":"W4412091361","doi":"10.14419/9g302r53","title":"Deep Learning-Based Classification of Comments and Reviews for Sustainable Development Goals (SDGs) with Web Application Implementation","year":2025,"lang":"en","type":"article","venue":"International Journal of Basic and Applied Sciences","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Sustainable development; Process management; Artificial intelligence; Data science; Business; Political science","score_opus":0.023112619588056185,"score_gpt":0.33260791881929946,"score_spread":0.3094952992312433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412091361","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4189173,0.0014507116,0.5109755,0.0024134582,0.0006143531,0.0014270584,0.011813707,0.035876274,0.016511627],"genre_scores_gemma":[0.69438773,0.00038472068,0.2829928,0.00037041117,0.00013315665,0.0005758287,0.013008069,0.00020545907,0.007941751],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992772,0.00022521697,0.00007908505,0.00018901232,0.00016509421,0.00006438565],"domain_scores_gemma":[0.9975612,0.001016715,0.00021252074,0.00015570638,0.00095482473,0.000099014724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001281516,0.00073367986,0.0003227062,0.0019697759,0.0002870382,0.0009607848,0.00071707857,0.0006440289,0.0025358398],"category_scores_gemma":[0.004771197,0.00020132987,0.00055813056,0.00096545176,0.00014792998,0.0010300586,0.0006085925,0.00076428725,0.0020062537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00085610274,0.0011753964,0.019649856,0.0007468143,0.00019962642,0.00038577325,0.00057186757,0.043251965,0.026264397,0.0019387921,0.031636678,0.8733227],"study_design_scores_gemma":[0.000030736683,0.00012658954,0.0056134886,0.000045272296,0.000038615435,0.000053470885,0.00024799755,0.9725954,0.012701752,0.0020678935,0.0064583323,0.000020468093],"about_ca_topic_score_codex":0.005658102,"about_ca_topic_score_gemma":0.010091206,"teacher_disagreement_score":0.005658102,"about_ca_system_score_codex":0.000750629,"about_ca_system_score_gemma":0.00082435255,"threshold_uncertainty_score":0.011250317},"labels":[],"label_agreement":null},{"id":"W4412416668","doi":"10.1177/20563051251355456","title":"Knowing Your Users by Heart: A Critical Examination of the Scientific Research on Emotions Conducted by Social Media Platforms","year":2025,"lang":"en","type":"article","venue":"Social Media + Society","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke; Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Social media; Psychology; Internet privacy; Sociology; Advertising; Computer science; World Wide Web; Business","score_opus":0.12098166310126988,"score_gpt":0.3891509214502285,"score_spread":0.2681692583489586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412416668","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42311138,0.15531349,0.044949353,0.2696738,0.006458892,0.00070495554,0.00066183717,0.00015343518,0.09897279],"genre_scores_gemma":[0.9171386,0.0487876,0.009580208,0.017214932,0.0027466856,0.00058411114,0.00016680387,0.00022885873,0.0035521863],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9675742,0.02334566,0.0014378562,0.0022979334,0.0043585035,0.0009858849],"domain_scores_gemma":[0.6934048,0.28045037,0.0063753636,0.004664816,0.013950205,0.0011544534],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.054765776,0.0009796956,0.0008865982,0.015050609,0.009427104,0.016668128,0.0015185066,0.0035267381,0.0016012646],"category_scores_gemma":[0.1045036,0.0006259898,0.00073860236,0.007586107,0.030168835,0.021646556,0.0066990703,0.0071973647,0.00035718764],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007448778,0.000034460358,0.0040404582,0.00188001,0.00005974739,0.0008102983,0.8343582,0.00008624376,0.0016121792,0.08026004,0.007652883,0.0691311],"study_design_scores_gemma":[0.000009780123,0.00006458329,0.0074014687,0.0073754266,0.000073967894,0.00076135143,0.7891028,0.0004460854,0.0017876059,0.03658,0.15631421,0.0000826993],"about_ca_topic_score_codex":0.0025555901,"about_ca_topic_score_gemma":0.0039340504,"teacher_disagreement_score":0.94523424,"about_ca_system_score_codex":0.0070833443,"about_ca_system_score_gemma":0.006642041,"threshold_uncertainty_score":0.28963256},"labels":[],"label_agreement":null},{"id":"W4412446474","doi":"10.1109/icici65870.2025.11069632","title":"An Emotion-Aware Recipe Generation Framework Using Distilbert and Large Language Models","year":2025,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Future Earth","funders":"","keywords":"Recipe; Computer science; Natural language processing; Artificial intelligence; Programming language; History","score_opus":0.030364042325144985,"score_gpt":0.32229782759172854,"score_spread":0.29193378526658353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412446474","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02118849,0.00017108374,0.9647106,0.00035799405,0.000060459803,0.00011745088,0.00068623415,0.009991021,0.0027167292],"genre_scores_gemma":[0.387226,0.00026569972,0.6010596,0.00039206079,0.000056385103,0.0003674007,0.0025632295,0.0008492621,0.007220395],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99981683,0.000052780026,0.000011593978,0.0000616618,0.000041433952,0.000015708929],"domain_scores_gemma":[0.9996556,0.00019396261,0.00001975385,0.00003366029,0.000077211436,0.000019747482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044335346,0.0007556771,0.00033563815,0.00043565384,0.00028437938,0.0007491926,0.00092656707,0.00052983186,0.0035467476],"category_scores_gemma":[0.0014699586,0.00042209684,0.0008987787,0.00022783835,0.00026564457,0.00091953133,0.0006884883,0.0012174172,0.0016228203],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062442065,0.00055896875,0.004924631,0.000442495,0.0003230405,0.00055647845,0.0009298887,0.3660259,0.07798845,0.026673868,0.022873865,0.49807814],"study_design_scores_gemma":[0.000014531758,0.000026370315,0.00021688001,0.0000058696933,0.00001719622,0.000027569491,0.000025927238,0.98674345,0.0046081617,0.0049729077,0.0033297539,0.000011286379],"about_ca_topic_score_codex":0.0062400084,"about_ca_topic_score_gemma":0.014871624,"teacher_disagreement_score":0.0062400084,"about_ca_system_score_codex":0.00068398064,"about_ca_system_score_gemma":0.00059472816,"threshold_uncertainty_score":0.012407362},"labels":[],"label_agreement":null},{"id":"W4412470267","doi":"10.1007/s42979-025-04141-8","title":"Taxonomy of Opinion Mining, Approaches and Domain Applications: Future Research Direction","year":2025,"lang":"en","type":"article","venue":"SN Computer Science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Taxonomy (biology); Domain (mathematical analysis); Data science; Computer science; Information retrieval; Data mining; Biology; Mathematics; Ecology","score_opus":0.08290433372789967,"score_gpt":0.3292632663399526,"score_spread":0.24635893261205294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412470267","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040357932,0.41571736,0.41710913,0.0478725,0.002751597,0.00092544325,0.001715247,0.0019072111,0.07164357],"genre_scores_gemma":[0.13999932,0.2740358,0.5613889,0.0049813194,0.0028115027,0.000743059,0.0025216811,0.00027338322,0.013245089],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99822944,0.00058760354,0.0002041416,0.00033337515,0.0005030182,0.00014234755],"domain_scores_gemma":[0.9853139,0.0067850268,0.0008836959,0.00091136014,0.0052768015,0.0008292621],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006903223,0.00078633963,0.0013782411,0.008051447,0.0015345154,0.009728815,0.0018383248,0.0014664535,0.0063586095],"category_scores_gemma":[0.0131592015,0.00043335446,0.0010432304,0.010866597,0.0012588468,0.011282604,0.0017970599,0.0023491927,0.0035345375],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014378286,0.00026485493,0.006723561,0.0022250738,0.000048581565,0.00007766556,0.0006044678,0.0008835944,0.0026804,0.061705194,0.0151287345,0.909514],"study_design_scores_gemma":[0.000111946436,0.00082918705,0.01914752,0.007884097,0.00027421323,0.0019475502,0.007962265,0.05635053,0.0047649024,0.43072146,0.46975085,0.00025554022],"about_ca_topic_score_codex":0.0029546167,"about_ca_topic_score_gemma":0.0033242756,"teacher_disagreement_score":0.009728815,"about_ca_system_score_codex":0.0021017871,"about_ca_system_score_gemma":0.0038964548,"threshold_uncertainty_score":0.036508143},"labels":[],"label_agreement":null},{"id":"W4412748883","doi":"10.1371/journal.pone.0326936","title":"Investigating the impact of social media images on users’ sentiments towards sociopolitical events based on deep artificial intelligence","year":2025,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Social media; Sentiment analysis; Public opinion; Big data; Social movement; Artificial intelligence; Computer science; Political science; Politics; World Wide Web; Law","score_opus":0.08984550964342598,"score_gpt":0.33179572754363107,"score_spread":0.2419502179002051,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412748883","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97012067,0.00040395366,0.021374729,0.0005305123,0.00005999719,0.00009647628,0.0010234204,0.0002788454,0.0061114053],"genre_scores_gemma":[0.99148625,0.0001695475,0.006631068,0.000059072474,0.000031032436,0.000030790296,0.00059530506,0.000015046474,0.0009818539],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958676,0.0001333529,0.000023274386,0.000065739674,0.00012887159,0.00006203366],"domain_scores_gemma":[0.99828017,0.0009593926,0.0002804272,0.00008978254,0.00032990382,0.000060382492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005925244,0.00048269486,0.0002503013,0.0010305621,0.00020684645,0.0008404211,0.0001742382,0.0003061272,0.0011242717],"category_scores_gemma":[0.0032010253,0.0001477818,0.00034285063,0.00064516295,0.00026452597,0.0009309935,0.00039640395,0.0005388699,0.00052123546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015217507,0.0010266607,0.3345141,0.0010795172,0.00046291467,0.0005051688,0.0031846457,0.02638346,0.10000009,0.0034379826,0.0070504956,0.52083325],"study_design_scores_gemma":[0.00002153233,0.00059067836,0.34908152,0.0000851985,0.00018798476,0.00021348057,0.0020685801,0.6125568,0.025646837,0.0036483945,0.005837787,0.000061220155],"about_ca_topic_score_codex":0.0030675468,"about_ca_topic_score_gemma":0.005021886,"teacher_disagreement_score":0.0030675468,"about_ca_system_score_codex":0.0003965028,"about_ca_system_score_gemma":0.00019308204,"threshold_uncertainty_score":0.006099403},"labels":[],"label_agreement":null},{"id":"W4412839854","doi":"10.21203/rs.3.rs-4906473/v1","title":"Deep Learning for Multimodal Sentimental Analysis Using Long-Short Term Memory","year":2025,"lang":"en","type":"preprint","venue":"Research Square","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"","keywords":"Computer science; Artificial intelligence; Term (time); Preprocessor; Deep learning; Feature (linguistics); Pattern recognition (psychology); Popularity; Feature extraction; Speech recognition; Machine learning","score_opus":0.08343846078271057,"score_gpt":0.43006827799490704,"score_spread":0.3466298172121965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412839854","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11512802,0.002259692,0.8725415,0.00096603855,0.00039772916,0.000080064616,0.0010919021,0.002874522,0.0046603912],"genre_scores_gemma":[0.8359552,0.0010606672,0.14632198,0.000360325,0.0003499928,0.00013514997,0.0023088271,0.00020844764,0.013299505],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981695,0.00003667304,0.00001247277,0.00004959298,0.000036346446,0.000047930116],"domain_scores_gemma":[0.9995542,0.00017504682,0.0000479029,0.000053591946,0.00013589617,0.000033386423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052649365,0.0008877474,0.0006038569,0.0006106421,0.00036806174,0.0007761225,0.0006675969,0.00076465623,0.0042399704],"category_scores_gemma":[0.0014860803,0.00027042784,0.0006312841,0.0007692878,0.000248948,0.0013033802,0.0008809159,0.0014429669,0.0017736288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000351915,0.00037017485,0.0026979325,0.00015229719,0.00020353257,0.00016022922,0.00012771226,0.0532287,0.041499715,0.0059922705,0.0150560355,0.88015944],"study_design_scores_gemma":[0.00000905147,0.000059473165,0.0008353487,0.000014057161,0.00003525951,0.000020572732,0.000030582967,0.9835824,0.005280193,0.008911456,0.0012125736,0.000009045913],"about_ca_topic_score_codex":0.0038950413,"about_ca_topic_score_gemma":0.0070673483,"teacher_disagreement_score":0.0042399704,"about_ca_system_score_codex":0.0005030473,"about_ca_system_score_gemma":0.00048978964,"threshold_uncertainty_score":0.014184117},"labels":[],"label_agreement":null},{"id":"W4412889553","doi":"10.18653/v1/2025.acl-short.48","title":"Dynamic Order Template Prediction for Generative Aspect-Based Sentiment Analysis","year":2025,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Chung-Ang University","keywords":"Computer science; Generative grammar; Artificial intelligence; Order (exchange); Sentiment analysis; Generative model; Natural language processing; Machine learning","score_opus":0.012374403311909872,"score_gpt":0.2874402406226955,"score_spread":0.27506583731078565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412889553","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04237309,0.00080977497,0.92993635,0.00035418782,0.0001971572,0.00031156492,0.003835881,0.018760342,0.003421723],"genre_scores_gemma":[0.41249448,0.0004645273,0.5665506,0.000326032,0.00021259072,0.00034291088,0.015064965,0.0012056404,0.0033382867],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990946,0.00014496036,0.000072223746,0.0003139938,0.0002787179,0.000095540134],"domain_scores_gemma":[0.9987471,0.00050798344,0.00010693604,0.0002410698,0.00031552388,0.00008133783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009318029,0.0015282161,0.00091738475,0.0024657757,0.00057038147,0.001274503,0.0013325029,0.0007454491,0.0039825765],"category_scores_gemma":[0.0036210695,0.0005664344,0.002234472,0.0016833752,0.00039783856,0.0019407526,0.0009808368,0.0015434633,0.0029744061],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005074759,0.0003568163,0.02225768,0.00029085777,0.00020628207,0.0005069854,0.0005520279,0.044903163,0.037926707,0.009415855,0.033335023,0.8497412],"study_design_scores_gemma":[0.000038358477,0.000054712556,0.00214717,0.000021015463,0.000058532878,0.00013580908,0.00011826447,0.96531117,0.009867383,0.015057841,0.0071635395,0.000026328242],"about_ca_topic_score_codex":0.0052526835,"about_ca_topic_score_gemma":0.0105574345,"teacher_disagreement_score":0.0052526835,"about_ca_system_score_codex":0.0007052829,"about_ca_system_score_gemma":0.0011855514,"threshold_uncertainty_score":0.013323009},"labels":[],"label_agreement":null},{"id":"W4413028731","doi":"10.21203/rs.3.rs-7021396/v1","title":"Attention Enhanced BiLSTM for Causal Sentiment Mining in Noisy Social Media Streams","year":2025,"lang":"en","type":"preprint","venue":"Research Square","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université TÉLUQ","funders":"","keywords":"STREAMS; Social media; Sentiment analysis; Data stream mining; Computer science; Data science; Data mining; Artificial intelligence; World Wide Web","score_opus":0.09414497212544871,"score_gpt":0.42274547349312425,"score_spread":0.32860050136767555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413028731","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1198381,0.0021454913,0.85650617,0.0016448617,0.0009696654,0.00015942035,0.0023375992,0.011431353,0.0049672793],"genre_scores_gemma":[0.82256615,0.0006047758,0.16249488,0.00053847354,0.0007127856,0.00016924685,0.004620142,0.0004655125,0.007828057],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994573,0.00011873828,0.000043245567,0.00016224678,0.000121479905,0.00009690859],"domain_scores_gemma":[0.9986343,0.00061116787,0.000087600405,0.00014483582,0.00044928657,0.00007283081],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015064168,0.00096633437,0.000940308,0.0012898963,0.00061948603,0.0011840335,0.001111835,0.0012650923,0.003952594],"category_scores_gemma":[0.0054129884,0.00038938536,0.0005686984,0.0011281929,0.0002953691,0.0016482178,0.0014571409,0.0016172148,0.0025443705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00075426645,0.0007453819,0.0079411585,0.00046839722,0.00022849713,0.0005062336,0.00028722157,0.05266019,0.040010042,0.008886889,0.03334944,0.8541623],"study_design_scores_gemma":[0.0000142064655,0.0000480407,0.0009294464,0.00001935072,0.000038242913,0.0000368777,0.000030203859,0.9855828,0.0034901109,0.008038409,0.0017640291,0.000008278279],"about_ca_topic_score_codex":0.0041276263,"about_ca_topic_score_gemma":0.007435735,"teacher_disagreement_score":0.0041276263,"about_ca_system_score_codex":0.00049670687,"about_ca_system_score_gemma":0.0011021239,"threshold_uncertainty_score":0.013222754},"labels":[],"label_agreement":null},{"id":"W4413036724","doi":"10.1016/j.mlwa.2025.100717","title":"Cross-domain fairness audit of sentiment label bias in foundation models: Comparing human and machine annotations on tweets and reviews","year":2025,"lang":"en","type":"article","venue":"Machine Learning with Applications","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; Vector Institute; Humber Polytechnic; Brock University","funders":"","keywords":"Foundation (evidence); Audit; Domain (mathematical analysis); Computer science; Artificial intelligence; Information retrieval; Natural language processing; Machine learning; Business; Accounting; Mathematics; Political science","score_opus":0.06347665065719738,"score_gpt":0.341585826618393,"score_spread":0.2781091759611956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413036724","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89539456,0.0011435844,0.09000143,0.0015308039,0.00044561384,0.00029316632,0.0018609557,0.0032336514,0.006096212],"genre_scores_gemma":[0.97723687,0.000080257676,0.01914684,0.0002921279,0.00007525869,0.00011287641,0.0018380235,0.00018102532,0.0010368465],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.97814107,0.015284538,0.00094821345,0.0026455747,0.002313271,0.00066728645],"domain_scores_gemma":[0.9200873,0.048329614,0.0056836517,0.015066807,0.00923343,0.0015992133],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.04237472,0.0011785245,0.0010511926,0.001360415,0.0015917593,0.0023835816,0.00110413,0.00119684,0.0011517901],"category_scores_gemma":[0.09850067,0.00039877664,0.00073234073,0.0009988914,0.0014320203,0.003137404,0.0030979733,0.0023641798,0.0010109104],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.009630711,0.0014176284,0.40842697,0.0009584943,0.0011451032,0.00046372128,0.006829441,0.0958555,0.025555776,0.009103731,0.03737356,0.40323928],"study_design_scores_gemma":[0.00039338958,0.0017123969,0.115466,0.000329455,0.00027102817,0.00047445545,0.0035832606,0.7930726,0.035296198,0.030599829,0.01858131,0.00022011624],"about_ca_topic_score_codex":0.0036854185,"about_ca_topic_score_gemma":0.0055326745,"teacher_disagreement_score":0.95762527,"about_ca_system_score_codex":0.0012206488,"about_ca_system_score_gemma":0.0018583598,"threshold_uncertainty_score":0.2241016},"labels":[],"label_agreement":null},{"id":"W4413181434","doi":"10.1109/aiiot65859.2025.11105344","title":"Sentiment Analysis of YouTube Comments on Videos about Severe Extremist Attacks","year":2025,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity Western University; Western University","funders":"","keywords":"Computer science; Sentiment analysis; Computer security; Internet privacy; World Wide Web; Artificial intelligence","score_opus":0.016606904511144004,"score_gpt":0.30024794509797686,"score_spread":0.28364104058683287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413181434","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98165625,0.0002561522,0.0010236097,0.00037666233,0.00011627741,0.00015797977,0.010408246,0.000090066875,0.005914732],"genre_scores_gemma":[0.9771723,0.00046564196,0.0032535025,0.00015597306,0.00017060088,0.00028148794,0.012889095,0.000041138184,0.0055701677],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99955565,0.000119815624,0.00004261655,0.000059602793,0.00016169227,0.00006052437],"domain_scores_gemma":[0.99732924,0.0011173418,0.00046241318,0.000053862652,0.0009373659,0.00009990064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005863466,0.0003044719,0.0002021487,0.0021706426,0.0005240064,0.0005020478,0.000116931275,0.00023482215,0.0013882727],"category_scores_gemma":[0.0040553855,0.000049896826,0.00015027681,0.0013430236,0.00020777193,0.00050139625,0.00034553788,0.00023716809,0.00049809844],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020974134,0.00029843464,0.5518571,0.002387178,0.00022906385,0.0029221745,0.027229758,0.0018821635,0.07960361,0.0013707756,0.06743297,0.26268938],"study_design_scores_gemma":[0.00001978301,0.00032148993,0.9171158,0.00027610263,0.000060804246,0.00047773749,0.02248731,0.012960648,0.00910236,0.000399146,0.036717277,0.00006152562],"about_ca_topic_score_codex":0.008264877,"about_ca_topic_score_gemma":0.020960487,"teacher_disagreement_score":0.008264877,"about_ca_system_score_codex":0.00045504078,"about_ca_system_score_gemma":0.0002955443,"threshold_uncertainty_score":0.016433537},"labels":[],"label_agreement":null},{"id":"W4413206355","doi":"10.1109/iccai66501.2025.00046","title":"Integrating Embedding Representations with Graph Convolutional Networks for Enhanced Sentiment Analysis","year":2025,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Embedding; Graph; Convolutional neural network; Artificial intelligence; Graph theory; Theoretical computer science; Mathematics; Combinatorics","score_opus":0.012259311663085725,"score_gpt":0.30870892248460857,"score_spread":0.2964496108215228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413206355","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10152537,0.00065844296,0.8885553,0.0005244043,0.00016248743,0.00008121238,0.0007031835,0.0034300725,0.0043594628],"genre_scores_gemma":[0.7922131,0.00065930886,0.19886766,0.00023833175,0.00009739647,0.00008517558,0.0021069283,0.00026384366,0.00546829],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988425,0.00002504246,0.0000066110706,0.00003172869,0.000030433264,0.000022071501],"domain_scores_gemma":[0.9997209,0.00010175851,0.000046406232,0.000032992386,0.000082009064,0.000015970898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027029717,0.0008085535,0.00030333266,0.0009903617,0.00019961204,0.0005818462,0.00044714936,0.00039132935,0.0015945807],"category_scores_gemma":[0.0012444486,0.00019274211,0.00049406156,0.0008529477,0.00026512562,0.0013585881,0.0004367816,0.0007550331,0.0007826601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022347648,0.00021687016,0.004715887,0.00024773137,0.00016968927,0.00022031793,0.00024299565,0.29321477,0.05887754,0.025683908,0.011346914,0.6048399],"study_design_scores_gemma":[0.0000034480277,0.000021671378,0.00040600487,0.000007004168,0.000014979635,0.000015515392,0.00001741753,0.98615324,0.0033838986,0.008618303,0.0013530586,0.0000054258544],"about_ca_topic_score_codex":0.006387461,"about_ca_topic_score_gemma":0.011498151,"teacher_disagreement_score":0.006387461,"about_ca_system_score_codex":0.00066509604,"about_ca_system_score_gemma":0.00037966642,"threshold_uncertainty_score":0.012700558},"labels":[],"label_agreement":null},{"id":"W4413230362","doi":"10.1142/s0219622025500798","title":"A Deep Convolutional Neural Network Model to Predict Consumer Recommendations using Online Reviews","year":2025,"lang":"en","type":"article","venue":"International Journal of Information Technology & Decision Making","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Convolutional neural network; Deep learning; Machine learning; Predictive power; Recall; Data science; Process (computing); Perception; Artificial neural network; Consumer behaviour; Psychology; Cognitive psychology","score_opus":0.03522227243974456,"score_gpt":0.36478665177866515,"score_spread":0.3295643793389206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413230362","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41785535,0.0030896359,0.5594766,0.0022210856,0.00047995147,0.00017557672,0.0026598396,0.0021907724,0.011851247],"genre_scores_gemma":[0.9450099,0.0007044506,0.042605802,0.00025802146,0.00008681826,0.00011830861,0.0015466155,0.000026985736,0.009643043],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986804,0.000021403186,0.000010512541,0.000043561682,0.000030196315,0.000026342545],"domain_scores_gemma":[0.99968565,0.00011043391,0.000030431718,0.000015225752,0.00014418483,0.00001414096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044382495,0.0006018746,0.00035008547,0.0005521654,0.00018531566,0.00046289933,0.00073757366,0.0006018121,0.0013273313],"category_scores_gemma":[0.001123878,0.00027934514,0.00044307674,0.00044835045,0.00015539596,0.00046004224,0.00026146712,0.00079341565,0.0004946108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038149135,0.00035226258,0.014868545,0.00013358767,0.00020770969,0.00018519167,0.00009385712,0.7367254,0.006794748,0.0030622426,0.007344023,0.22985098],"study_design_scores_gemma":[0.0000028139882,0.000014461656,0.0005340847,0.000003548953,0.000009957376,0.0000066869347,0.0000020393181,0.9986494,0.00034557385,0.00026143173,0.00016777455,0.0000022062695],"about_ca_topic_score_codex":0.030135896,"about_ca_topic_score_gemma":0.030162955,"teacher_disagreement_score":0.030135896,"about_ca_system_score_codex":0.0008980934,"about_ca_system_score_gemma":0.00076187414,"threshold_uncertainty_score":0.059920967},"labels":[],"label_agreement":null},{"id":"W4413387924","doi":"10.1016/j.neunet.2025.108012","title":"A novel span and syntax enhanced large language model based framework for fine-grained sentiment analysis","year":2025,"lang":"en","type":"article","venue":"Neural Networks","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Jiangsu Provincial Key Research and Development Program; National Natural Science Foundation of China; Jiangsu Commission of Health","keywords":"Computer science; Syntax; Sentiment analysis; Natural language processing; Artificial intelligence; Span (engineering)","score_opus":0.01236063928599296,"score_gpt":0.286887950066339,"score_spread":0.27452731078034603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413387924","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009854781,0.00019155719,0.97854006,0.00017798532,0.00009571208,0.00009190244,0.0009224449,0.008224863,0.0019006269],"genre_scores_gemma":[0.27544856,0.0003264265,0.70693564,0.00040154535,0.00016291327,0.0003491694,0.005511661,0.0012657038,0.0095983455],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996151,0.00006946658,0.00003084425,0.00010621399,0.00012680971,0.00005159079],"domain_scores_gemma":[0.99955946,0.00009467103,0.000044835153,0.00007345126,0.0001895407,0.000038006347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005790954,0.00087842613,0.0007420958,0.0013391561,0.00054740725,0.001145921,0.0012483073,0.0006301521,0.00552744],"category_scores_gemma":[0.0013075916,0.00035724606,0.0010497781,0.0010665104,0.00029106077,0.0023946727,0.001657493,0.001377263,0.0036093483],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004360052,0.0005145202,0.0027684367,0.00025939816,0.0002826079,0.00048838253,0.00024267852,0.07160076,0.079469785,0.032776866,0.03233921,0.7788213],"study_design_scores_gemma":[0.000012459024,0.00004111127,0.00036693955,0.00000849788,0.00003294106,0.00005831511,0.000040029616,0.9733699,0.005381039,0.016572237,0.0040985416,0.000018008928],"about_ca_topic_score_codex":0.004637444,"about_ca_topic_score_gemma":0.010751592,"teacher_disagreement_score":0.00552744,"about_ca_system_score_codex":0.00051487976,"about_ca_system_score_gemma":0.0011906566,"threshold_uncertainty_score":0.01849109},"labels":[],"label_agreement":null},{"id":"W4413472021","doi":"10.1109/access.2025.3601610","title":"Calibrating Sentiment Analysis: A Unimodal-Weighted Label Distribution Learning Approach","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Ministry of Education, Youth and Science; Federation for the Humanities and Social Sciences","keywords":"Computer science; Sentiment analysis; Artificial intelligence; Distribution (mathematics); Pattern recognition (psychology); Mathematics","score_opus":0.02469464560550674,"score_gpt":0.3125884370746556,"score_spread":0.2878937914691489,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413472021","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02596884,0.00029462768,0.97007555,0.00038721337,0.00005164861,0.0000662881,0.0001719043,0.0013294108,0.0016544651],"genre_scores_gemma":[0.6427062,0.00045723442,0.35000056,0.0007688169,0.0002738154,0.0002518004,0.0014955134,0.0004328501,0.0036131572],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982237,0.0006881045,0.0000881093,0.000497561,0.00038252628,0.00011992707],"domain_scores_gemma":[0.9964653,0.0017217596,0.0003587062,0.000578604,0.0007580212,0.00011758059],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040840646,0.0010858248,0.0008557306,0.0019319629,0.0006349407,0.0017251159,0.0016717871,0.0013644866,0.0025830397],"category_scores_gemma":[0.012441589,0.00043255414,0.00071288884,0.0012528063,0.0012199716,0.0030659868,0.0022652368,0.0026354187,0.0013722569],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039244257,0.00028198084,0.008070378,0.000265426,0.00017338025,0.00012560684,0.00047693282,0.17600697,0.019211221,0.025342893,0.010290167,0.75936264],"study_design_scores_gemma":[0.000017160131,0.00004657064,0.0006672218,0.000024983325,0.000016805512,0.000042587162,0.000055182092,0.9684345,0.0038694856,0.02500581,0.0018024192,0.000017220953],"about_ca_topic_score_codex":0.0012895653,"about_ca_topic_score_gemma":0.0016882714,"teacher_disagreement_score":0.0040840646,"about_ca_system_score_codex":0.0010381107,"about_ca_system_score_gemma":0.0008526987,"threshold_uncertainty_score":0.021598876},"labels":[],"label_agreement":null},{"id":"W4413477307","doi":"10.1007/978-3-032-02406-0_5","title":"Exploring Public Trust Through LLM-Driven Opinion Mining","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Calgary Laboratory Services; University of Calgary","funders":"","keywords":"Computer science; Public opinion; Data science; Computer security; World Wide Web; Information retrieval; Political science; Law","score_opus":0.10448317101686176,"score_gpt":0.28815018649621166,"score_spread":0.18366701547934988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413477307","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2890033,0.00071400683,0.6908849,0.0024271414,0.00025718278,0.00024792436,0.0030370837,0.00233329,0.01109514],"genre_scores_gemma":[0.911752,0.00011662328,0.08269198,0.00020859978,0.00016948694,0.0000966876,0.002523332,0.00007631453,0.0023649368],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9989177,0.00042783353,0.0000614709,0.00015857205,0.00029596325,0.00013851757],"domain_scores_gemma":[0.99367553,0.004206052,0.0005648904,0.00037393207,0.0010362118,0.00014327318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016910052,0.0004597393,0.000616042,0.0013805671,0.00045007444,0.0016260165,0.0010117231,0.0008168432,0.0028152487],"category_scores_gemma":[0.012559447,0.00024168793,0.000642084,0.0011350623,0.00027875663,0.0022865138,0.00104616,0.0012752643,0.0019312229],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011202692,0.00094247947,0.062336747,0.0005251009,0.0004085167,0.0005382161,0.0010466429,0.07276718,0.020662969,0.029239176,0.043972,0.7664407],"study_design_scores_gemma":[0.000014909192,0.000045748737,0.002937623,0.000019411733,0.00002670374,0.000037780108,0.00017605654,0.9801192,0.0023189904,0.012394913,0.0018977404,0.000010980963],"about_ca_topic_score_codex":0.0034782605,"about_ca_topic_score_gemma":0.004901669,"teacher_disagreement_score":0.0034782605,"about_ca_system_score_codex":0.0008374442,"about_ca_system_score_gemma":0.00050624926,"threshold_uncertainty_score":0.009417951},"labels":[],"label_agreement":null},{"id":"W4414061363","doi":"10.64803/jocsaic.v1i2.18","title":"Sentiment Analysis of Social Media Towards Public Services Using Naive Bayes and Text Mining","year":2024,"lang":"en","type":"article","venue":"Journal of Computer Science Artificial Intelligence and Communications","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Naive Bayes classifier; Social media; Sentiment analysis; Classifier (UML); Public service; Preprocessor; Data pre-processing","score_opus":0.11750840685754152,"score_gpt":0.364188791714649,"score_spread":0.2466803848571075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414061363","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.65188444,0.0007898128,0.33260855,0.0010248721,0.00037495574,0.0012637704,0.0019440682,0.00079960085,0.00930986],"genre_scores_gemma":[0.84961426,0.00039758382,0.14598261,0.0001463372,0.00014920722,0.00035311925,0.0015571581,0.000027166978,0.0017724992],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974705,0.0008191812,0.00040229844,0.000323819,0.0008416835,0.00014267664],"domain_scores_gemma":[0.99578696,0.0022501317,0.00043695778,0.0001188364,0.0013467054,0.000060384962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031159355,0.00069427077,0.000793133,0.0025261585,0.0007493579,0.0015706418,0.0004884481,0.00053372514,0.0012848178],"category_scores_gemma":[0.0077178073,0.00024096729,0.0010274677,0.001675368,0.0003364158,0.0010717915,0.0003314034,0.00060623285,0.0006665628],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013465447,0.0012229274,0.11133456,0.0010033962,0.0005961681,0.0008220264,0.0026661637,0.048322096,0.039697453,0.0062931073,0.009704899,0.7769906],"study_design_scores_gemma":[0.000063068735,0.00033746756,0.04272455,0.00015053556,0.00020134433,0.00028753912,0.0017229837,0.9301563,0.012356465,0.006665301,0.005261521,0.00007287801],"about_ca_topic_score_codex":0.004432995,"about_ca_topic_score_gemma":0.004313928,"teacher_disagreement_score":0.004432995,"about_ca_system_score_codex":0.0008578684,"about_ca_system_score_gemma":0.0007981798,"threshold_uncertainty_score":0.016478837},"labels":[],"label_agreement":null},{"id":"W4414187888","doi":"10.1016/j.ins.2025.122684","title":"SEAD-MGFE-Net: Schrödinger equation-based adaptive dropout multi-granular feature enhancement network for conversational aspect-based sentiment quadruple analysis","year":2025,"lang":"en","type":"article","venue":"Information Sciences","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; McGill University","funders":"Science and Technology Service Network Plan; National Key Research and Development Program of China; Sichuan Province Science and Technology Support Program; Chengdu Science and Technology Bureau; Organization Department of Sichuan Provincial Party Committee; Ministry of Science and Technology of the People's Republic of China; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Dropout (neural networks); Sentiment analysis; Feature (linguistics); Benchmark (surveying); Regularization (linguistics); Adjacency list; Feature extraction; Syntax; Sentence","score_opus":0.033696778352127754,"score_gpt":0.3038836580473439,"score_spread":0.27018687969521615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414187888","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010498974,0.00010797951,0.9843423,0.00021900568,0.00008188812,0.000055988054,0.00034326816,0.001276009,0.0030747142],"genre_scores_gemma":[0.43927017,0.00031629,0.5405845,0.0005411309,0.000112545145,0.0005707877,0.0019882382,0.0007896011,0.015826708],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986124,0.00004360569,0.0000067139113,0.000028496444,0.00004101735,0.000018920116],"domain_scores_gemma":[0.9996233,0.0002014497,0.000020655298,0.00005011774,0.00007019283,0.000034312794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053285086,0.00043748325,0.0007170427,0.00043681217,0.0005630189,0.00057861436,0.0017145028,0.0011888603,0.00738585],"category_scores_gemma":[0.0017408578,0.00031067623,0.0006043133,0.00041369317,0.00038904045,0.0012511029,0.0011682571,0.0012935979,0.0020171632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034794022,0.00022149672,0.0015784319,0.00029804363,0.00013412436,0.00038975693,0.00025009303,0.625366,0.015601437,0.12044387,0.02184057,0.2135282],"study_design_scores_gemma":[0.0000042832858,0.000006390676,0.000037620997,0.0000028817756,0.0000023960786,0.000007104853,0.0000044441485,0.9912533,0.0004351078,0.007685509,0.00055762904,0.0000033208282],"about_ca_topic_score_codex":0.004025704,"about_ca_topic_score_gemma":0.0073240367,"teacher_disagreement_score":0.00738585,"about_ca_system_score_codex":0.0005864967,"about_ca_system_score_gemma":0.00070735003,"threshold_uncertainty_score":0.024708152},"labels":[],"label_agreement":null},{"id":"W4414190468","doi":"10.18280/isi.300720","title":"MCWA-LSTM with SELU for Text-Based Emotion Classification","year":2025,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Categorization; Focus (optics); Emotion classification; Binary classification; Cognition; Encoder; Sentiment analysis; Emotion detection; Context (archaeology)","score_opus":0.01869121626121616,"score_gpt":0.2537283706667935,"score_spread":0.23503715440557732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414190468","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13759424,0.004229812,0.82975674,0.0013182366,0.00088520197,0.00024743032,0.0014381845,0.015700145,0.008829976],"genre_scores_gemma":[0.7829644,0.0011148253,0.1938202,0.0009583048,0.00023387058,0.00046575567,0.0027265372,0.0005075373,0.017208582],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997621,0.00003638156,0.00001885058,0.0000967968,0.00004400104,0.00004192071],"domain_scores_gemma":[0.9997392,0.00009568916,0.000025415626,0.00002970059,0.00009553807,0.000014454964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035869388,0.001282761,0.00051423995,0.0005401308,0.00032907643,0.00058399217,0.0012092469,0.0008501634,0.003780706],"category_scores_gemma":[0.001224859,0.00029300246,0.0006121633,0.00056530425,0.00030395883,0.0014669579,0.0007676321,0.0016298845,0.0018954771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035484246,0.00027349102,0.0013962096,0.00026217432,0.0001360062,0.00023590613,0.00024872733,0.07035852,0.056616824,0.0027540245,0.015714128,0.8516492],"study_design_scores_gemma":[0.000016330247,0.00011993922,0.0006328128,0.000025621623,0.000046718214,0.00005210864,0.00007013188,0.97427565,0.018258426,0.0035653017,0.002919524,0.000017452727],"about_ca_topic_score_codex":0.0037041493,"about_ca_topic_score_gemma":0.007758418,"teacher_disagreement_score":0.003780706,"about_ca_system_score_codex":0.0006070252,"about_ca_system_score_gemma":0.00066666707,"threshold_uncertainty_score":0.012647688},"labels":[],"label_agreement":null},{"id":"W4414221134","doi":"10.1016/j.im.2025.104252","title":"Diving into recession: the collective knowledge of online users as an early warning system for recessionary expectations","year":2025,"lang":"en","type":"article","venue":"Information & Management","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Zayed University","keywords":"Warning system; Early warning system; Information system; Information technology; The Internet","score_opus":0.015077592782346131,"score_gpt":0.306883555665192,"score_spread":0.2918059628828459,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414221134","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9701737,0.00043004635,0.0079569435,0.0034859644,0.00019344724,0.00009963262,0.0009581906,0.00025869717,0.016443329],"genre_scores_gemma":[0.9938805,0.00017001804,0.003163622,0.0002277748,0.00013725087,0.000026285525,0.00054337666,0.00001872189,0.001832494],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9994473,0.0002436271,0.00003823273,0.000071793715,0.00013821719,0.00006084634],"domain_scores_gemma":[0.99504423,0.0026301248,0.00090795924,0.00032917454,0.0007481045,0.00034047384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016546644,0.00024333701,0.00026382777,0.0011930475,0.00055011694,0.001846155,0.00035101504,0.0008343143,0.0025009946],"category_scores_gemma":[0.008364159,0.00011654367,0.00022114064,0.0008106727,0.000272415,0.0028374942,0.0010397339,0.0007845792,0.0007472368],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001528591,0.0009491998,0.44570255,0.00037964855,0.0002826544,0.0007454583,0.012494962,0.0036769528,0.018191809,0.0045276447,0.037270814,0.47424978],"study_design_scores_gemma":[0.00012052128,0.0013665131,0.66606766,0.00037915097,0.0006315927,0.00050221034,0.03194254,0.22154877,0.010751483,0.020920519,0.045553964,0.00021503669],"about_ca_topic_score_codex":0.0034427405,"about_ca_topic_score_gemma":0.00559508,"teacher_disagreement_score":0.0034427405,"about_ca_system_score_codex":0.00031980735,"about_ca_system_score_gemma":0.00048999046,"threshold_uncertainty_score":0.008750856},"labels":[],"label_agreement":null},{"id":"W4414331349","doi":"10.14419/zxxek146","title":"Optimized Random Forest Classifier for Predicting Ideal Candidate for The General Election","year":2025,"lang":"en","type":"article","venue":"International Journal of Basic and Applied Sciences","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Random forest; Ideal point; Hyperparameter; Classifier (UML); Popularity; Ideal (ethics); Naive Bayes classifier; Bayesian probability; Mean squared error","score_opus":0.020041225604926362,"score_gpt":0.3072528200951429,"score_spread":0.2872115944902165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414331349","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22080037,0.0039262786,0.7594567,0.0010813945,0.00055227114,0.0004472253,0.0032845354,0.0034272843,0.007023956],"genre_scores_gemma":[0.8070063,0.0011914119,0.1754017,0.00036647933,0.00037422674,0.00041999033,0.009138606,0.00018253947,0.0059186686],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985978,0.0002927438,0.000110152054,0.00038118448,0.00032767278,0.00029041883],"domain_scores_gemma":[0.9985815,0.0006360055,0.00013167954,0.00009567931,0.0004983145,0.00005682305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026458006,0.0013051603,0.0014809846,0.002430432,0.0008391956,0.0010835784,0.0011684045,0.0014593829,0.0019905062],"category_scores_gemma":[0.004252384,0.00027131778,0.0015053772,0.001668007,0.00026779133,0.001165791,0.00039750838,0.001242147,0.0015347565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073435745,0.00058330584,0.039162472,0.0002661764,0.00025913556,0.00036845275,0.000119089804,0.32405165,0.005348478,0.0037264186,0.025430832,0.59994966],"study_design_scores_gemma":[0.000024770508,0.000093374925,0.0035775895,0.000030135,0.000058139212,0.00010957919,0.00003418004,0.9913735,0.0010379895,0.0019817776,0.0016594598,0.000019552412],"about_ca_topic_score_codex":0.015019086,"about_ca_topic_score_gemma":0.01469904,"teacher_disagreement_score":0.015019086,"about_ca_system_score_codex":0.00075040624,"about_ca_system_score_gemma":0.0017947245,"threshold_uncertainty_score":0.029863358},"labels":[],"label_agreement":null},{"id":"W4414359725","doi":"10.24963/ijcai.2025/1271","title":"SandboxSocial: A Sandbox for Social Media Using Multimodal AI Agents","year":2025,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Impact; Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Social media; Sandbox (software development); Bridge (graph theory); Key (lock); Grounded theory; Upload; Social dynamics","score_opus":0.06472675789559049,"score_gpt":0.36601121462093056,"score_spread":0.3012844567253401,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414359725","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18528631,0.00060855766,0.6736946,0.0019025656,0.0006909034,0.0019367182,0.006410763,0.08841166,0.04105787],"genre_scores_gemma":[0.6982268,0.0004994326,0.2747531,0.00061708054,0.00008361908,0.0020368893,0.0048794867,0.004295657,0.014607927],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995283,0.00018608595,0.000036705835,0.000089874484,0.000109694745,0.000049326725],"domain_scores_gemma":[0.9979119,0.0010864006,0.00010288036,0.00044328225,0.00020133778,0.0002542043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011330086,0.00093039544,0.00053357583,0.0006522708,0.0009186587,0.0015722518,0.0022272682,0.0014704809,0.0126177175],"category_scores_gemma":[0.0058430177,0.000522487,0.0008657529,0.00036312192,0.00083543925,0.0024036453,0.002683828,0.0013955554,0.001809714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017978472,0.0012252952,0.020797614,0.0011582677,0.0006037563,0.0010891103,0.0022500325,0.65458214,0.01983153,0.11040179,0.06518477,0.12107792],"study_design_scores_gemma":[0.00012870254,0.00009471322,0.00056160736,0.00003277409,0.000029361194,0.000060973205,0.000091415415,0.95506084,0.0031428686,0.016885167,0.02387892,0.000032668086],"about_ca_topic_score_codex":0.0054707793,"about_ca_topic_score_gemma":0.007209372,"teacher_disagreement_score":0.0126177175,"about_ca_system_score_codex":0.00077645463,"about_ca_system_score_gemma":0.0012770011,"threshold_uncertainty_score":0.04221046},"labels":[],"label_agreement":null},{"id":"W4414413122","doi":"10.1007/s00146-025-02600-7","title":"Conflicting feelings: sociological and computational sentiment in workplace sentiment surveillance","year":2025,"lang":"en","type":"article","venue":"AI & Society","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Andrew W. Mellon Foundation","keywords":"Sentiment analysis; Operationalization; Context (archaeology); Rhetoric; Conversation; Software; Rhetorical question","score_opus":0.014483925688633815,"score_gpt":0.31205319839743006,"score_spread":0.29756927270879624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414413122","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8609533,0.00077462394,0.1160916,0.0025527463,0.00016697541,0.00008430223,0.00050613214,0.0001219972,0.018748337],"genre_scores_gemma":[0.9937835,0.00007837391,0.005461192,0.00006536693,0.000043402757,0.000015645315,0.00009595111,0.000008148279,0.0004484917],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9989197,0.0005934462,0.000062206534,0.00015356396,0.00018877577,0.000082386076],"domain_scores_gemma":[0.99459547,0.0037883627,0.0006624665,0.00018183324,0.0005725968,0.00019936316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021518378,0.00027455387,0.00029946928,0.000873617,0.00074324233,0.002600165,0.00044115787,0.0007327921,0.0018318502],"category_scores_gemma":[0.014099952,0.00019775194,0.0002772784,0.0006597548,0.00067057187,0.0027466477,0.0009026822,0.00097292854,0.0002756187],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013975472,0.0008613246,0.34472054,0.00052960316,0.0003267581,0.00094957964,0.011961104,0.034762114,0.02548796,0.07716561,0.013906266,0.48793155],"study_design_scores_gemma":[0.000032745964,0.0002172498,0.16444598,0.00013928446,0.00014722458,0.00034255625,0.007931525,0.7340736,0.0042777904,0.08136677,0.0069430326,0.00008231761],"about_ca_topic_score_codex":0.002053218,"about_ca_topic_score_gemma":0.0028009736,"teacher_disagreement_score":0.002600165,"about_ca_system_score_codex":0.00052073237,"about_ca_system_score_gemma":0.0003513614,"threshold_uncertainty_score":0.011380136},"labels":[],"label_agreement":null},{"id":"W4414462934","doi":"10.1109/conit65521.2025.11167613","title":"Social Media Sentiments Analysis using Convolutional Neural Network and Support Vector Machine","year":2025,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Support vector machine; Convolutional neural network; Feature extraction; Preprocessor; Pattern recognition (psychology); Feature (linguistics); Set (abstract data type); Mel-frequency cepstrum; Noise (video)","score_opus":0.027415183564066508,"score_gpt":0.29879214503488805,"score_spread":0.2713769614708215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414462934","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47292063,0.0011039734,0.5159967,0.0006185408,0.0002278078,0.00023581368,0.0010167739,0.0021406757,0.005739018],"genre_scores_gemma":[0.9338609,0.00035687687,0.06216787,0.0000727469,0.000068866575,0.00009324733,0.00092774414,0.00003072227,0.0024210033],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9997167,0.000055987526,0.000023793405,0.000060659353,0.00008968719,0.000053063668],"domain_scores_gemma":[0.9997008,0.000078315185,0.000051916883,0.000019610696,0.00013341251,0.000015978147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005609128,0.0007908168,0.00038697573,0.0012810706,0.00021196943,0.0005783982,0.0003955571,0.00039778015,0.0010132886],"category_scores_gemma":[0.0011608917,0.00016628072,0.0004989055,0.00063381006,0.00015079917,0.00067109586,0.00036708245,0.00048869394,0.0004238308],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004872461,0.00042039144,0.02640574,0.00017642562,0.00028742425,0.00032560612,0.00021829871,0.088473864,0.04302975,0.0028857437,0.0057564257,0.83153313],"study_design_scores_gemma":[0.0000045226593,0.00006005437,0.005697477,0.000007792954,0.000021390371,0.000024871073,0.000048979113,0.98782533,0.004759499,0.00078285014,0.0007581626,0.0000090299645],"about_ca_topic_score_codex":0.0037431791,"about_ca_topic_score_gemma":0.0045010517,"teacher_disagreement_score":0.0037431791,"about_ca_system_score_codex":0.0005223565,"about_ca_system_score_gemma":0.0002742794,"threshold_uncertainty_score":0.0074427724},"labels":[],"label_agreement":null},{"id":"W4414657843","doi":"10.1002/mar.70058","title":"Stories of Service Slip‐Ups: Judgments of Pre‐Service Deservingness Shape Reactions to Service Failures","year":2025,"lang":"en","type":"article","venue":"Psychology and Marketing","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Blame; Service (business); Perception; Skepticism; Economic Justice; Service provider; Attribution","score_opus":0.030044659729010985,"score_gpt":0.34077039928386027,"score_spread":0.3107257395548493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414657843","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9945522,0.000072758885,0.0005940432,0.00039866925,0.000020375603,0.000014504544,0.000047153335,0.000011240485,0.0042891814],"genre_scores_gemma":[0.99912554,0.000051858067,0.00018474447,0.00014679751,0.000010470679,0.000011320596,0.000032336036,0.000011669728,0.00042515702],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985671,0.0008602773,0.000043284093,0.00010776837,0.00030481373,0.00011667974],"domain_scores_gemma":[0.9820409,0.01045877,0.004825486,0.0006465911,0.0010834943,0.000944862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020983717,0.00026543977,0.00022080143,0.00042296405,0.00078660913,0.002515198,0.00024489642,0.00090599194,0.004463381],"category_scores_gemma":[0.022732286,0.0002219611,0.00022659785,0.0002699407,0.0013090557,0.001683441,0.0013988281,0.0015242964,0.000495441],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0037301502,0.0006316171,0.54240876,0.0005650376,0.0002457816,0.0019270574,0.3418372,0.00080028217,0.031680252,0.0059248484,0.007713572,0.062535405],"study_design_scores_gemma":[0.000043906635,0.00085461326,0.7648208,0.00018103157,0.00013342894,0.00060584024,0.20760112,0.0031572557,0.0057857474,0.0055595213,0.011113641,0.0001431212],"about_ca_topic_score_codex":0.0014148159,"about_ca_topic_score_gemma":0.0018530991,"teacher_disagreement_score":0.004463381,"about_ca_system_score_codex":0.000757031,"about_ca_system_score_gemma":0.00023942259,"threshold_uncertainty_score":0.0149315},"labels":[],"label_agreement":null},{"id":"W4414681521","doi":"10.1016/j.ins.2025.122704","title":"3WD-DRT: A three-way decision enhanced dynamic routing transformer for cost-sensitive multimodal sentiment analysis","year":2025,"lang":"en","type":"article","venue":"Information Sciences","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Université de Montréal","funders":"National Key Research and Development Program of China; Sichuan Province Science and Technology Support Program; National Natural Science Foundation of China; Department of Science and Technology of Sichuan Province; Organization Department of Sichuan Provincial Party Committee; Ministry of Science and Technology of the People's Republic of China; Chinese Academy of Sciences","keywords":"Sentiment analysis; Transformer; Key (lock); Partition (number theory); Routing (electronic design automation); Information fusion","score_opus":0.015098924396555299,"score_gpt":0.32011335375433064,"score_spread":0.30501442935777534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414681521","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009317933,0.00018918814,0.98195136,0.00014721305,0.0001727452,0.00012209955,0.00044891302,0.0045456025,0.0031049454],"genre_scores_gemma":[0.37163806,0.00037057363,0.61287755,0.0005085271,0.00014530907,0.00027151348,0.0020760908,0.0009093275,0.01120303],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951696,0.000102326834,0.00003428729,0.00009948702,0.00018309984,0.000063717736],"domain_scores_gemma":[0.9995701,0.0001305876,0.000028846973,0.00006850205,0.00017102671,0.000030969833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070855033,0.0007946001,0.00096367445,0.0010193266,0.00046394803,0.0011615416,0.0013772866,0.0007264965,0.00922992],"category_scores_gemma":[0.0018410962,0.00035033238,0.0008432801,0.00084537076,0.00028803851,0.0016357375,0.0011411399,0.0010632464,0.002635217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005853494,0.00027933542,0.0008551948,0.00023838635,0.00011781739,0.0002016938,0.000106933716,0.09424875,0.03909133,0.019139703,0.026881034,0.81825453],"study_design_scores_gemma":[0.00002595094,0.00007853862,0.00019163081,0.000010097286,0.000028647102,0.00007904326,0.000032580698,0.977089,0.008342457,0.008175531,0.0059256423,0.000020910195],"about_ca_topic_score_codex":0.0042596487,"about_ca_topic_score_gemma":0.0061701233,"teacher_disagreement_score":0.00922992,"about_ca_system_score_codex":0.0007848907,"about_ca_system_score_gemma":0.001067167,"threshold_uncertainty_score":0.030877173},"labels":[],"label_agreement":null},{"id":"W4414725767","doi":"10.1007/978-3-031-95111-4_5","title":"Can Social Media Data Be Useful for Assessing Road Safety? An Investigation Using X/Twitter","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Collision; Social media; Perception; Big data; Road traffic safety; Poison control","score_opus":0.07596187725493887,"score_gpt":0.29567868707459044,"score_spread":0.21971680981965158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414725767","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9535231,0.0006127471,0.003938047,0.0029561548,0.00014226852,0.00013047813,0.0026275427,0.00011472629,0.03595484],"genre_scores_gemma":[0.98920804,0.0007011233,0.0030641046,0.00019639549,0.00012842826,0.00006108799,0.0010804647,0.000023701896,0.0055366578],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99901414,0.00035482238,0.00006552855,0.00008586312,0.00038731727,0.00009242726],"domain_scores_gemma":[0.99334246,0.003951815,0.000871409,0.00017760618,0.0015079404,0.00014876336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017286885,0.00035197742,0.00016455643,0.00234653,0.00064667827,0.0021320353,0.0004158611,0.00064453675,0.0028224974],"category_scores_gemma":[0.0066538677,0.00015334612,0.0003106804,0.0028186364,0.0003592363,0.0035563158,0.000668797,0.0005183657,0.0015499959],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063472666,0.00060694583,0.680918,0.0006609552,0.00015687082,0.0011489394,0.005936829,0.0012639135,0.011928856,0.006310067,0.015476926,0.27495703],"study_design_scores_gemma":[0.000035996072,0.001055996,0.8111455,0.0005693937,0.0003482435,0.0012344338,0.061366998,0.046100948,0.015531955,0.00499452,0.057513885,0.00010217934],"about_ca_topic_score_codex":0.005179821,"about_ca_topic_score_gemma":0.0071061514,"teacher_disagreement_score":0.005179821,"about_ca_system_score_codex":0.00039260782,"about_ca_system_score_gemma":0.0003774067,"threshold_uncertainty_score":0.010299325},"labels":[],"label_agreement":null},{"id":"W4414760568","doi":"10.20429/amtp.2025.44","title":"Exploring the Drivers of Customers’ Willingness to Accept Chatbot Intervention in Service Recovery","year":2025,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Chatbot; Service (business); Relevance (law); Service recovery; Service provider","score_opus":0.060603093856364185,"score_gpt":0.28975906899410714,"score_spread":0.22915597513774294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414760568","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99668425,0.00005293586,0.00074321433,0.00036705125,0.00000593997,0.000031604806,0.00001526243,0.000011484641,0.0020882601],"genre_scores_gemma":[0.9993445,0.000034250923,0.00022547197,0.0000664972,0.0000044739218,0.000017577173,0.000016316151,0.000005042442,0.00028563474],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9965971,0.0016750746,0.00016807273,0.00020806747,0.00083503017,0.0005166048],"domain_scores_gemma":[0.96713144,0.020751847,0.005740485,0.0006985504,0.0038069056,0.0018707652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004943499,0.0002401597,0.00029154122,0.00047516535,0.0007545423,0.0025221547,0.000535477,0.00085060945,0.0047572516],"category_scores_gemma":[0.03606712,0.00017194872,0.00040482712,0.00042662836,0.00085008115,0.001736154,0.0012186122,0.0017513994,0.0005358993],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009854201,0.0016911183,0.8298961,0.00055207993,0.00021916536,0.000939709,0.066639006,0.0010905142,0.005242726,0.0027533572,0.0014040241,0.08858672],"study_design_scores_gemma":[0.000034869256,0.0007494573,0.88742113,0.0001495778,0.0001491267,0.00030649078,0.096888624,0.008316319,0.0013964627,0.0013861015,0.0031278534,0.00007395076],"about_ca_topic_score_codex":0.0035033906,"about_ca_topic_score_gemma":0.0037799142,"teacher_disagreement_score":0.004943499,"about_ca_system_score_codex":0.0009054254,"about_ca_system_score_gemma":0.0011903942,"threshold_uncertainty_score":0.026144087},"labels":[],"label_agreement":null},{"id":"W4415047052","doi":"10.1145/3767746","title":"Frequency Restoration and Modality Enforcement towards Resisting-corruption Multimodal Sentiment Analysis","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Modality (human–computer interaction); Modal; Robustness (evolution); Discriminative model; Leverage (statistics); Sentiment analysis; Key (lock); Semantics (computer science)","score_opus":0.02908402523713622,"score_gpt":0.32467602416157515,"score_spread":0.29559199892443894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415047052","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.095140845,0.0014573261,0.89390516,0.0005210977,0.00023258307,0.00019847286,0.00030914118,0.0033505028,0.0048849117],"genre_scores_gemma":[0.60263973,0.0012778495,0.3871703,0.0006574021,0.00045228712,0.00022045261,0.0012983076,0.00046434737,0.005819213],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920636,0.00020948605,0.00004154346,0.00021610019,0.00021909732,0.000107504464],"domain_scores_gemma":[0.99914324,0.00022193101,0.00009954262,0.00019589378,0.00028471922,0.000054655295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015427793,0.001192183,0.0008070809,0.0013889954,0.0004270558,0.0008434541,0.0008175931,0.0009121582,0.0025075972],"category_scores_gemma":[0.004079275,0.0002337292,0.0010492721,0.00055261585,0.000673134,0.0014724475,0.0014689595,0.0013429801,0.0016110351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059053145,0.00017620085,0.0022413747,0.00028429253,0.00012974208,0.00019014336,0.00028596687,0.02166339,0.1529411,0.0042628124,0.0067015886,0.8105329],"study_design_scores_gemma":[0.00011281274,0.00053626206,0.007741747,0.000116213574,0.00025097275,0.00064869004,0.00048862596,0.8387068,0.113778606,0.015442647,0.022068536,0.00010805543],"about_ca_topic_score_codex":0.0013894585,"about_ca_topic_score_gemma":0.0016834361,"teacher_disagreement_score":0.0025075972,"about_ca_system_score_codex":0.00028332122,"about_ca_system_score_gemma":0.00045890416,"threshold_uncertainty_score":0.008388758},"labels":[],"label_agreement":null},{"id":"W4415360279","doi":"10.59934/jaiea.v5i1.1611","title":"Public Sentiment Analysis on Facebook Posts About Shin Tae-Yong's Dismissal Using the K-Nearest Neighbors Algorithm","year":2025,"lang":"","type":"article","venue":"Journal of Artificial Intelligence and Engineering Applications (JAIEA)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Sentiment analysis; Dismissal; Social media; Public opinion; Preprocessor; Weighting; Microblogging","score_opus":0.051842958323866,"score_gpt":0.30598456333173285,"score_spread":0.25414160500786687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415360279","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9752367,0.00016150501,0.018172344,0.0002053622,0.000087267705,0.00014834278,0.0013199121,0.00018926506,0.004479214],"genre_scores_gemma":[0.985252,0.00011193929,0.011834755,0.000018079838,0.00003336128,0.00006698424,0.0012320759,0.000010258113,0.0014405902],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9994893,0.000120052435,0.000070617876,0.00008177376,0.00018863428,0.000049623606],"domain_scores_gemma":[0.99891496,0.00041932854,0.00016226177,0.00004693845,0.00042231355,0.000034244233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075303524,0.00028062644,0.00038315152,0.0013485074,0.00041238967,0.0005854777,0.0001769043,0.00025067554,0.0010865818],"category_scores_gemma":[0.0025104366,0.00008355984,0.00042402398,0.00086204085,0.00016112183,0.00039809544,0.00023750933,0.00024781981,0.0006968726],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017254336,0.00059318286,0.30714715,0.0006739069,0.0003467328,0.0010328308,0.0037541261,0.022950493,0.032395095,0.0016626772,0.017400902,0.6103174],"study_design_scores_gemma":[0.000039054343,0.00036824695,0.32137248,0.00011745911,0.00014297191,0.00043514554,0.0054822187,0.6471497,0.014117789,0.0015247692,0.009174009,0.00007608505],"about_ca_topic_score_codex":0.004364244,"about_ca_topic_score_gemma":0.006349981,"teacher_disagreement_score":0.004364244,"about_ca_system_score_codex":0.0004235073,"about_ca_system_score_gemma":0.0002777708,"threshold_uncertainty_score":0.008677721},"labels":[],"label_agreement":null},{"id":"W4415360535","doi":"10.59934/jaiea.v5i1.1643","title":"Application of Sentiment Analysis to Crime News using Tf-Idf and K-Nearest Neighbor to Assess Public Perception","year":2025,"lang":"","type":"article","venue":"Journal of Artificial Intelligence and Engineering Applications (JAIEA)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Sentiment analysis; Public opinion; Perception; Social media; Recall; Preprocessor; Term (time); Crime scene; Feature (linguistics)","score_opus":0.06893804411035655,"score_gpt":0.3403690342731577,"score_spread":0.27143099016280114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415360535","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8328974,0.00073646236,0.15602043,0.00032458612,0.00025075662,0.0005398773,0.001656613,0.0007025504,0.006871217],"genre_scores_gemma":[0.92901254,0.0002916691,0.06834426,0.000037175683,0.000079122205,0.00017959722,0.001083242,0.000021504475,0.0009509821],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9987465,0.00031479006,0.00016111454,0.00017646018,0.00050713023,0.00009408984],"domain_scores_gemma":[0.9965629,0.0011743308,0.000419176,0.000115376846,0.0016523716,0.0000758398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023258177,0.00058229786,0.00068902096,0.004006663,0.00045918833,0.00089587976,0.00026892606,0.00047493936,0.0006803919],"category_scores_gemma":[0.0054712305,0.00012831984,0.00065796706,0.0019154387,0.00021281489,0.00081783533,0.00035441827,0.0003510403,0.0004533849],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00087800913,0.0007245317,0.18737815,0.00074364705,0.00044170464,0.00054460135,0.002479477,0.015293148,0.062004875,0.0014524128,0.0065581882,0.72150135],"study_design_scores_gemma":[0.00004921,0.0007560715,0.18813466,0.00012140721,0.0002420809,0.0006735624,0.003014227,0.76776713,0.031073226,0.0021883966,0.005841326,0.00013873828],"about_ca_topic_score_codex":0.0054986407,"about_ca_topic_score_gemma":0.004165965,"teacher_disagreement_score":0.0054986407,"about_ca_system_score_codex":0.00067188556,"about_ca_system_score_gemma":0.0004424373,"threshold_uncertainty_score":0.012300253},"labels":[],"label_agreement":null},{"id":"W4415806375","doi":"10.61882/jhrd.3.3.22","title":"Sentiment Analysis of Twitter Users during the 12-Day Iran-Israel War: A Psychological Approach","year":2025,"lang":"fa","type":"article","venue":"Journal of Health Research and Development","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Sentiment analysis; Social media; The Internet","score_opus":0.2527591382581591,"score_gpt":0.45215656161789897,"score_spread":0.19939742335973987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415806375","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99753594,0.00003488341,0.00033541396,0.00013954028,0.000015639913,0.000034140292,0.00029600208,0.0000056916497,0.0016026791],"genre_scores_gemma":[0.9984555,0.00006507316,0.0005085391,0.00005832175,0.00003019835,0.00005269871,0.00028036468,0.000003451443,0.0005458779],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994998,0.0001831704,0.000043402266,0.000054252992,0.00015043309,0.00006891317],"domain_scores_gemma":[0.99806935,0.0007008879,0.0006422163,0.000054082735,0.00042784782,0.00010556208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008626645,0.00018026933,0.00022308005,0.001046479,0.0005794336,0.00097225764,0.00013411121,0.0002522656,0.0012534568],"category_scores_gemma":[0.0032924553,0.000087680746,0.0002732312,0.00078694045,0.00032435174,0.0006106898,0.00054284686,0.00035385363,0.00032319082],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070609787,0.00017795853,0.89880717,0.0002820116,0.00010666148,0.00035911516,0.032813985,0.00035881408,0.010809337,0.0005873956,0.0031363533,0.051855028],"study_design_scores_gemma":[0.000007325922,0.00017946868,0.95363855,0.00003973211,0.00004165144,0.00014398934,0.039137542,0.002624277,0.0014629303,0.00027804024,0.0024194284,0.000027160242],"about_ca_topic_score_codex":0.001479161,"about_ca_topic_score_gemma":0.0025484376,"teacher_disagreement_score":0.001479161,"about_ca_system_score_codex":0.00040703805,"about_ca_system_score_gemma":0.00017881078,"threshold_uncertainty_score":0.004562199},"labels":[],"label_agreement":null},{"id":"W4416036533","doi":"10.18653/v1/2025.emnlp-main.493","title":"CR4-NarrEmote: An Open Vocabulary Dataset of Narrative Emotions Derived Using Citizen Science","year":2025,"lang":"","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Citizen science; Narrative; Vocabulary; Discourse analysis","score_opus":0.07445549163604204,"score_gpt":0.39211974347038236,"score_spread":0.3176642518343403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416036533","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15928532,0.0028435914,0.024942925,0.0019011754,0.000950983,0.0011730386,0.7553435,0.007867842,0.045691665],"genre_scores_gemma":[0.12077956,0.0006507381,0.03489944,0.00065307104,0.00024368806,0.0019278296,0.8288675,0.0007895042,0.011188675],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989247,0.0003727232,0.00012797095,0.0002453033,0.00023448985,0.000094778516],"domain_scores_gemma":[0.99725324,0.0011644919,0.00033835554,0.00047192493,0.000523541,0.00024851412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009534686,0.0009775637,0.00038492822,0.0028353757,0.0010179126,0.0014133912,0.001035652,0.0014948595,0.006306447],"category_scores_gemma":[0.0073327017,0.00021233899,0.00060192053,0.0018944065,0.0006260313,0.002056936,0.0025362293,0.0011836732,0.006052087],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011497381,0.00057027815,0.03289122,0.005743162,0.00019277465,0.0017848229,0.0134699745,0.0033465289,0.030820725,0.011013843,0.7477492,0.15126766],"study_design_scores_gemma":[0.00012711393,0.0001849668,0.05192629,0.0007045596,0.00007207221,0.0008039531,0.008558803,0.009424867,0.0084348675,0.006310734,0.91328794,0.00016394698],"about_ca_topic_score_codex":0.00517458,"about_ca_topic_score_gemma":0.01726523,"teacher_disagreement_score":0.006306447,"about_ca_system_score_codex":0.0008477475,"about_ca_system_score_gemma":0.00068314123,"threshold_uncertainty_score":0.021097124},"labels":[],"label_agreement":null},{"id":"W4416142091","doi":"10.1007/978-981-95-4409-7_19","title":"Task-Specific Knowledge Distillation for Scalable Sentiment Classification in Low-Resource Settings","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Scalability; Sentiment analysis; Limiting; Distillation; Software deployment; Enhanced Data Rates for GSM Evolution","score_opus":0.03929592685276493,"score_gpt":0.3017272538036325,"score_spread":0.26243132695086757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416142091","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029351102,0.002519937,0.9366436,0.0012390323,0.000994747,0.00026049727,0.0032869622,0.015854765,0.009849472],"genre_scores_gemma":[0.3276196,0.0016353531,0.6383562,0.0010747283,0.00094827893,0.00065229414,0.013996066,0.0012667333,0.014450622],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993012,0.00013897703,0.000056531037,0.00020699027,0.00015997227,0.0001362921],"domain_scores_gemma":[0.9987251,0.00059768057,0.000052667667,0.0003264939,0.00021987052,0.00007820474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009812242,0.0018013349,0.0016811986,0.00088823563,0.0008509767,0.0018394048,0.0027537148,0.001319722,0.013972518],"category_scores_gemma":[0.0044449517,0.000655916,0.0011891349,0.00209199,0.00047693486,0.0042070933,0.0028708624,0.0030249287,0.0073427814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045689702,0.0005128798,0.0005959561,0.00040221476,0.00013305455,0.00016596788,0.00009439787,0.0534768,0.0129313255,0.009989237,0.08235975,0.8388815],"study_design_scores_gemma":[0.000060576986,0.00006493943,0.00023937489,0.000023659342,0.0000375574,0.000054849166,0.000048514717,0.9583398,0.004305208,0.030990757,0.005814825,0.000020030402],"about_ca_topic_score_codex":0.0046177926,"about_ca_topic_score_gemma":0.011434865,"teacher_disagreement_score":0.013972518,"about_ca_system_score_codex":0.0006882951,"about_ca_system_score_gemma":0.001584366,"threshold_uncertainty_score":0.046742737},"labels":[],"label_agreement":null},{"id":"W4416341558","doi":"10.1109/aists66100.2025.11232732","title":"ABSA-Driven Recommendation System for Domain-Specific Decision-Making","year":2025,"lang":"","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Government (linguistics); Sentiment analysis; Process (computing); Public opinion; Feeling; Democracy; Corporate governance","score_opus":0.02393934013506724,"score_gpt":0.31008454845416605,"score_spread":0.28614520831909884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416341558","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08527949,0.0023608808,0.71752846,0.0026999798,0.00081698707,0.0024407017,0.039543014,0.11734668,0.031983808],"genre_scores_gemma":[0.3558655,0.0009837514,0.5767158,0.0011058409,0.00020576705,0.001153745,0.0404804,0.00047215135,0.023016954],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994307,0.00008379297,0.00007968907,0.00019291553,0.00016118264,0.000051673047],"domain_scores_gemma":[0.99867404,0.0003655987,0.000060393704,0.00017317489,0.0006286357,0.0000982754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008964386,0.0010262277,0.00085625995,0.0018038462,0.000690706,0.0010542751,0.0014973903,0.001103785,0.013119778],"category_scores_gemma":[0.0027123631,0.00043398238,0.0011513145,0.0012999442,0.0001444264,0.0011174047,0.00061249186,0.0011042199,0.010030086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001437219,0.0011155605,0.013796671,0.0007918138,0.0004624405,0.001057839,0.0002393739,0.028836438,0.0244939,0.0036746184,0.15434256,0.7697515],"study_design_scores_gemma":[0.0001779423,0.00020943006,0.006191857,0.000102107384,0.00016726322,0.0003296985,0.00019726588,0.9138789,0.013156541,0.005822667,0.059674803,0.00009146277],"about_ca_topic_score_codex":0.032581665,"about_ca_topic_score_gemma":0.061703503,"teacher_disagreement_score":0.032581665,"about_ca_system_score_codex":0.001022282,"about_ca_system_score_gemma":0.0013749243,"threshold_uncertainty_score":0.06478405},"labels":[],"label_agreement":null},{"id":"W4416539290","doi":"10.48550/arxiv.2504.06166","title":"Assessing how hyperparameters impact Large Language Models' sarcasm detection performance","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sarcasm; Hyperparameter; Natural language understanding; Language model; Computational linguistics","score_opus":0.0628139093902101,"score_gpt":0.32778456308638204,"score_spread":0.26497065369617195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416539290","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79761,0.013306993,0.13112463,0.0070314957,0.0021053327,0.0006758129,0.006039693,0.025533287,0.016572805],"genre_scores_gemma":[0.94225276,0.0009148717,0.04312495,0.001332048,0.00022006677,0.00037451406,0.0074608657,0.0009304929,0.0033894207],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966774,0.0015444607,0.00021184057,0.00096884556,0.00033508762,0.00026235668],"domain_scores_gemma":[0.98934036,0.0076301475,0.00037405986,0.0013383602,0.00092151546,0.00039549498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009200376,0.0030041386,0.001263261,0.0013825143,0.0011044724,0.0032214706,0.002018451,0.0028686586,0.0025612765],"category_scores_gemma":[0.031518172,0.00091283413,0.0013825367,0.0007545635,0.0010171676,0.0047341306,0.0014657415,0.005337833,0.0028346947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0032620656,0.0016542115,0.072694086,0.0015242654,0.002225677,0.0007861487,0.0016116878,0.40090552,0.024144627,0.004423384,0.07272146,0.41404682],"study_design_scores_gemma":[0.00024888376,0.00065435184,0.0077799074,0.0002853347,0.00034227266,0.0003386503,0.00057471177,0.9651429,0.01063653,0.0074089216,0.006461535,0.0001259535],"about_ca_topic_score_codex":0.012536015,"about_ca_topic_score_gemma":0.021839552,"teacher_disagreement_score":0.012536015,"about_ca_system_score_codex":0.001864376,"about_ca_system_score_gemma":0.0015806345,"threshold_uncertainty_score":0.04865682},"labels":[],"label_agreement":null},{"id":"W4416799345","doi":"10.1109/snpd65828.2025.11254747","title":"Empirical Study of BERT-Based Models for Sentiment Analysis","year":2025,"lang":"","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Sentiment analysis; Empirical research; Social media; Class (philosophy); Deep learning; Language model; Natural language","score_opus":0.06404052016985219,"score_gpt":0.36768706393139966,"score_spread":0.3036465437615475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416799345","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8982524,0.0019079807,0.08601254,0.0022785875,0.00017472493,0.00020992101,0.0033043846,0.0009198629,0.006939534],"genre_scores_gemma":[0.98166174,0.00025250242,0.01392668,0.000120554505,0.00004905928,0.00007036819,0.0028756205,0.000055997807,0.0009875001],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99775416,0.0015166025,0.00011425274,0.00025086425,0.00023386911,0.00013025902],"domain_scores_gemma":[0.9484408,0.044364534,0.0015813133,0.002220324,0.0028029836,0.00059010636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012359209,0.0012124948,0.0007241246,0.00092381105,0.00061800936,0.0013328335,0.0014472012,0.0010803543,0.0022623602],"category_scores_gemma":[0.042319912,0.0004464728,0.0007168951,0.0013323221,0.0006455367,0.0030086767,0.00080989057,0.0019869069,0.00077895354],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002034291,0.00070454966,0.07511568,0.0004932891,0.00030107162,0.00021302936,0.00039590767,0.79629016,0.0016719679,0.0118904505,0.017023524,0.09386609],"study_design_scores_gemma":[0.000016694641,0.00007330757,0.0033988946,0.000013132598,0.000011821171,0.000024412226,0.00004580048,0.993298,0.00029029397,0.0023288212,0.00048715874,0.000011626496],"about_ca_topic_score_codex":0.008681131,"about_ca_topic_score_gemma":0.0089526065,"teacher_disagreement_score":0.012359209,"about_ca_system_score_codex":0.0022424133,"about_ca_system_score_gemma":0.00068451697,"threshold_uncertainty_score":0.06536257},"labels":[],"label_agreement":null},{"id":"W4416799549","doi":"10.1109/snpd65828.2025.11253182","title":"Personalizing E-Commerce by Optimizing LLMs for Tailored Product Recommendations","year":2025,"lang":"","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Algoma University","funders":"","keywords":"Preprocessor; Context (archaeology); Encoder; Product (mathematics); Implementation; Ranking (information retrieval); Dual (grammatical number); Data pre-processing","score_opus":0.03367819884080886,"score_gpt":0.32029571166584303,"score_spread":0.28661751282503417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416799549","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05837645,0.0005676767,0.93406606,0.00050702813,0.000074568365,0.00016023559,0.00037001376,0.0028226357,0.003055404],"genre_scores_gemma":[0.5430406,0.00036478825,0.4504177,0.00042998735,0.0001240267,0.00024061577,0.0010611486,0.00025715306,0.004063948],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99935967,0.00017668944,0.000050054656,0.00016545368,0.00018064628,0.00006757775],"domain_scores_gemma":[0.9984773,0.00086073286,0.00011202256,0.00015156057,0.0003483502,0.000050060375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014174647,0.0009527067,0.0008019086,0.0010173619,0.00030406,0.0010943725,0.0009916646,0.0010109026,0.0025105677],"category_scores_gemma":[0.0055134716,0.000495927,0.0006297013,0.0009350993,0.00038367638,0.0017961407,0.00073210907,0.0011645778,0.0016066196],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003682721,0.0005268532,0.006110645,0.00022934456,0.0001675038,0.00011152816,0.00015953749,0.34079432,0.025805883,0.00518514,0.0080814175,0.6124596],"study_design_scores_gemma":[0.000015655098,0.000041766674,0.00034033178,0.0000046967307,0.000020403966,0.000018200699,0.000019680398,0.99367297,0.003128693,0.0020394519,0.0006923491,0.000005826998],"about_ca_topic_score_codex":0.0066549177,"about_ca_topic_score_gemma":0.013382338,"teacher_disagreement_score":0.0066549177,"about_ca_system_score_codex":0.0010098837,"about_ca_system_score_gemma":0.0013040191,"threshold_uncertainty_score":0.01323235},"labels":[],"label_agreement":null},{"id":"W4417003267","doi":"10.1109/icitcom66635.2025.11265185","title":"MultiGranular Sentiment Intensity Augmentation with Ordinal Contrastive Learning for Enhanced Sentiment Classification","year":2025,"lang":"","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Marriott International (Canada)","funders":"","keywords":"Sentiment analysis; Benchmark (surveying); Contrast (vision); Embedding; Pattern recognition (psychology); Function (biology)","score_opus":0.022207019549667514,"score_gpt":0.2942852440879996,"score_spread":0.2720782245383321,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417003267","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12800871,0.00049211236,0.86081964,0.0005347066,0.00016834991,0.00016342214,0.00047848557,0.0035086814,0.0058258185],"genre_scores_gemma":[0.6686309,0.0002495357,0.32400456,0.00047517338,0.00017528495,0.00027128865,0.001575559,0.00032456324,0.004293077],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999622,0.000106656174,0.000021916578,0.00009923535,0.00010558553,0.000044583318],"domain_scores_gemma":[0.99940825,0.00022434459,0.000073021256,0.00009869773,0.00015997124,0.000035693116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008879153,0.0009394457,0.0005523496,0.00077512726,0.00029235092,0.0008622351,0.0008158004,0.00047357683,0.0027989978],"category_scores_gemma":[0.0028131157,0.0002211674,0.0006884142,0.00058460503,0.00046116736,0.0016064995,0.0013324895,0.0014224707,0.0013062753],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006383659,0.0005229124,0.00641792,0.00029027066,0.00012235096,0.00017806425,0.00044550275,0.06591502,0.13666323,0.011605907,0.0139556145,0.7632449],"study_design_scores_gemma":[0.00004950964,0.00019480358,0.0022319583,0.000024416988,0.000036600868,0.00006858292,0.000118268305,0.95513195,0.022761183,0.01465134,0.0047035813,0.000027809812],"about_ca_topic_score_codex":0.0005670019,"about_ca_topic_score_gemma":0.001305672,"teacher_disagreement_score":0.0027989978,"about_ca_system_score_codex":0.000403855,"about_ca_system_score_gemma":0.00038527718,"threshold_uncertainty_score":0.009363592},"labels":[],"label_agreement":null},{"id":"W4417092342","doi":"10.22214/ijraset.2025.75971","title":"GovPulse AI-powered News Intelligence and Sentiment Alert System","year":2025,"lang":"","type":"article","venue":"International Journal for Research in Applied Science and Engineering Technology","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Government (linguistics); Corporate governance; Intelligence analysis; E-Government; Sentiment analysis","score_opus":0.0511332681680002,"score_gpt":0.3911011900737217,"score_spread":0.3399679219057215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417092342","genre_codex":"software","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060530934,0.0014878779,0.3202319,0.0023010466,0.0010713977,0.0023704157,0.030124452,0.4924533,0.08942872],"genre_scores_gemma":[0.38600895,0.0012105682,0.43793216,0.0028014479,0.00045692493,0.002243333,0.07658634,0.004039143,0.08872114],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995316,0.00004862369,0.000042397074,0.0001383494,0.00019133101,0.000047649086],"domain_scores_gemma":[0.999373,0.00010297539,0.000055713514,0.00009918538,0.000292593,0.00007654314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006835444,0.0010247149,0.0005730024,0.0026117929,0.00064657786,0.0017764254,0.0013256408,0.0009236597,0.014951692],"category_scores_gemma":[0.0020069259,0.0003206481,0.00050478586,0.0011142874,0.00023823555,0.002003873,0.001315914,0.00094279245,0.011129757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012026975,0.0006863817,0.007837631,0.0010097609,0.0002318553,0.0008764071,0.0006867091,0.0028311238,0.06903687,0.006318883,0.3004223,0.60885936],"study_design_scores_gemma":[0.0003493826,0.0006630565,0.023357233,0.0002407364,0.00036234164,0.0011578468,0.00090242364,0.4474073,0.122745045,0.020216193,0.38232967,0.00026871377],"about_ca_topic_score_codex":0.00569967,"about_ca_topic_score_gemma":0.0053170957,"teacher_disagreement_score":0.014951692,"about_ca_system_score_codex":0.00079943694,"about_ca_system_score_gemma":0.00079171674,"threshold_uncertainty_score":0.05001843},"labels":[],"label_agreement":null},{"id":"W4417312283","doi":"10.18280/isi.301008","title":"Enhancing Sentiment Analysis Accuracy Through Intelligent Spelling Correction Using Damerau-Levenshtein Distance and N-Gram with Random Forest Classifier","year":2025,"lang":"","type":"article","venue":"Ingénierie des systèmes d information","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Random forest; Classifier (UML); Sentiment analysis; Pattern recognition (psychology)","score_opus":0.02123104573779639,"score_gpt":0.2740604898328334,"score_spread":0.252829444095037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417312283","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21883956,0.0012541657,0.76204455,0.0006010968,0.0008296804,0.00020943626,0.0012751806,0.009524183,0.0054220892],"genre_scores_gemma":[0.4931254,0.00053310365,0.4940874,0.00019149497,0.00024331718,0.0001093927,0.0035070237,0.0005266139,0.007676359],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987411,0.000192803,0.00015119232,0.00022197227,0.0005537272,0.00013925422],"domain_scores_gemma":[0.9976943,0.00042794493,0.00018872086,0.00017118816,0.001459068,0.000058702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011433874,0.0009269111,0.00091946346,0.0017055107,0.00083349436,0.00096458197,0.0005579862,0.00057571416,0.0025262465],"category_scores_gemma":[0.0032974351,0.00015863047,0.00075053604,0.0011964537,0.0001755997,0.001283155,0.0005724028,0.00075175613,0.0034966948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042159014,0.00025229176,0.004395155,0.00015771975,0.00008131773,0.00016903407,0.00010266465,0.0039421762,0.13054192,0.0007109881,0.008632634,0.8505926],"study_design_scores_gemma":[0.00006065176,0.0004568015,0.0141091645,0.00005330595,0.00019963185,0.0005673477,0.0002639391,0.76059717,0.20960216,0.0023034012,0.011699371,0.00008707307],"about_ca_topic_score_codex":0.0046811383,"about_ca_topic_score_gemma":0.006978689,"teacher_disagreement_score":0.0046811383,"about_ca_system_score_codex":0.00033358744,"about_ca_system_score_gemma":0.0010320928,"threshold_uncertainty_score":0.009307802},"labels":[],"label_agreement":null},{"id":"W4417420641","doi":"10.3390/a18120798","title":"Genetic-Based Lottery Ticket Pruning for Transformers in Sentiment Classification: Realized Through Lottery Sample Selection","year":2025,"lang":"en","type":"article","venue":"Algorithms","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Inference; Lottery; Ticket; Transformer; Pruning; Sample (material); Selection (genetic algorithm); Language model","score_opus":0.035167542418044734,"score_gpt":0.3140874068677849,"score_spread":0.27891986444974015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417420641","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1740514,0.00023774928,0.8179994,0.000709573,0.00010381704,0.00024374388,0.00015087177,0.0032440736,0.0032593508],"genre_scores_gemma":[0.74261993,0.00008474872,0.25214162,0.00068105856,0.00005494478,0.00028082135,0.00062487554,0.0003517809,0.0031602078],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990557,0.00037997175,0.000051754745,0.0001742239,0.00019706678,0.0001412214],"domain_scores_gemma":[0.9960795,0.0025512825,0.00020963843,0.0005159611,0.0004981299,0.00014559933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026434187,0.0008203654,0.0009570021,0.0009614002,0.0007050101,0.0010575041,0.001962033,0.0011959911,0.003674172],"category_scores_gemma":[0.009219539,0.00054264045,0.00095785095,0.0006166853,0.001074162,0.0018421307,0.001325013,0.0017380442,0.00093602284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011426335,0.0007726358,0.014877597,0.00017715206,0.00020666052,0.00041401558,0.0005268692,0.38102168,0.025010644,0.026303818,0.009500416,0.5400459],"study_design_scores_gemma":[0.000041839096,0.00008748094,0.0004816739,0.000009849869,0.000026001162,0.00003867325,0.00004368742,0.9894908,0.0037372734,0.005393211,0.0006412541,0.0000081417775],"about_ca_topic_score_codex":0.004165625,"about_ca_topic_score_gemma":0.008024248,"teacher_disagreement_score":0.004165625,"about_ca_system_score_codex":0.0010913553,"about_ca_system_score_gemma":0.0017293963,"threshold_uncertainty_score":0.013979912},"labels":[],"label_agreement":null},{"id":"W45569842","doi":"10.1111/j.1467-8640.2012.00417.x","title":"MULTI‐DOCUMENT SUMMARIZATION OF EVALUATIVE TEXT","year":2012,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":161,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of British Columbia","funders":"","keywords":"Automatic summarization; Computer science; Multi-document summarization; Information retrieval; Natural language processing","score_opus":0.06534615047374848,"score_gpt":0.35946882201812624,"score_spread":0.29412267154437777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W45569842","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1800574,0.0064386553,0.79364216,0.0012467079,0.00039722805,0.0010988904,0.0033222025,0.0053094774,0.008487305],"genre_scores_gemma":[0.3974231,0.0018788475,0.58810383,0.0002195079,0.00038766608,0.00057694135,0.0054415865,0.00037552355,0.0055930917],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997834,0.0009254996,0.00020506972,0.00033352574,0.0006304194,0.000071402814],"domain_scores_gemma":[0.98932487,0.006204506,0.0011325693,0.00089597324,0.0022650703,0.00017698658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003716193,0.0010043858,0.0008305039,0.0030649607,0.0006395458,0.0018826168,0.00071369635,0.0004940515,0.0020517372],"category_scores_gemma":[0.012489135,0.0002154585,0.00051423605,0.0017760018,0.00031160234,0.0020104179,0.0008648583,0.0007171534,0.0008171114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007010719,0.00026974565,0.0031933272,0.0024942018,0.00025522278,0.00046576053,0.0038620916,0.020594835,0.053826284,0.0073610297,0.012661518,0.89431494],"study_design_scores_gemma":[0.00033432653,0.0029729125,0.02531066,0.0011483376,0.0013007246,0.0011342472,0.0075243167,0.48697308,0.21277888,0.0627846,0.19731142,0.00042646698],"about_ca_topic_score_codex":0.00085434556,"about_ca_topic_score_gemma":0.0020924779,"teacher_disagreement_score":0.003716193,"about_ca_system_score_codex":0.00046510418,"about_ca_system_score_gemma":0.0006926849,"threshold_uncertainty_score":0.01965332},"labels":[],"label_agreement":null},{"id":"W55067354","doi":"","title":"INFORMATIONAL SUPPORT OR EMOTIONAL SUPPORT: PRELIMINARY STUDY OF AN AUTOMATED APPROACH TO ANALYZE ONLINE SUPPORT COMMUNITY CONTENTS","year":2010,"lang":"en","type":"article","venue":"International Conference on Information Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Emotional support; Content analysis; Support vector machine; Qualitative analysis; Online community; Machine learning; Data mining; Qualitative research; World Wide Web; Social support; Psychology","score_opus":0.0816712906517177,"score_gpt":0.3504108192172091,"score_spread":0.26873952856549144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W55067354","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.867354,0.00016821278,0.123671584,0.0003994196,0.00004039144,0.0013292165,0.00064366916,0.0008792554,0.005514199],"genre_scores_gemma":[0.84924495,0.00011956312,0.14763209,0.00008021341,0.000057962032,0.000637856,0.0005994046,0.000056833847,0.0015711052],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99720746,0.0014253452,0.00014752251,0.00034224792,0.00072962284,0.00014770933],"domain_scores_gemma":[0.9750293,0.019153917,0.0014763238,0.000992273,0.002929406,0.00041885738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003134034,0.00041281915,0.00037694463,0.0022864302,0.00072599197,0.0016132002,0.00077208044,0.00066992454,0.0016427656],"category_scores_gemma":[0.018056218,0.00021085028,0.0002956717,0.0013672144,0.0005326777,0.0019563353,0.000719748,0.0006411056,0.0007069272],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015058933,0.0041032257,0.15304796,0.0012471003,0.00014708529,0.00043272556,0.012275721,0.0071656993,0.06634658,0.003961954,0.0030241527,0.7467419],"study_design_scores_gemma":[0.00024112363,0.0029347404,0.28717458,0.0001870855,0.00021005742,0.00090328156,0.0137023805,0.6159403,0.05765341,0.008668812,0.01219947,0.00018482936],"about_ca_topic_score_codex":0.003403167,"about_ca_topic_score_gemma":0.0039984942,"teacher_disagreement_score":0.003403167,"about_ca_system_score_codex":0.00062097213,"about_ca_system_score_gemma":0.0008284793,"threshold_uncertainty_score":0.016574502},"labels":[],"label_agreement":null},{"id":"W561237857","doi":"","title":"Sentence-level sentiment tagging across different domains and genres","year":2009,"lang":"en","type":"dissertation","venue":"Spectrum Research Repository (Concordia University)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Artificial intelligence; WordNet; Natural language processing; Sentiment analysis; Lexicon; Software portability; Annotation; Sentence; Domain (mathematical analysis)","score_opus":0.03994814824358621,"score_gpt":0.3079360223518373,"score_spread":0.2679878741082511,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W561237857","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58274376,0.001589019,0.34173673,0.0011003496,0.0009733192,0.0009835282,0.012920635,0.0059415144,0.0520111],"genre_scores_gemma":[0.7570897,0.00087754766,0.21041234,0.000384871,0.00034199454,0.0005821097,0.018259676,0.0005442728,0.011507579],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990829,0.00028855374,0.0001315503,0.00021643483,0.00022117455,0.00005931587],"domain_scores_gemma":[0.9955705,0.0014697651,0.00040073608,0.00033744908,0.0021030868,0.000118461874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018812141,0.00051198987,0.00052655034,0.0025364235,0.0006258505,0.0014190881,0.00037209404,0.0003549495,0.0028082433],"category_scores_gemma":[0.006418078,0.00028828028,0.00067516975,0.0019727442,0.00020493694,0.0017708087,0.0008343442,0.00065163826,0.0028903838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006351401,0.0003443819,0.06576199,0.00096935214,0.00024879965,0.00043853765,0.0025972514,0.0038049612,0.20073065,0.0037002799,0.029254593,0.6915141],"study_design_scores_gemma":[0.000102829465,0.0012439227,0.30836713,0.0006789302,0.0009573099,0.0013466596,0.0054700463,0.33542773,0.20664413,0.018776389,0.12070052,0.00028449125],"about_ca_topic_score_codex":0.0012635805,"about_ca_topic_score_gemma":0.0027955542,"teacher_disagreement_score":0.0028082433,"about_ca_system_score_codex":0.00038952328,"about_ca_system_score_gemma":0.00048854516,"threshold_uncertainty_score":0.009948909},"labels":[],"label_agreement":null},{"id":"W570968517","doi":"10.17705/1thci.00050","title":"Introduction to the Special Issue on Human-Computer Interaction in the Web 2.0 Era","year":2013,"lang":"en","type":"article","venue":"AIS Transactions on Human-Computer Interaction","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; World Wide Web; Human–computer interaction","score_opus":0.03482592404752127,"score_gpt":0.31654357191948584,"score_spread":0.28171764787196457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W570968517","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00023279511,0.07099311,0.002133239,0.04683911,0.8535444,0.0000945972,0.00034762453,0.00022931276,0.025585763],"genre_scores_gemma":[0.0010294515,0.048576776,0.00062928634,0.015339083,0.8883087,0.00010505935,0.00041360818,0.00025571565,0.045342345],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99668056,0.0005241386,0.0003874286,0.0007624168,0.0013296057,0.0003158111],"domain_scores_gemma":[0.9853505,0.0067263464,0.00081594626,0.00087715714,0.0039886073,0.002241586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032011091,0.0034630962,0.004887511,0.0057139313,0.002794463,0.011668992,0.0035880748,0.009487277,0.10136509],"category_scores_gemma":[0.010254616,0.0010148184,0.0026873206,0.0044036005,0.0026551813,0.010609509,0.0039331554,0.0122548025,0.05679102],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021240756,0.000035908117,0.00014387292,0.00036996257,0.000014676731,0.00006806305,0.000044022785,0.000057941295,0.000113906885,0.0014958563,0.9729576,0.024676917],"study_design_scores_gemma":[0.000007801802,0.00003793372,0.00058806565,0.0005169158,0.000018294926,0.00028953957,0.00006625601,0.00014758494,0.00005352002,0.0021975278,0.99605983,0.000016575397],"about_ca_topic_score_codex":0.0013938388,"about_ca_topic_score_gemma":0.0024615044,"teacher_disagreement_score":0.10136509,"about_ca_system_score_codex":0.0026264128,"about_ca_system_score_gemma":0.0023102632,"threshold_uncertainty_score":0.3391},"labels":[],"label_agreement":null},{"id":"W582033733","doi":"10.1609/aaai.v27i1.8700","title":"A Hierarchical Aspect-Sentiment Model for Online Reviews","year":2013,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":126,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Korea Evaluation Institute of Industrial Technology; Ministry of Knowledge Economy","keywords":"Computer science; Sentiment analysis; Tree (set theory); Tree structure; Hierarchical database model; Artificial intelligence; Sentence; Topic model; Polarity (international relations); Bayesian probability; Process (computing); Machine learning; Natural language processing; Data mining; Data structure; Mathematics","score_opus":0.14674591981338353,"score_gpt":0.33825623807699573,"score_spread":0.1915103182636122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W582033733","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07351638,0.0010168258,0.9178928,0.0009441445,0.00008952873,0.00014932864,0.0012973299,0.0009802459,0.004113409],"genre_scores_gemma":[0.83915263,0.0009770243,0.14834113,0.00035948562,0.00031664918,0.00045593717,0.0025851154,0.00015494518,0.007657019],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929774,0.00025428314,0.000042214815,0.00021628883,0.00012373639,0.00006568313],"domain_scores_gemma":[0.99863213,0.00077901466,0.00018663022,0.00007751082,0.00026934023,0.000055425877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013984079,0.0008486879,0.0009192295,0.0013966863,0.0004134495,0.0010644408,0.0015322019,0.0011493663,0.0020547877],"category_scores_gemma":[0.0046631433,0.0007806739,0.0012860451,0.0013696143,0.00053648115,0.002025257,0.0005896147,0.0012988676,0.0012534412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007326361,0.00028325443,0.023666095,0.0004551089,0.0004143002,0.0007197384,0.0014271032,0.58687776,0.008533045,0.11574944,0.017374713,0.24376677],"study_design_scores_gemma":[0.000011132807,0.000020412514,0.0009059087,0.00000733835,0.00002250571,0.00004511329,0.0000125879515,0.9852937,0.0001399682,0.0127607,0.0007717901,0.000008883054],"about_ca_topic_score_codex":0.009678348,"about_ca_topic_score_gemma":0.014471528,"teacher_disagreement_score":0.009678348,"about_ca_system_score_codex":0.0008660049,"about_ca_system_score_gemma":0.0008401217,"threshold_uncertainty_score":0.019244015},"labels":[],"label_agreement":null},{"id":"W6884624144","doi":"10.11575/prism/42174","title":"Advancing Smart Cities through Novel Social Media Text Analysis: A Case Study of Calgary","year":2023,"lang":"en","type":"other","venue":"Open MIND","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Social media; Variety (cybernetics); Set (abstract data type); Bureaucracy; Population; Perception","score_opus":0.0709479041161818,"score_gpt":0.34330852866463896,"score_spread":0.27236062454845716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6884624144","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.989528,0.000113220456,0.0013086718,0.001892194,0.000028478675,0.00012390845,0.00032490946,0.000053982607,0.0066267406],"genre_scores_gemma":[0.9833219,0.00039522434,0.0052727973,0.000871964,0.00005387994,0.00009132602,0.0005309327,0.000096660806,0.00936527],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99909353,0.00039602432,0.0000318408,0.00013337827,0.00016956717,0.00017569451],"domain_scores_gemma":[0.99810094,0.0010353703,0.00016110134,0.00010938454,0.00033478817,0.00025843817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010623919,0.0005276491,0.00026783728,0.0013648064,0.0058339513,0.0024392758,0.0011761418,0.0016404993,0.001471004],"category_scores_gemma":[0.0029826544,0.00019002717,0.00022136953,0.0028690652,0.002334617,0.0013672457,0.0015038127,0.0013739681,0.00035237262],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003931513,0.0017592384,0.13502885,0.0006062355,0.00018753212,0.05973303,0.5648108,0.008670807,0.011623992,0.010792714,0.040219106,0.16617453],"study_design_scores_gemma":[0.000058184545,0.00022593218,0.14101698,0.00014171602,0.000058222293,0.0015678436,0.7371383,0.021133255,0.0041131875,0.002590108,0.091869764,0.00008649347],"about_ca_topic_score_codex":0.22883186,"about_ca_topic_score_gemma":0.45581555,"teacher_disagreement_score":0.7711681,"about_ca_system_score_codex":0.00509678,"about_ca_system_score_gemma":0.0021276472,"threshold_uncertainty_score":0.4549998},"labels":[],"label_agreement":null},{"id":"W6888990958","doi":"10.25318/3310032201-eng","title":"Ability for the business or organization to take on more debt, by business characteristics, first quarter of 2021","year":2021,"lang":"en","type":"dataset","venue":"Statistics Canada Dissemination","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Business analysis; Business valuation; Business failure; New business development","score_opus":0.007539747152211175,"score_gpt":0.2536582725374462,"score_spread":0.24611852538523504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6888990958","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014701851,0.000045681867,0.000029284947,0.00014255862,0.000028130686,0.0000126322775,0.99755365,0.000054845117,0.0006631708],"genre_scores_gemma":[0.002273273,0.000057314905,0.00012042839,0.000068964095,0.000010473837,0.000046928846,0.99654883,0.000010918782,0.00086289964],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99951553,0.000045861543,0.00008307106,0.00013592384,0.000128807,0.000090873946],"domain_scores_gemma":[0.9983924,0.00033233594,0.00028907406,0.0001687404,0.0006212851,0.00019613898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073597184,0.00094403425,0.00070543215,0.0018173221,0.00054791645,0.0012299177,0.0015007248,0.0010482151,0.016189203],"category_scores_gemma":[0.0045461333,0.00031212444,0.0008265273,0.00313686,0.00022576457,0.0008698717,0.0008909115,0.0012326045,0.015371922],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011114469,0.00003866107,0.0114509165,0.00037136383,0.000041564108,0.000019977626,0.000026328225,0.00025638024,0.00009231397,0.0003261423,0.98493934,0.0023259113],"study_design_scores_gemma":[0.00086824805,0.00013128221,0.24568915,0.00068289746,0.00015007218,0.00029802497,0.00066074537,0.003228352,0.0007601572,0.0014292906,0.746004,0.00009784466],"about_ca_topic_score_codex":0.07381121,"about_ca_topic_score_gemma":0.15914603,"teacher_disagreement_score":0.92618877,"about_ca_system_score_codex":0.001717979,"about_ca_system_score_gemma":0.0013836042,"threshold_uncertainty_score":0.14676315},"labels":[],"label_agreement":null},{"id":"W6901750632","doi":"10.60692/6esv2-x4v18","title":"AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages","year":2022,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Language model; Context (archaeology); Code (set theory); Training set; Language identification; Natural language; Code-switching; Downstream (manufacturing)","score_opus":0.026089059852597768,"score_gpt":0.24671678319012924,"score_spread":0.22062772333753147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6901750632","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19480805,0.0048270416,0.67567235,0.0018502653,0.0019268193,0.0007926239,0.019605624,0.082050055,0.01846713],"genre_scores_gemma":[0.47262076,0.0014460337,0.4193441,0.0013719396,0.00024137222,0.0012241189,0.07460514,0.0032095446,0.025937036],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939156,0.00015523654,0.000044686,0.00022443393,0.00010170491,0.000082362116],"domain_scores_gemma":[0.99926597,0.00032393506,0.000039770453,0.00013520439,0.00018435362,0.000050737293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011833028,0.0023632667,0.00096224877,0.0011102447,0.0007776453,0.0012667467,0.0026912072,0.0012829285,0.007431809],"category_scores_gemma":[0.0027485774,0.0007703878,0.0019057818,0.00086867023,0.00060834433,0.002586537,0.002180875,0.0032920062,0.005776548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00082492235,0.00072617404,0.0064098174,0.00072632293,0.00051962986,0.0008964555,0.0007257593,0.168666,0.020240458,0.0049097007,0.08409529,0.71125954],"study_design_scores_gemma":[0.00010419893,0.00024534378,0.0017267944,0.00008889399,0.00010714994,0.0003726329,0.0002679036,0.942171,0.019881878,0.00459881,0.030327545,0.00010773338],"about_ca_topic_score_codex":0.013273945,"about_ca_topic_score_gemma":0.022708232,"teacher_disagreement_score":0.013273945,"about_ca_system_score_codex":0.00087036245,"about_ca_system_score_gemma":0.0015288878,"threshold_uncertainty_score":0.026393354},"labels":[],"label_agreement":null},{"id":"W6901787445","doi":"10.60692/knrg0-pvc70","title":"WordNet Semantic Relations Based Enhancement of KNN Model for Implicit Aspect Identification in Sentiment Analysis","year":2023,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Overfitting; WordNet; Sentiment analysis; Task (project management); Key (lock); Identification (biology); Semantics (computer science); Computation","score_opus":0.045491757637183876,"score_gpt":0.2610148729722463,"score_spread":0.2155231153350624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6901787445","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22388622,0.00081391307,0.7680279,0.00044464774,0.00018715163,0.00022440233,0.00049014494,0.0014363646,0.004489288],"genre_scores_gemma":[0.81887996,0.00028105231,0.1774536,0.00017189086,0.00010552538,0.00013054015,0.0010060834,0.000080953796,0.0018904759],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922943,0.0002179125,0.00007689537,0.00020239265,0.00019954731,0.000073752],"domain_scores_gemma":[0.9988865,0.00040116347,0.00011194253,0.00009045191,0.00046946315,0.00004042896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011106323,0.0008325736,0.0007449821,0.0017009897,0.00053871964,0.0008228014,0.0008704093,0.00073874596,0.0013647414],"category_scores_gemma":[0.0031756551,0.0002220191,0.0007429601,0.001257991,0.00030921033,0.0018558328,0.0006263357,0.0007699145,0.00064670236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070384267,0.0005957546,0.018966742,0.0002884049,0.00028772527,0.00031845685,0.0005051751,0.31366208,0.016871337,0.009925234,0.0065865493,0.6312887],"study_design_scores_gemma":[0.0000072100443,0.000031785672,0.0009248077,0.0000082218885,0.000018602106,0.000024608464,0.00003518085,0.99476385,0.0011839966,0.0025023636,0.00049343257,0.0000059695776],"about_ca_topic_score_codex":0.0092331655,"about_ca_topic_score_gemma":0.0136905275,"teacher_disagreement_score":0.0092331655,"about_ca_system_score_codex":0.0007057223,"about_ca_system_score_gemma":0.00073008786,"threshold_uncertainty_score":0.018358886},"labels":[],"label_agreement":null},{"id":"W6907742069","doi":"10.25316/ir-11377","title":"The Nanaimo Free Press [Wednesday, August 11, 1875]","year":2019,"lang":"en","type":"other","venue":"VIUSpace (Vancouver Island University Library)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Free press; Gloom; Free enterprise","score_opus":0.008781673960764628,"score_gpt":0.18974750392320336,"score_spread":0.18096582996243873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6907742069","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011957049,0.013796515,0.00020270089,0.0041803033,0.0047635077,0.000033159096,0.004149305,0.00018894233,0.9714897],"genre_scores_gemma":[0.0023751664,0.0018090833,0.00007118462,0.00013509962,0.00018988758,0.000012024487,0.0003972738,0.00006690851,0.9949433],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99963915,0.000025717423,0.000013818908,0.00006427907,0.00019276264,0.00006416411],"domain_scores_gemma":[0.99971145,0.00003356677,0.000018090876,0.000020842797,0.00015241509,0.000063517095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032649242,0.000842332,0.0004864302,0.0027055407,0.004470763,0.0059828167,0.00064808223,0.0014809691,0.21870124],"category_scores_gemma":[0.0015418343,0.0003700506,0.00028980116,0.0044216434,0.0008027859,0.0019634268,0.0012329645,0.002097125,0.070308715],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027086815,0.000007233128,0.00030426992,0.00009714641,0.0000040447567,0.000100461366,0.00022547401,0.000049154696,0.00008771342,0.015637146,0.93833584,0.045124456],"study_design_scores_gemma":[0.0000012990092,0.0000022188349,0.00069806166,0.000059422186,9.156955e-7,0.000020455871,0.00006749727,0.000014216825,0.000039894207,0.00047534416,0.9986179,0.0000027568192],"about_ca_topic_score_codex":0.15471059,"about_ca_topic_score_gemma":0.53933156,"teacher_disagreement_score":0.8452894,"about_ca_system_score_codex":0.005446288,"about_ca_system_score_gemma":0.0033113097,"threshold_uncertainty_score":0.73162854},"labels":[],"label_agreement":null},{"id":"W6920376414","doi":"10.60692/njq4b-70v06","title":"Semantic and Syntactic Enhanced Aspect Sentiment Triplet Extraction","year":2021,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Sentence; Inference; Pipeline (software); Word (group theory); ENCODE; Sentiment analysis; Exploit; Graph","score_opus":0.026643637118347902,"score_gpt":0.23647160005704995,"score_spread":0.20982796293870204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6920376414","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05877155,0.0007330723,0.9083788,0.0007426772,0.00032246477,0.00043193658,0.0067537776,0.011779444,0.012086319],"genre_scores_gemma":[0.44106218,0.00064043066,0.5235163,0.00045168647,0.00022384211,0.00033077493,0.023433616,0.0007286893,0.00961249],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999624,0.0000582534,0.000034681878,0.0001265441,0.00010894675,0.00004754681],"domain_scores_gemma":[0.9995679,0.00009903372,0.00006172505,0.00007271032,0.00017653003,0.000022166852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047401965,0.0014977996,0.00074397185,0.0018733703,0.00049697305,0.00085704605,0.0007587771,0.0007548929,0.003702506],"category_scores_gemma":[0.0017328251,0.00036541128,0.0013904892,0.001478221,0.00027380596,0.0021305133,0.0011663357,0.0012433934,0.00233189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004269868,0.0002797939,0.006251583,0.00061508385,0.00024821484,0.0010070199,0.00042481135,0.02243302,0.10832376,0.014841741,0.053174555,0.7919734],"study_design_scores_gemma":[0.00005581815,0.00018409446,0.0067324475,0.000072436866,0.00019435982,0.0005952176,0.00031445897,0.86526936,0.048760183,0.04516568,0.032577988,0.000078064615],"about_ca_topic_score_codex":0.0025415076,"about_ca_topic_score_gemma":0.00582068,"teacher_disagreement_score":0.003702506,"about_ca_system_score_codex":0.0005200008,"about_ca_system_score_gemma":0.00080619345,"threshold_uncertainty_score":0.012386084},"labels":[],"label_agreement":null},{"id":"W6920483645","doi":"10.60692/s88n9-k1f75","title":"A HYBRID METHOD USING LEXICON-BASED APPROACH AND NAIVE BAYES CLASSIFIER FOR ARABIC OPINION QUESTION ANSWERING","year":2014,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Question answering; Naive Bayes classifier; Support vector machine; Classifier (UML); Sentiment analysis; Arabic; Normalization (sociology); Multi-label classification","score_opus":0.056403975560016015,"score_gpt":0.2724081555304547,"score_spread":0.2160041799704387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6920483645","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030678507,0.0018829325,0.95073545,0.0009446812,0.0005484893,0.0012050607,0.0009385336,0.004872976,0.0081933495],"genre_scores_gemma":[0.26188707,0.0013850796,0.72139305,0.0008494053,0.0005367925,0.0010774407,0.003660857,0.00021364057,0.008996676],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99679,0.0006425857,0.00047445146,0.0006655876,0.0012259023,0.00020139279],"domain_scores_gemma":[0.9977362,0.0006709099,0.000119120596,0.00008848678,0.0013265951,0.000058492442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019955586,0.0015019145,0.0017403358,0.0053507066,0.001224978,0.0023238023,0.0016863919,0.001800848,0.0044475202],"category_scores_gemma":[0.004870083,0.00046450848,0.0016173637,0.002731701,0.00049228064,0.002711685,0.00066796254,0.0010298118,0.0042196573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003449592,0.0003999243,0.005521414,0.0005273446,0.00019348945,0.00032279658,0.00039960138,0.0055453563,0.02332013,0.0028944712,0.013247514,0.9472829],"study_design_scores_gemma":[0.000235915,0.00054905686,0.008493427,0.00026170904,0.00057839963,0.0017384113,0.0011124685,0.899982,0.0363568,0.014497923,0.03594543,0.00024849057],"about_ca_topic_score_codex":0.008803088,"about_ca_topic_score_gemma":0.009463034,"teacher_disagreement_score":0.008803088,"about_ca_system_score_codex":0.0010252851,"about_ca_system_score_gemma":0.0019042696,"threshold_uncertainty_score":0.017503738},"labels":[],"label_agreement":null},{"id":"W6920552191","doi":"10.60692/7yqwd-3rk86","title":"SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)","year":2023,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Task (project management); Sentiment analysis; SemEval; Languages of Africa; Task analysis","score_opus":0.04113118126596096,"score_gpt":0.256651799655822,"score_spread":0.21552061838986106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6920552191","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30465332,0.010878063,0.10103791,0.01256065,0.016180154,0.009579292,0.3639671,0.072577365,0.10856616],"genre_scores_gemma":[0.21589908,0.001096626,0.17091934,0.0030806188,0.0016796478,0.0057636616,0.53423715,0.0060890303,0.061234828],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9928196,0.0029591266,0.0005396072,0.0012908587,0.0014967476,0.00089410006],"domain_scores_gemma":[0.9888254,0.0034820328,0.00041863683,0.001595183,0.003991561,0.001687253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009221613,0.0039824042,0.0022940224,0.00331562,0.0037085388,0.0035462505,0.0025222974,0.003979795,0.018683577],"category_scores_gemma":[0.014663641,0.0006090744,0.0021361378,0.002536635,0.0009444175,0.0036865459,0.0061605317,0.003335179,0.018584097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011104675,0.0010452636,0.0035834245,0.0017320354,0.00027306468,0.00049536146,0.0008916953,0.0018884906,0.018420342,0.0010467704,0.8072174,0.16229562],"study_design_scores_gemma":[0.0015377458,0.0016434195,0.029401053,0.0005641513,0.00034816386,0.0016621702,0.003814273,0.061228823,0.047645725,0.0072769504,0.8444821,0.00039546192],"about_ca_topic_score_codex":0.010246232,"about_ca_topic_score_gemma":0.022871818,"teacher_disagreement_score":0.018683577,"about_ca_system_score_codex":0.001996194,"about_ca_system_score_gemma":0.004318801,"threshold_uncertainty_score":0.0625028},"labels":[],"label_agreement":null},{"id":"W6928922439","doi":"10.48448/7ns7-xx17","title":"Semantics Squad at BLP-2023 Task 2: Sentiment Analysis of Bangla Text with Fine Tuned Transformer Based Models","year":2023,"lang":"en","type":"other","venue":"Open MIND","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Transformer; Task (project management); Sentiment analysis; Bengali; Semantics (computer science)","score_opus":0.03927372684706836,"score_gpt":0.28835270906434257,"score_spread":0.2490789822172742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6928922439","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24035688,0.0036102799,0.23528466,0.004871696,0.004774112,0.0020985887,0.23811217,0.18914956,0.08174208],"genre_scores_gemma":[0.33722597,0.000538634,0.20893367,0.0010805478,0.0005113718,0.000991825,0.40191397,0.006875327,0.041928634],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981731,0.0005183028,0.00014483412,0.000606127,0.00033662323,0.00022094847],"domain_scores_gemma":[0.9978756,0.0006595066,0.00007482684,0.00051454396,0.00065303204,0.00022248809],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019660485,0.0035731767,0.0013441592,0.0017968845,0.0015598773,0.0025353753,0.0017512118,0.0025960084,0.025405267],"category_scores_gemma":[0.0050556576,0.0006377049,0.0019499265,0.0015720348,0.00048614043,0.0030987612,0.0033108944,0.0028495288,0.031571496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001532315,0.0012198508,0.0037026566,0.0012365906,0.00032610312,0.0007982037,0.000490032,0.006685028,0.028257977,0.0031337042,0.5420725,0.41054514],"study_design_scores_gemma":[0.0014913543,0.0012452648,0.019090507,0.0002838621,0.00042467107,0.0012552284,0.002145903,0.5250927,0.08309056,0.035955325,0.32965526,0.00026928043],"about_ca_topic_score_codex":0.008578118,"about_ca_topic_score_gemma":0.013543917,"teacher_disagreement_score":0.025405267,"about_ca_system_score_codex":0.0011969637,"about_ca_system_score_gemma":0.0014139276,"threshold_uncertainty_score":0.08498907},"labels":[],"label_agreement":null},{"id":"W6939267819","doi":"10.60692/qqq44-5kg60","title":"Semantic and Syntactic Enhanced Aspect Sentiment Triplet Extraction","year":2021,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Sentence; Inference; Pipeline (software); Word (group theory); ENCODE; Sentiment analysis; Exploit; Graph","score_opus":0.026643637118347902,"score_gpt":0.23647160005704995,"score_spread":0.20982796293870204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6939267819","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05877155,0.0007330723,0.9083788,0.0007426772,0.00032246477,0.00043193658,0.0067537776,0.011779444,0.012086319],"genre_scores_gemma":[0.44106218,0.00064043066,0.5235163,0.00045168647,0.00022384211,0.00033077493,0.023433616,0.0007286893,0.00961249],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999624,0.0000582534,0.000034681878,0.0001265441,0.00010894675,0.00004754681],"domain_scores_gemma":[0.9995679,0.00009903372,0.00006172505,0.00007271032,0.00017653003,0.000022166852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047401965,0.0014977996,0.00074397185,0.0018733703,0.00049697305,0.00085704605,0.0007587771,0.0007548929,0.003702506],"category_scores_gemma":[0.0017328251,0.00036541128,0.0013904892,0.001478221,0.00027380596,0.0021305133,0.0011663357,0.0012433934,0.00233189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004269868,0.0002797939,0.006251583,0.00061508385,0.00024821484,0.0010070199,0.00042481135,0.02243302,0.10832376,0.014841741,0.053174555,0.7919734],"study_design_scores_gemma":[0.00005581815,0.00018409446,0.0067324475,0.000072436866,0.00019435982,0.0005952176,0.00031445897,0.86526936,0.048760183,0.04516568,0.032577988,0.000078064615],"about_ca_topic_score_codex":0.0025415076,"about_ca_topic_score_gemma":0.00582068,"teacher_disagreement_score":0.003702506,"about_ca_system_score_codex":0.0005200008,"about_ca_system_score_gemma":0.00080619345,"threshold_uncertainty_score":0.012386084},"labels":[],"label_agreement":null},{"id":"W6939363225","doi":"10.60692/t3etk-caa16","title":"Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter","year":2020,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Latent Dirichlet allocation; Topic model; Coronavirus disease 2019 (COVID-19); Salient; Sentiment analysis; Social media; Coronavirus; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)","score_opus":0.2256852576089422,"score_gpt":0.29336896296785175,"score_spread":0.06768370535890955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6939363225","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9244136,0.00048874196,0.06777651,0.002140992,0.00011582741,0.00020125855,0.0013852515,0.00018527056,0.003292554],"genre_scores_gemma":[0.98558897,0.00016815485,0.012346294,0.00009809648,0.00011521876,0.0001262759,0.0009151842,0.000018853034,0.0006229752],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980268,0.00136055,0.00009326722,0.00026897652,0.0001186259,0.00013180963],"domain_scores_gemma":[0.9915902,0.007391055,0.00047974472,0.0001842572,0.0002376193,0.00011712423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035341764,0.0005199339,0.0005291485,0.001722086,0.00072301104,0.0019498114,0.000451492,0.00082107045,0.0010512196],"category_scores_gemma":[0.010465648,0.0002806473,0.0009301512,0.001438636,0.0005305009,0.002054461,0.0009571822,0.0011347294,0.00046235713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033265683,0.0017465014,0.37842607,0.0009843573,0.00094527984,0.00075008423,0.020609003,0.16071184,0.018091083,0.01994209,0.012671614,0.3817955],"study_design_scores_gemma":[0.000032676377,0.0001247974,0.043939266,0.00004999163,0.000075649645,0.00006348917,0.003028623,0.9414896,0.0013927115,0.0074412315,0.0023093754,0.000052728876],"about_ca_topic_score_codex":0.005583064,"about_ca_topic_score_gemma":0.0047671096,"teacher_disagreement_score":0.005583064,"about_ca_system_score_codex":0.0010166323,"about_ca_system_score_gemma":0.00052538223,"threshold_uncertainty_score":0.018690765},"labels":[],"label_agreement":null},{"id":"W6939412700","doi":"10.60692/z1evj-tbs32","title":"Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter","year":2020,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Latent Dirichlet allocation; Topic model; Coronavirus disease 2019 (COVID-19); Salient; Sentiment analysis; Social media; Coronavirus; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)","score_opus":0.2256852576089422,"score_gpt":0.29336896296785175,"score_spread":0.06768370535890955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6939412700","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9244136,0.00048874196,0.06777651,0.002140992,0.00011582741,0.00020125855,0.0013852515,0.00018527056,0.003292554],"genre_scores_gemma":[0.98558897,0.00016815485,0.012346294,0.00009809648,0.00011521876,0.0001262759,0.0009151842,0.000018853034,0.0006229752],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980268,0.00136055,0.00009326722,0.00026897652,0.0001186259,0.00013180963],"domain_scores_gemma":[0.9915902,0.007391055,0.00047974472,0.0001842572,0.0002376193,0.00011712423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035341764,0.0005199339,0.0005291485,0.001722086,0.00072301104,0.0019498114,0.000451492,0.00082107045,0.0010512196],"category_scores_gemma":[0.010465648,0.0002806473,0.0009301512,0.001438636,0.0005305009,0.002054461,0.0009571822,0.0011347294,0.00046235713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033265683,0.0017465014,0.37842607,0.0009843573,0.00094527984,0.00075008423,0.020609003,0.16071184,0.018091083,0.01994209,0.012671614,0.3817955],"study_design_scores_gemma":[0.000032676377,0.0001247974,0.043939266,0.00004999163,0.000075649645,0.00006348917,0.003028623,0.9414896,0.0013927115,0.0074412315,0.0023093754,0.000052728876],"about_ca_topic_score_codex":0.005583064,"about_ca_topic_score_gemma":0.0047671096,"teacher_disagreement_score":0.005583064,"about_ca_system_score_codex":0.0010166323,"about_ca_system_score_gemma":0.00052538223,"threshold_uncertainty_score":0.018690765},"labels":[],"label_agreement":null},{"id":"W6944229824","doi":"10.18148/zs/2025-2003","title":"Computational evaluation","year":2025,"lang":"en","type":"article","venue":"Open Journal Systems (Global Science & Technology Forum)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computational linguistics; Field (mathematics); Lexicon; Point (geometry); Computational model; Representation (politics); Conversation; Semantics (computer science)","score_opus":0.02163918612173248,"score_gpt":0.3562155238045112,"score_spread":0.3345763376827787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6944229824","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010891003,0.0029534386,0.6912027,0.009804413,0.0012235633,0.00091614097,0.0025013844,0.002401006,0.2781064],"genre_scores_gemma":[0.43141174,0.0029745826,0.48021898,0.0030093805,0.0009967358,0.002172591,0.007979108,0.0014161407,0.06982081],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.98750395,0.0056032427,0.0010905644,0.0019549013,0.0031214831,0.0007259087],"domain_scores_gemma":[0.9764622,0.013058857,0.0008563664,0.0048008743,0.0042671976,0.0005544356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009209418,0.0014007643,0.0013415854,0.0040462073,0.0016745317,0.008781994,0.0038246778,0.0021875654,0.057207074],"category_scores_gemma":[0.056995206,0.000516872,0.0018603145,0.0033430182,0.0026538153,0.010537187,0.0053368732,0.0020183416,0.009946335],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017105327,0.00008950426,0.0017962843,0.0005136615,0.000110368695,0.00011281267,0.00027458835,0.01950329,0.0004045413,0.6920808,0.044830497,0.24011265],"study_design_scores_gemma":[0.00005111771,0.000073808784,0.0006027233,0.00029459683,0.0000684492,0.00020932213,0.0002978637,0.088142134,0.00087830215,0.7898655,0.119475774,0.00004038333],"about_ca_topic_score_codex":0.0029641953,"about_ca_topic_score_gemma":0.0030099526,"teacher_disagreement_score":0.057207074,"about_ca_system_score_codex":0.0038219865,"about_ca_system_score_gemma":0.003484401,"threshold_uncertainty_score":0.19137675},"labels":[],"label_agreement":null},{"id":"W6949129192","doi":"10.5281/zenodo.11516190","title":"Text Sentiment Detection and Classification Based on Integrated Learning Algorithm","year":2024,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CanBiocin (Canada)","funders":"","keywords":"Sentiment analysis; Random forest; Voting; Classifier (UML); Support vector machine; Majority rule; Test set; Statistical classification","score_opus":0.030807825687346215,"score_gpt":0.2528390788650512,"score_spread":0.22203125317770497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6949129192","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07535745,0.00051919563,0.9184858,0.00020151425,0.00012973517,0.0002251783,0.00013808999,0.0021597522,0.0027832817],"genre_scores_gemma":[0.46748865,0.00033318115,0.52834547,0.00013126215,0.000109480185,0.0002811511,0.00068895594,0.00011507187,0.0025067185],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988864,0.00024913508,0.00011590244,0.00026481043,0.00037408204,0.00010970609],"domain_scores_gemma":[0.99875045,0.00036997735,0.00010616514,0.0000930382,0.00064517534,0.00003517316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016648805,0.0008252143,0.001152554,0.0020860506,0.00044222755,0.0012509002,0.0009542229,0.00083371915,0.0018514672],"category_scores_gemma":[0.003505342,0.00026277994,0.00082337635,0.001631159,0.0002689835,0.001788936,0.00067449064,0.0007470663,0.0011763524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057523214,0.00035534918,0.0071675833,0.00015317881,0.00018913743,0.00009292173,0.00013334259,0.036898166,0.028687194,0.0032598244,0.0032514585,0.91923654],"study_design_scores_gemma":[0.000020307101,0.00012961535,0.0016199354,0.000013603865,0.000041183164,0.00005933176,0.000032626413,0.9885239,0.0071355887,0.001510813,0.00090093614,0.0000122320125],"about_ca_topic_score_codex":0.0012674128,"about_ca_topic_score_gemma":0.0011355322,"teacher_disagreement_score":0.0020860506,"about_ca_system_score_codex":0.0004498762,"about_ca_system_score_gemma":0.00052244536,"threshold_uncertainty_score":0.008804798},"labels":[],"label_agreement":null},{"id":"W6958168519","doi":"10.60692/thprp-jwz90","title":"TEmoX: Classification of Textual Emotion Using Ensemble of Transformers","year":2023,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University","funders":"","keywords":"Categorization; Bengali; Classifier (UML); Transformer; Ensemble learning; Emotion classification; Emotion recognition; Embedding","score_opus":0.08252252987448386,"score_gpt":0.25750812056372185,"score_spread":0.174985590689238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958168519","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57611394,0.0019065938,0.4044474,0.0005620615,0.0004933671,0.00022656989,0.0016726389,0.0059751254,0.008602293],"genre_scores_gemma":[0.9500358,0.00045260857,0.03968657,0.00011649364,0.00006758588,0.00007606296,0.0026260947,0.000103260805,0.0068354732],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997843,0.000035609188,0.000014259651,0.00007191073,0.000049578313,0.000044368662],"domain_scores_gemma":[0.99971277,0.00008040266,0.000021695007,0.000032090094,0.00012871673,0.000024285624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059803517,0.00092842156,0.00047121546,0.00071025634,0.00023229625,0.00056799257,0.0005751309,0.00039570604,0.0014326825],"category_scores_gemma":[0.0010894223,0.00016797485,0.0007881138,0.00039463447,0.00016624712,0.0009960661,0.0005998985,0.00086736586,0.00093261374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010279418,0.00033533148,0.02480957,0.00013678575,0.00029664303,0.0002646863,0.00038736098,0.07409516,0.051132347,0.0016839084,0.010267153,0.8355632],"study_design_scores_gemma":[0.000014701882,0.00024651102,0.008295817,0.00001720382,0.00011558093,0.00011550897,0.00022613963,0.9716375,0.015834704,0.0011822891,0.0022895962,0.000024330924],"about_ca_topic_score_codex":0.0043213367,"about_ca_topic_score_gemma":0.005564144,"teacher_disagreement_score":0.0043213367,"about_ca_system_score_codex":0.0005038746,"about_ca_system_score_gemma":0.00036215255,"threshold_uncertainty_score":0.008592367},"labels":[],"label_agreement":null},{"id":"W6958257114","doi":"10.60692/r0bdb-f1n85","title":"WordNet Semantic Relations Based Enhancement of KNN Model for Implicit Aspect Identification in Sentiment Analysis","year":2023,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Overfitting; WordNet; Sentiment analysis; Task (project management); Key (lock); Identification (biology); Semantics (computer science); Computation","score_opus":0.045491757637183876,"score_gpt":0.2610148729722463,"score_spread":0.2155231153350624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958257114","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22388622,0.00081391307,0.7680279,0.00044464774,0.00018715163,0.00022440233,0.00049014494,0.0014363646,0.004489288],"genre_scores_gemma":[0.81887996,0.00028105231,0.1774536,0.00017189086,0.00010552538,0.00013054015,0.0010060834,0.000080953796,0.0018904759],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922943,0.0002179125,0.00007689537,0.00020239265,0.00019954731,0.000073752],"domain_scores_gemma":[0.9988865,0.00040116347,0.00011194253,0.00009045191,0.00046946315,0.00004042896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011106323,0.0008325736,0.0007449821,0.0017009897,0.00053871964,0.0008228014,0.0008704093,0.00073874596,0.0013647414],"category_scores_gemma":[0.0031756551,0.0002220191,0.0007429601,0.001257991,0.00030921033,0.0018558328,0.0006263357,0.0007699145,0.00064670236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070384267,0.0005957546,0.018966742,0.0002884049,0.00028772527,0.00031845685,0.0005051751,0.31366208,0.016871337,0.009925234,0.0065865493,0.6312887],"study_design_scores_gemma":[0.0000072100443,0.000031785672,0.0009248077,0.0000082218885,0.000018602106,0.000024608464,0.00003518085,0.99476385,0.0011839966,0.0025023636,0.00049343257,0.0000059695776],"about_ca_topic_score_codex":0.0092331655,"about_ca_topic_score_gemma":0.0136905275,"teacher_disagreement_score":0.0092331655,"about_ca_system_score_codex":0.0007057223,"about_ca_system_score_gemma":0.00073008786,"threshold_uncertainty_score":0.018358886},"labels":[],"label_agreement":null},{"id":"W6958397227","doi":"10.6084/m9.figshare.13003097","title":"Supplementary Material.pdf","year":2020,"lang":"en","type":"other","venue":"Figshare","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Process (computing); Natural (archaeology); Identification (biology)","score_opus":0.031900106254487554,"score_gpt":0.25943259451208134,"score_spread":0.22753248825759378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958397227","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004256813,0.00032411455,0.009954495,0.0013379299,0.0016999254,0.0002462549,0.6945066,0.06412889,0.22737613],"genre_scores_gemma":[0.00511201,0.00064986706,0.014619479,0.0015762777,0.00073306507,0.00064014026,0.47432235,0.074041456,0.42830533],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991134,0.00009986568,0.00006478793,0.00024133721,0.0003434149,0.00013722343],"domain_scores_gemma":[0.9954417,0.0014098221,0.00022148447,0.00089722435,0.0014523375,0.00057744706],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0014199006,0.0020382758,0.0013121032,0.0037945781,0.0014806308,0.0068002813,0.0032293377,0.0022430483,0.95028937],"category_scores_gemma":[0.01346008,0.0012007145,0.0018358023,0.004553515,0.00068972696,0.0052508353,0.0040025623,0.0015129211,0.8939342],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031729935,0.000010964046,0.00006629434,0.00019237775,0.0000065746794,0.000016679809,0.000011924922,0.000059847072,0.000083822364,0.00079663796,0.9846201,0.0141029535],"study_design_scores_gemma":[0.000069197835,0.000014311942,0.00049688824,0.000115068426,0.000010938533,0.000058648075,0.000030300462,0.00030352623,0.0006096487,0.0058729546,0.99238914,0.000029341312],"about_ca_topic_score_codex":0.0038852384,"about_ca_topic_score_gemma":0.0056336727,"teacher_disagreement_score":0.04971063,"about_ca_system_score_codex":0.0012464866,"about_ca_system_score_gemma":0.001437331,"threshold_uncertainty_score":0.0709061},"labels":[],"label_agreement":null},{"id":"W6959124210","doi":"10.1021/acs.est.4c08675.s001","title":"Unveilingthe Impact of Wildfires on NanoparticleCharacteristics and Exposure Disparities through Mobile and Fixed-SiteMonitoring in Toronto, Canada","year":2025,"lang":"en","type":"article","venue":"Figshare","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Immigration; Aerosol; Downtown; Particle (ecology); Climate change","score_opus":0.013134045091091395,"score_gpt":0.28433900743076906,"score_spread":0.27120496233967767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6959124210","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9865378,0.0007166527,0.0014963043,0.0008397602,0.000029000084,0.00003532366,0.0061864844,0.000019244486,0.004139387],"genre_scores_gemma":[0.9961659,0.0003263225,0.0007989708,0.00011251644,0.000012296294,0.000012552157,0.0012862206,0.0000062275085,0.0012789281],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997259,0.000045722132,0.000010162677,0.0000477969,0.000081223036,0.000089207155],"domain_scores_gemma":[0.99928397,0.00009462472,0.00012013473,0.00002951844,0.0003770215,0.00009478278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047346717,0.00021798753,0.00020638922,0.00057947,0.0010057138,0.00076400145,0.00035135722,0.00019794606,0.0015262201],"category_scores_gemma":[0.0011695988,0.00010718958,0.00023738798,0.0012651983,0.0003925749,0.0002954547,0.00049458863,0.00031374386,0.00014930245],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016923092,0.00003471084,0.952807,0.00014797553,0.0000977102,0.00024487905,0.0037930575,0.0010649612,0.0022648247,0.0007261791,0.006852134,0.031797357],"study_design_scores_gemma":[0.0000053633066,0.000026189358,0.9799349,0.000057669004,0.000056195044,0.000038057788,0.0077067027,0.003717432,0.00065319665,0.00016047318,0.007625319,0.000018504668],"about_ca_topic_score_codex":0.9639483,"about_ca_topic_score_gemma":0.9855924,"teacher_disagreement_score":0.03605169,"about_ca_system_score_codex":0.007899503,"about_ca_system_score_gemma":0.0071404525,"threshold_uncertainty_score":0.072527945},"labels":[],"label_agreement":null},{"id":"W6964531701","doi":"10.26226/morressier.5d9e3dea740457b65481ac70","title":"Quality of Life and Wellbeing Following Treatment for AML, and the Co-design of Community-based Care Plans","year":2017,"lang":"en","type":"other","venue":"BiblioBoard Library Catalog (Open Research Library)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Debriefing; Interim; CLARITY; Quality of life (healthcare); Relevance (law); Audit; Notice; Nice; Palliative care","score_opus":0.19472025219170344,"score_gpt":0.40722514808507326,"score_spread":0.21250489589336982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6964531701","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9156374,0.006035186,0.002023102,0.054849368,0.00037440725,0.0014179645,0.00023430097,0.000055948727,0.019372333],"genre_scores_gemma":[0.99221426,0.0014716308,0.003318079,0.0010817213,0.00007303927,0.0006113285,0.00012757277,0.0000071707254,0.0010951557],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9847515,0.011888919,0.00048364841,0.00042220927,0.0014658478,0.000987853],"domain_scores_gemma":[0.9846565,0.004768001,0.0026124483,0.00068834855,0.0017254949,0.0055492055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014554225,0.00019842206,0.00029975318,0.0008806037,0.0022767726,0.003425879,0.001054127,0.00057738606,0.003490818],"category_scores_gemma":[0.03293391,0.00017337709,0.0006579235,0.00064193923,0.0013163358,0.0010554227,0.004158457,0.0012251659,0.00018654976],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054536475,0.0040059304,0.25615057,0.0007361631,0.0002981563,0.0004082356,0.03789471,0.0008738727,0.000370897,0.004820512,0.020052902,0.67384267],"study_design_scores_gemma":[0.000860876,0.0042136814,0.71780574,0.0030961393,0.00039369203,0.001035854,0.13497148,0.004806976,0.0011880564,0.015432057,0.1159794,0.00021594825],"about_ca_topic_score_codex":0.014097327,"about_ca_topic_score_gemma":0.032996286,"teacher_disagreement_score":0.014554225,"about_ca_system_score_codex":0.0052836263,"about_ca_system_score_gemma":0.015639765,"threshold_uncertainty_score":0.076970994},"labels":[],"label_agreement":null},{"id":"W6967465700","doi":"10.5281/zenodo.10953122","title":"SENTIMENT ANALYSIS FOR CONSUMER BEHAVIOR PREDICTION","year":2024,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bell (Canada)","funders":"","keywords":"Sentiment analysis; Lexicon; Consumer confidence index; Variety (cybernetics); Transparency (behavior); Product (mathematics); Consumer behaviour; Analytics; Focus (optics)","score_opus":0.04417418093545444,"score_gpt":0.27699419930163055,"score_spread":0.2328200183661761,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6967465700","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12576832,0.012378442,0.7672121,0.0050997837,0.0022717381,0.001856329,0.027911445,0.009344095,0.04815779],"genre_scores_gemma":[0.6604857,0.00644726,0.28857037,0.001022271,0.0011803985,0.0013581661,0.023767846,0.00045194142,0.016716067],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987012,0.00044104987,0.000120438766,0.0002504447,0.00038622436,0.000100620186],"domain_scores_gemma":[0.99802136,0.0008597234,0.0003145918,0.00014811127,0.0005991174,0.000057075795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020729415,0.0011506282,0.0009230693,0.0023444258,0.00045351026,0.0014313754,0.00053261494,0.0007559792,0.008108397],"category_scores_gemma":[0.006340105,0.0003261471,0.001292727,0.0021077963,0.00026228157,0.0011571958,0.00069251953,0.0012287127,0.005837121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005480755,0.0004484932,0.032590702,0.0010303794,0.00043979083,0.00037293253,0.0004916305,0.026260654,0.012619159,0.012084841,0.0881502,0.82496315],"study_design_scores_gemma":[0.00006277471,0.00035956173,0.0416104,0.00045799266,0.00019449247,0.00033001628,0.0007685265,0.8384563,0.007924011,0.03742848,0.07226626,0.00014127287],"about_ca_topic_score_codex":0.0033654175,"about_ca_topic_score_gemma":0.0029123293,"teacher_disagreement_score":0.008108397,"about_ca_system_score_codex":0.0007231175,"about_ca_system_score_gemma":0.00061125145,"threshold_uncertainty_score":0.027125299},"labels":[],"label_agreement":null},{"id":"W6976749053","doi":"10.60692/w0n32-50780","title":"TEmoX: Classification of Textual Emotion Using Ensemble of Transformers","year":2023,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University","funders":"","keywords":"Categorization; Bengali; Classifier (UML); Transformer; Ensemble learning; Emotion classification; Emotion recognition; Embedding","score_opus":0.08252252987448386,"score_gpt":0.25750812056372185,"score_spread":0.174985590689238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6976749053","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57611394,0.0019065938,0.4044474,0.0005620615,0.0004933671,0.00022656989,0.0016726389,0.0059751254,0.008602293],"genre_scores_gemma":[0.9500358,0.00045260857,0.03968657,0.00011649364,0.00006758588,0.00007606296,0.0026260947,0.000103260805,0.0068354732],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997843,0.000035609188,0.000014259651,0.00007191073,0.000049578313,0.000044368662],"domain_scores_gemma":[0.99971277,0.00008040266,0.000021695007,0.000032090094,0.00012871673,0.000024285624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059803517,0.00092842156,0.00047121546,0.00071025634,0.00023229625,0.00056799257,0.0005751309,0.00039570604,0.0014326825],"category_scores_gemma":[0.0010894223,0.00016797485,0.0007881138,0.00039463447,0.00016624712,0.0009960661,0.0005998985,0.00086736586,0.00093261374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010279418,0.00033533148,0.02480957,0.00013678575,0.00029664303,0.0002646863,0.00038736098,0.07409516,0.051132347,0.0016839084,0.010267153,0.8355632],"study_design_scores_gemma":[0.000014701882,0.00024651102,0.008295817,0.00001720382,0.00011558093,0.00011550897,0.00022613963,0.9716375,0.015834704,0.0011822891,0.0022895962,0.000024330924],"about_ca_topic_score_codex":0.0043213367,"about_ca_topic_score_gemma":0.005564144,"teacher_disagreement_score":0.0043213367,"about_ca_system_score_codex":0.0005038746,"about_ca_system_score_gemma":0.00036215255,"threshold_uncertainty_score":0.008592367},"labels":[],"label_agreement":null},{"id":"W6977561539","doi":"10.6084/m9.figshare.c.5118003.v1","title":"Inter-rater agreement, sensitivity, and specificity of the prone hip extension test and active straight leg raise test","year":2020,"lang":"en","type":"other","venue":"Figshare","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Kappa; Straight leg raise; Asymptomatic; Test (biology); Range of motion; Observational study; Low back pain; Electromyography","score_opus":0.043167812663780655,"score_gpt":0.25308758320816543,"score_spread":0.20991977054438477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977561539","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9895293,0.0009080504,0.006479352,0.000044745913,0.00007483932,0.000231089,0.00024905577,0.000042964966,0.0024407515],"genre_scores_gemma":[0.9961372,0.0001222895,0.0032500052,0.000013396751,0.000022420341,0.00011949609,0.00014427568,0.000010082093,0.00018072492],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.94047636,0.038180936,0.006800924,0.0038636841,0.0097386325,0.0009395264],"domain_scores_gemma":[0.8423945,0.110949874,0.015957572,0.00652498,0.022740424,0.0014326973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.051650677,0.0006423527,0.0012410779,0.0027607344,0.0005005689,0.0013729281,0.001053637,0.0008551874,0.0010442148],"category_scores_gemma":[0.09667006,0.00046009864,0.001451122,0.0010113544,0.0013450987,0.0013012356,0.0016746394,0.0006915112,0.00042453845],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016312485,0.00014315348,0.97157073,0.0003200148,0.0014141642,0.00015108228,0.0033984976,0.00084656227,0.0023756379,0.00021695599,0.00039538872,0.01753647],"study_design_scores_gemma":[0.00013341018,0.0011781118,0.9770148,0.00018999749,0.00065910036,0.00092440855,0.0026762339,0.012200832,0.0037114585,0.0004732694,0.00073798286,0.00010045649],"about_ca_topic_score_codex":0.0012230206,"about_ca_topic_score_gemma":0.0015812599,"teacher_disagreement_score":0.051650677,"about_ca_system_score_codex":0.0003659136,"about_ca_system_score_gemma":0.00043014952,"threshold_uncertainty_score":0.27315813},"labels":[],"label_agreement":null},{"id":"W6979224450","doi":"","title":"A model for cholera with infectiousness of deceased individuals and vaccination","year":2025,"lang":"en","type":"article","venue":"ArXiv.org","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vaccination; Basic reproduction number; Transmission (telecommunications); Stability (learning theory); Cholera; Infectivity; Epidemic model; Bistability","score_opus":0.03247326056053726,"score_gpt":0.2885445356061398,"score_spread":0.25607127504560256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6979224450","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40552163,0.0029675777,0.4812711,0.013737405,0.0012005607,0.00048000243,0.007951369,0.00043198717,0.08643842],"genre_scores_gemma":[0.904724,0.0016826113,0.02790681,0.0009929973,0.00037918787,0.0005889843,0.0014969395,0.00006927318,0.06215909],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993581,0.00025601112,0.000033437795,0.00012406705,0.000077584256,0.00015081235],"domain_scores_gemma":[0.9975738,0.0014950346,0.00042351033,0.00008037652,0.00020223332,0.00022505551],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001163075,0.00087663636,0.0013220451,0.0006629539,0.0006632453,0.0015550199,0.0030187974,0.0035636686,0.00870758],"category_scores_gemma":[0.0043828236,0.00046025828,0.0012697653,0.00085375353,0.0010560965,0.0014244018,0.0011676007,0.0018118392,0.0014169451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029260755,0.00014809084,0.0028796836,0.00023348007,0.00009835001,0.0009832287,0.00048787578,0.7844649,0.0023044555,0.19412449,0.0061533283,0.007829513],"study_design_scores_gemma":[0.000100024896,0.00012688797,0.0007664152,0.000027869866,0.00004683626,0.00016808098,0.00012513789,0.9571489,0.00012607184,0.03759754,0.0037351935,0.000031023847],"about_ca_topic_score_codex":0.016331716,"about_ca_topic_score_gemma":0.008912361,"teacher_disagreement_score":0.016331716,"about_ca_system_score_codex":0.0018643707,"about_ca_system_score_gemma":0.0011073849,"threshold_uncertainty_score":0.032473266},"labels":[],"label_agreement":null},{"id":"W6986687478","doi":"","title":"Puissance Maximale - Emission 13 juin 2013","year":2013,"lang":"fr","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Battle; EPIC; Musical; Musical instrument","score_opus":0.010757066318330346,"score_gpt":0.19715273816016043,"score_spread":0.1863956718418301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6986687478","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03458713,0.014172272,0.017438618,0.036740627,0.051041886,0.000967382,0.05212348,0.011751176,0.7811774],"genre_scores_gemma":[0.04726522,0.0016940432,0.003470353,0.00162365,0.0029240476,0.00016799585,0.011422513,0.0012700023,0.93016225],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99821657,0.00012554563,0.000035693185,0.00026491415,0.0010055559,0.0003517725],"domain_scores_gemma":[0.99684465,0.00015037281,0.00008422402,0.00015410714,0.0019609593,0.0008056327],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0025016037,0.0010229313,0.0007389798,0.0017047898,0.0034569288,0.0049029323,0.0010745524,0.0016838154,0.14059673],"category_scores_gemma":[0.0034387999,0.00043243956,0.00059981307,0.00090065994,0.00082444365,0.0013570582,0.0024756088,0.0024607282,0.041096047],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006452276,0.00007796812,0.002749167,0.00024069662,0.000029592651,0.0003469818,0.00039294388,0.000412653,0.003759153,0.0048505296,0.91653115,0.06996402],"study_design_scores_gemma":[0.00002838181,0.000057145597,0.0079576215,0.000087779445,0.0000077569575,0.00005312604,0.00019768899,0.00031510813,0.0016019034,0.0006018489,0.9890689,0.000022622256],"about_ca_topic_score_codex":0.21157601,"about_ca_topic_score_gemma":0.4264013,"teacher_disagreement_score":0.85940325,"about_ca_system_score_codex":0.0068940204,"about_ca_system_score_gemma":0.0056827026,"threshold_uncertainty_score":0.47034293},"labels":[],"label_agreement":null},{"id":"W6987819074","doi":"","title":"Utilizing NLP Sentiment Analysis Approach to Categorize Amazon Reviews against an Extended Testing Set","year":2024,"lang":"en","type":"article","venue":"Global Society of Scientific Research and Researchers - International Journal of Computer","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Sentiment analysis; Random forest; Preprocessor; Categorization; Support vector machine; Bag-of-words model; Set (abstract data type); Feature (linguistics); Feature extraction; Product (mathematics)","score_opus":0.23965094842407286,"score_gpt":0.44321120196418534,"score_spread":0.20356025354011248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6987819074","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8990248,0.0007364457,0.066560425,0.00052352523,0.0002906862,0.0015666182,0.016640106,0.002231922,0.012425574],"genre_scores_gemma":[0.9032556,0.00017666642,0.07435103,0.00016931663,0.000116841904,0.0009929246,0.017570794,0.000100159144,0.003266693],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979582,0.00039722858,0.00030611487,0.0005065321,0.00073438184,0.00009757704],"domain_scores_gemma":[0.9936014,0.002521605,0.0007712627,0.00045659015,0.0025226825,0.00012654535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001966537,0.00095578335,0.0006190844,0.0031012886,0.00039931698,0.0009842297,0.0005186331,0.00042890193,0.002117752],"category_scores_gemma":[0.0097802365,0.000116641175,0.00069634744,0.0017800473,0.0002498716,0.0008171253,0.00063415716,0.00060095434,0.0021179607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017123767,0.0011583713,0.20095092,0.0012036384,0.0004922287,0.0013348167,0.00153068,0.010946325,0.069629796,0.0018000575,0.02841048,0.6808303],"study_design_scores_gemma":[0.0001456048,0.0018104566,0.34938443,0.00020266532,0.00031730032,0.0014488916,0.0037580635,0.5371829,0.06946796,0.0027806158,0.033355698,0.0001452977],"about_ca_topic_score_codex":0.004188361,"about_ca_topic_score_gemma":0.0065493933,"teacher_disagreement_score":0.004188361,"about_ca_system_score_codex":0.00058623793,"about_ca_system_score_gemma":0.00057477277,"threshold_uncertainty_score":0.010400176},"labels":[],"label_agreement":null},{"id":"W6991711041","doi":"","title":"Identifying and Analyzing Provocative Text: An XAI Approach to Classification and Feature Selection","year":2025,"lang":"en","type":"article","venue":"DiVA at Umeå University (Umeå University)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Ambiguity; Bayesian probability; Feature selection; Feature (linguistics); Task (project management); Model selection; Naive Bayes classifier; Discriminator; Bayesian inference","score_opus":0.021842790413429753,"score_gpt":0.23540694965456876,"score_spread":0.213564159241139,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6991711041","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04857995,0.0013401638,0.9403104,0.0014503372,0.00012093044,0.00040744178,0.0006980302,0.0016105094,0.0054821614],"genre_scores_gemma":[0.5632459,0.0007719923,0.42577025,0.00051588565,0.00040453844,0.0006953483,0.0022894745,0.0001453591,0.0061612083],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965485,0.001388684,0.0002382756,0.00059990573,0.0010219763,0.00020263984],"domain_scores_gemma":[0.9960763,0.0023372243,0.00028747477,0.00046923928,0.0007326175,0.00009709515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048808083,0.0012215262,0.0010825435,0.006185394,0.00081990624,0.002834889,0.0016432179,0.0010524467,0.0024784375],"category_scores_gemma":[0.00686305,0.00027508446,0.0013882701,0.0031700027,0.0008920944,0.001828017,0.0014487671,0.0018923421,0.0012896181],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035903,0.0006589792,0.021738375,0.00030631106,0.00032383113,0.00024669207,0.00070946064,0.022356363,0.009278375,0.0139049385,0.007600111,0.92251766],"study_design_scores_gemma":[0.000050946666,0.0005592658,0.016213493,0.00011742383,0.00014555549,0.00048451818,0.0011733804,0.9134659,0.011412226,0.045606997,0.010688316,0.000081944876],"about_ca_topic_score_codex":0.0021654437,"about_ca_topic_score_gemma":0.0023486319,"teacher_disagreement_score":0.006185394,"about_ca_system_score_codex":0.0011014852,"about_ca_system_score_gemma":0.0009094996,"threshold_uncertainty_score":0.025812447},"labels":[],"label_agreement":null},{"id":"W6993946052","doi":"","title":"#Emotional Tweets","year":2012,"lang":"nl","type":"article","venue":"National Research Council Canada (Government of Canada)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Lexicon; Microblogging; Social media; WordNet; Affect (linguistics); Association (psychology)","score_opus":0.15163965597923665,"score_gpt":0.30890784203079863,"score_spread":0.15726818605156198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6993946052","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18422407,0.0014535108,0.04046927,0.004049724,0.0032175544,0.0027424863,0.48775825,0.0097750155,0.2663101],"genre_scores_gemma":[0.3621632,0.0017203653,0.06369332,0.0016153555,0.0013293566,0.0038632657,0.38087782,0.0021908574,0.18254642],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989467,0.00018600092,0.0001633108,0.00016896358,0.00040688142,0.00012821566],"domain_scores_gemma":[0.9983571,0.00047004383,0.00019206302,0.00018748027,0.0006764304,0.00011682487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005714208,0.0007641074,0.00034238628,0.0022943693,0.0009733629,0.0013583623,0.00037860882,0.0005161392,0.04606392],"category_scores_gemma":[0.0037715826,0.00028763787,0.00033069265,0.0017484074,0.00016263821,0.0012333216,0.0011468475,0.00057344855,0.03230467],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00093943614,0.0002705002,0.039638,0.0019598491,0.00008584325,0.0008405818,0.002100618,0.0011283382,0.031246433,0.010805058,0.5131878,0.3977975],"study_design_scores_gemma":[0.00006497388,0.00015436986,0.054331895,0.00018057649,0.00008261341,0.0008095216,0.0013814182,0.0077623925,0.019838596,0.0054101776,0.9099046,0.00007880547],"about_ca_topic_score_codex":0.0014916018,"about_ca_topic_score_gemma":0.0030990397,"teacher_disagreement_score":0.04606392,"about_ca_system_score_codex":0.00046623035,"about_ca_system_score_gemma":0.00041893424,"threshold_uncertainty_score":0.15409923},"labels":[],"label_agreement":null},{"id":"W6994278526","doi":"","title":"Congregation","year":2014,"lang":"en","type":"other","venue":"Digital Commons - ACU (Abilene Christian University)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Front (military); Front cover; Period (music); Front line; Quarter (Canadian coin)","score_opus":0.010669589496062584,"score_gpt":0.1991643995192843,"score_spread":0.18849481002322171,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6994278526","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037453286,0.0012748545,0.0006872355,0.0090354225,0.014334737,0.00014797636,0.007575063,0.0009999875,0.9621994],"genre_scores_gemma":[0.009799774,0.00045965187,0.00033551553,0.0012005039,0.0007496283,0.00006939275,0.0020411008,0.00035283036,0.9849917],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996282,0.000045535344,0.000009539258,0.00008399076,0.000118395816,0.00011434001],"domain_scores_gemma":[0.99882,0.000053786895,0.000046674522,0.00010543585,0.00029107468,0.0006829094],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004244915,0.00070168375,0.00041871646,0.0005929487,0.004481874,0.0030657228,0.000649967,0.00069262553,0.5410882],"category_scores_gemma":[0.0018617114,0.00028094044,0.00030628222,0.00063965155,0.00048876036,0.0016067122,0.002971941,0.0020318544,0.2555375],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015829366,0.000004601392,0.00008051997,0.000019260082,0.0000010173204,0.00003729184,0.00015700991,0.0000068243635,0.00010276155,0.0008182328,0.9909966,0.0077600963],"study_design_scores_gemma":[0.0000015608007,0.000005688601,0.00054349226,0.00001153016,7.131304e-7,0.00003603665,0.0002839107,0.0000045248025,0.000031836553,0.00005005336,0.99902797,0.0000027278827],"about_ca_topic_score_codex":0.008580912,"about_ca_topic_score_gemma":0.036226712,"teacher_disagreement_score":0.5410882,"about_ca_system_score_codex":0.0012352502,"about_ca_system_score_gemma":0.00064295443,"threshold_uncertainty_score":0.6545819},"labels":[],"label_agreement":null},{"id":"W7008925451","doi":"","title":"Compositional Matrix-Space Models: Learning Methods and Evaluation","year":2020,"lang":"en","type":"other","venue":"Qucosa (Saxon State and University Library Dresden)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Technische Universität Darmstadt; Atomic Energy of Canada Limited; McGill University; Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México; University of Maryland, Baltimore County","keywords":"Representation (politics); Property (philosophy); Meaning (existential); Nasalization; Feature (linguistics); Word (group theory)","score_opus":0.03127872511455307,"score_gpt":0.2854640948697017,"score_spread":0.2541853697551486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7008925451","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027807813,0.015378316,0.94472754,0.0016460405,0.0006445907,0.0006922681,0.0013230217,0.0025881468,0.005192239],"genre_scores_gemma":[0.38544336,0.0090880785,0.5895206,0.00083499664,0.00074144773,0.0018498341,0.006527712,0.0010516512,0.004942337],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9890261,0.006559594,0.00058320566,0.0011499964,0.0023743347,0.00030673106],"domain_scores_gemma":[0.96201897,0.027547842,0.0013061644,0.0028490783,0.0056296494,0.00064845546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019952673,0.0031834443,0.0030928277,0.0043784226,0.0011598582,0.0035897186,0.0040393802,0.003931361,0.0085146995],"category_scores_gemma":[0.062326126,0.00082007697,0.0019363232,0.0042019906,0.0015917524,0.007585927,0.0037665532,0.004352704,0.002656563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012208831,0.0007376179,0.0039972654,0.0012002002,0.0006122723,0.00008203581,0.00017947445,0.45903122,0.0007809766,0.042917907,0.021254767,0.4679854],"study_design_scores_gemma":[0.00003122507,0.00009735448,0.00017954725,0.000051741423,0.000030362233,0.000019423973,0.000027465647,0.98387736,0.00025840898,0.01455594,0.0008555258,0.000015609403],"about_ca_topic_score_codex":0.014314044,"about_ca_topic_score_gemma":0.008407116,"teacher_disagreement_score":0.019952673,"about_ca_system_score_codex":0.0033378208,"about_ca_system_score_gemma":0.0024633668,"threshold_uncertainty_score":0.10552108},"labels":[],"label_agreement":null},{"id":"W7009752449","doi":"","title":"Episode 186 - LORETTA LYNN - ANNE MURRAY - GRADY L.","year":2021,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Front (military); Center (category theory); Microphone; Front line","score_opus":0.008991594707917043,"score_gpt":0.20240521945193615,"score_spread":0.1934136247440191,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7009752449","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019936156,0.001275964,0.0003833441,0.025097694,0.0063571236,0.0001604601,0.0059355046,0.00066071947,0.95813566],"genre_scores_gemma":[0.0031352951,0.00023640123,0.00007227061,0.0026614626,0.00037824287,0.000029121542,0.0009938106,0.00023713462,0.99225616],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995407,0.000043516942,0.000010521544,0.00006115828,0.00019424906,0.0001499102],"domain_scores_gemma":[0.9989587,0.000063036816,0.000038940514,0.00004092241,0.00031422492,0.0005840724],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00047059802,0.0004759595,0.00038480808,0.00057716767,0.0057851756,0.00410649,0.000812947,0.0021988177,0.4965534],"category_scores_gemma":[0.002728289,0.0003043767,0.0002807747,0.0007976901,0.0004369775,0.0024496312,0.0034140912,0.0029455395,0.31066713],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000076171286,0.0000038036915,0.00005746342,0.000010212012,2.7501238e-7,0.00003522091,0.00009451465,0.0000026967195,0.000033245924,0.00030843768,0.99551207,0.00393443],"study_design_scores_gemma":[0.0000013929478,0.00000388693,0.00041342218,0.000020338592,3.2451112e-7,0.000032912736,0.00036738158,0.000006982203,0.00003539804,0.000122048084,0.99899405,0.0000017919267],"about_ca_topic_score_codex":0.04954862,"about_ca_topic_score_gemma":0.23364535,"teacher_disagreement_score":0.5034466,"about_ca_system_score_codex":0.0026286934,"about_ca_system_score_gemma":0.001991757,"threshold_uncertainty_score":0.71810544},"labels":[],"label_agreement":null},{"id":"W7016138621","doi":"","title":"Young Offenders? The Case For The Cultural Value Of Recent Architectural Heritage","year":2024,"lang":"en","type":"article","venue":"Document Server@UHasselt (UHasselt)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Value (mathematics); Architecture; Objectivity (philosophy); Context (archaeology); Cultural heritage; Materiality (auditing); Bridging (networking)","score_opus":0.03707178024367652,"score_gpt":0.3142648823698316,"score_spread":0.27719310212615506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7016138621","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07346095,0.010058706,0.00036154038,0.8472971,0.0017222733,0.000025777665,0.000048643687,0.000011344969,0.06701367],"genre_scores_gemma":[0.7162081,0.008387545,0.00032188641,0.2518406,0.0021533398,0.00007730315,0.000026139038,0.000045064735,0.02094006],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.99307597,0.0029036447,0.00016647203,0.00057335815,0.0009765134,0.0023040422],"domain_scores_gemma":[0.98272943,0.0076576937,0.0017591326,0.0004901436,0.0013824061,0.0059812646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007501732,0.00026585057,0.0006293512,0.0009967136,0.019547544,0.011485253,0.0020138456,0.015767474,0.008366248],"category_scores_gemma":[0.01878628,0.0005343003,0.0003505457,0.0009921858,0.029565696,0.010723142,0.0088084815,0.02141017,0.0008672638],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000098602264,0.000118060656,0.016270835,0.00019376527,0.000026871787,0.007592178,0.18069449,0.00007163398,0.00017079484,0.60070825,0.15200824,0.042046193],"study_design_scores_gemma":[0.000050045215,0.000095905314,0.00941145,0.002460673,0.00004042617,0.007174015,0.35473818,0.00020693197,0.00029230898,0.114456154,0.5109661,0.00010784242],"about_ca_topic_score_codex":0.03341883,"about_ca_topic_score_gemma":0.06320468,"teacher_disagreement_score":0.03341883,"about_ca_system_score_codex":0.0066350633,"about_ca_system_score_gemma":0.0118251275,"threshold_uncertainty_score":0.06644863},"labels":[],"label_agreement":null},{"id":"W7021073977","doi":"","title":"2005 National Construction Codes now available","year":2005,"lang":"en","type":"article","venue":"NPARC","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Key (lock); Code (set theory); Table (database); Government (linguistics)","score_opus":0.021639825650346084,"score_gpt":0.25474086508568816,"score_spread":0.23310103943534208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7021073977","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0156864,0.0030907122,0.016939765,0.011303609,0.011820796,0.00073861907,0.16045216,0.0035672437,0.7764007],"genre_scores_gemma":[0.030325532,0.0032029662,0.029893184,0.00303622,0.0006761072,0.0012515681,0.15233219,0.0011213283,0.778161],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9951568,0.00028097953,0.00035911068,0.00023589357,0.0034829397,0.00048430386],"domain_scores_gemma":[0.98458546,0.00071505544,0.00086833304,0.0008188591,0.012274725,0.0007376035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020826417,0.0006534449,0.00047783292,0.0071382863,0.0019571222,0.0032827894,0.001192972,0.0012706515,0.07604824],"category_scores_gemma":[0.012740328,0.00039531657,0.0004665907,0.010309809,0.0006134482,0.0020190428,0.0014463685,0.0028189458,0.055516314],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000101569785,0.00003739941,0.0016072321,0.0002286526,0.000004255658,0.00008571055,0.00020838084,0.0003043523,0.00055790815,0.024739197,0.89232725,0.07979801],"study_design_scores_gemma":[0.000004045813,0.000008409255,0.0023523716,0.00005780729,0.0000017925728,0.00002704244,0.0000717364,0.00008849565,0.00017090386,0.0005886242,0.996617,0.000011716029],"about_ca_topic_score_codex":0.09139818,"about_ca_topic_score_gemma":0.12352712,"teacher_disagreement_score":0.09139818,"about_ca_system_score_codex":0.005558867,"about_ca_system_score_gemma":0.013547548,"threshold_uncertainty_score":0.2544067},"labels":[],"label_agreement":null},{"id":"W7021353728","doi":"","title":"The Nova Southeastern Lawyer, Summer 2002, Volume 12, Number 2","year":2002,"lang":"en","type":"article","venue":"NSUWorks (Nova Southeastern University)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Volume (thermodynamics); Nova (rocket); Nova scotia; Field (mathematics)","score_opus":0.04539630942707246,"score_gpt":0.23082407135345362,"score_spread":0.18542776192638116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7021353728","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0069042677,0.011071238,0.0010226481,0.09381007,0.008940774,0.00025101623,0.004404731,0.0009375401,0.8726577],"genre_scores_gemma":[0.0069067245,0.0036282435,0.0007248398,0.0038226272,0.0007670406,0.00004665931,0.0009869355,0.000142175,0.9829748],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993492,0.00007898402,0.000037263537,0.00006963844,0.000391796,0.000073059426],"domain_scores_gemma":[0.9983368,0.0003247391,0.00008323582,0.00007374904,0.00066763494,0.0005138396],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010288444,0.00029470105,0.00030676555,0.000945492,0.003842084,0.0056078634,0.00047209734,0.0012538568,0.29506934],"category_scores_gemma":[0.0043554516,0.0002669712,0.0001671356,0.0012881316,0.00057908165,0.002104201,0.0015110371,0.0018079,0.10456018],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008182147,0.000017950555,0.0004935862,0.000023195167,0.0000011261548,0.00003751224,0.00008401354,0.0000096651365,0.000068409696,0.001097316,0.96858317,0.029575815],"study_design_scores_gemma":[0.0000030743033,0.0000066477883,0.0021949736,0.0000769252,0.0000014711277,0.000027266406,0.0002890595,0.000040245253,0.000057705758,0.0004601834,0.99683905,0.000003487069],"about_ca_topic_score_codex":0.019560855,"about_ca_topic_score_gemma":0.14458817,"teacher_disagreement_score":0.70493066,"about_ca_system_score_codex":0.0019413827,"about_ca_system_score_gemma":0.003280868,"threshold_uncertainty_score":0.98710525},"labels":[],"label_agreement":null},{"id":"W7022254492","doi":"","title":"kinesis_2000_03_11","year":2000,"lang":"en","type":"other","venue":"cIRcle (University of British Columbia)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"","score_opus":0.007657022769772844,"score_gpt":0.17268488366945609,"score_spread":0.16502786089968324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7022254492","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026012985,0.00059656886,0.000769256,0.0016577695,0.0009022941,0.00011189544,0.050566774,0.0028079492,0.9399863],"genre_scores_gemma":[0.0030707447,0.0004042325,0.00040849968,0.0001548822,0.00006884737,0.000018895778,0.017194534,0.00045756402,0.9782219],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996592,0.000013454466,0.00001207731,0.000050649815,0.0001984551,0.00006626349],"domain_scores_gemma":[0.99812335,0.00012618049,0.00005527383,0.00009145563,0.0011804898,0.00042317217],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00045699388,0.0007413978,0.00041886437,0.0035487404,0.0023621675,0.006094621,0.00071132183,0.00070863374,0.40867755],"category_scores_gemma":[0.0016695602,0.00028156445,0.00024729298,0.005840033,0.0005603883,0.0013760116,0.0008669812,0.00076672883,0.24612704],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043339,0.000021170668,0.00061946985,0.00015108316,0.0000028272682,0.000057634777,0.00014433307,0.00007073161,0.00044126005,0.0019124664,0.9276851,0.06885055],"study_design_scores_gemma":[0.0000060550296,0.000005710622,0.003098826,0.000043185395,0.000002511348,0.00002526288,0.00016410495,0.00014957166,0.00029871755,0.00027617955,0.9959234,0.0000065846057],"about_ca_topic_score_codex":0.34681493,"about_ca_topic_score_gemma":0.68802947,"teacher_disagreement_score":0.5913224,"about_ca_system_score_codex":0.005498563,"about_ca_system_score_gemma":0.0059372378,"threshold_uncertainty_score":0.8434497},"labels":[],"label_agreement":null},{"id":"W7022496887","doi":"","title":"Montréal","year":2020,"lang":"fr","type":"other","venue":"EspaceINRS (National Institute for Scientific Research (Canada))","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Process (computing); Identification (biology); Product (mathematics)","score_opus":0.07635053009160789,"score_gpt":0.32476877234569707,"score_spread":0.24841824225408918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7022496887","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00051860156,0.0018380691,0.00085680233,0.0011662572,0.00069095555,0.000029555351,0.003971697,0.00047777133,0.9904503],"genre_scores_gemma":[0.0013063571,0.0005836114,0.0004364337,0.00014313473,0.00003842395,0.00001315935,0.00093104073,0.00012867816,0.9964193],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914646,0.00006279927,0.000021021002,0.00029358506,0.0003230141,0.00015320907],"domain_scores_gemma":[0.9993487,0.0000687569,0.000034147415,0.00008595095,0.0003246268,0.00013778087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000388235,0.0015720789,0.0006223515,0.001970842,0.0032490229,0.005444589,0.001258631,0.0013718461,0.7714208],"category_scores_gemma":[0.0013230804,0.00048248377,0.00060370966,0.0025360265,0.0006494289,0.0014577023,0.0017467645,0.001478447,0.47753873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007639236,0.000050622308,0.0006353654,0.00017060182,0.00001690289,0.0002567483,0.00016899579,0.0004048979,0.000913373,0.046253286,0.75898,0.19207287],"study_design_scores_gemma":[0.000005907379,0.0000050738254,0.0005369134,0.000044566903,0.0000034682348,0.000046723755,0.000055910612,0.000066122826,0.00014339841,0.0009964745,0.99809104,0.00000437922],"about_ca_topic_score_codex":0.2758952,"about_ca_topic_score_gemma":0.52633286,"teacher_disagreement_score":0.7714208,"about_ca_system_score_codex":0.0052115703,"about_ca_system_score_gemma":0.0057882485,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W7023540001","doi":"","title":"Organizational and pedagogical conditions of academic mobility development of students at school of higher professional education","year":2015,"lang":"en","type":"other","venue":"zvestiya of the National Academy of Sciences of Belarus (National Academy of Sciences of Belarus)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Nucleofection; TSG101; Gestational period; Hyporeflexia; Articular cartilage damage; Demotion; Diafiltration; Pretext","score_opus":0.11839070432194816,"score_gpt":0.42409798004941723,"score_spread":0.3057072757274691,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7023540001","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9692063,0.00017415854,0.00036169591,0.0010765209,0.000019246923,0.000031809166,0.00020608537,0.000005579305,0.028918665],"genre_scores_gemma":[0.9991555,0.000057208264,0.000056034067,0.000010728018,0.0000051816287,0.0000050746467,0.000040981988,7.4848185e-7,0.00066840876],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99892694,0.0003382708,0.00006750998,0.00010565349,0.000258238,0.00030332519],"domain_scores_gemma":[0.9971976,0.0005192251,0.0008177755,0.00010651953,0.00063089404,0.00072797266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010983219,0.00009905784,0.00011606402,0.0010598687,0.0015674968,0.0027135347,0.00030751433,0.0003107257,0.005309102],"category_scores_gemma":[0.003841509,0.000054083088,0.00013880609,0.0010131568,0.0010741543,0.00085219956,0.0013528623,0.00027924607,0.00032827648],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027991502,0.0003141451,0.852927,0.00015044713,0.000038254613,0.0008026889,0.036623605,0.0006695705,0.002440777,0.02471091,0.0065668533,0.07447585],"study_design_scores_gemma":[0.000008227315,0.00005758914,0.90734386,0.00005359417,0.000014951017,0.00012453852,0.07651783,0.0011436209,0.00051086023,0.0041317283,0.010078266,0.00001505943],"about_ca_topic_score_codex":0.018253239,"about_ca_topic_score_gemma":0.019324811,"teacher_disagreement_score":0.018253239,"about_ca_system_score_codex":0.0033357432,"about_ca_system_score_gemma":0.0033229017,"threshold_uncertainty_score":0.036293983},"labels":[],"label_agreement":null},{"id":"W7024184145","doi":"","title":"The Real Plan Policy Blueprint: Democratic Reform in Alberta (Part 1)","year":2006,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Cabinet (room); Plan (archaeology); Legislature; Democracy; Advisory committee; Social democracy","score_opus":0.006586559944486706,"score_gpt":0.19565752193187155,"score_spread":0.18907096198738485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024184145","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028041463,0.017555246,0.0015110632,0.1502617,0.0042939354,0.00027593112,0.0023941654,0.00037901834,0.7952875],"genre_scores_gemma":[0.124017924,0.0060393345,0.0022078087,0.010917467,0.0004060468,0.000068209505,0.0014803216,0.00008547274,0.85477734],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99889153,0.00010179537,0.000013841417,0.00006110673,0.00053485826,0.00039686498],"domain_scores_gemma":[0.9991142,0.000089530455,0.00003068524,0.000045898076,0.00027188353,0.0004476604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012433769,0.00029398742,0.00017743593,0.00084517535,0.0056938976,0.0056917686,0.00089157047,0.0023394579,0.029277205],"category_scores_gemma":[0.0017818672,0.00018955414,0.00018483725,0.0026517464,0.0018867735,0.001284762,0.0021055469,0.0019441379,0.0017601646],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003846706,0.00004037746,0.0018496157,0.00012257679,0.0000053989006,0.0002406235,0.00237073,0.00076230435,0.00046144822,0.09296378,0.7987081,0.10243666],"study_design_scores_gemma":[0.000009696422,0.000009818564,0.010781106,0.00007098233,0.000003256458,0.000030761523,0.0020949417,0.00023813763,0.00019832619,0.004145942,0.98240227,0.000014893143],"about_ca_topic_score_codex":0.92718303,"about_ca_topic_score_gemma":0.9706566,"teacher_disagreement_score":0.07281697,"about_ca_system_score_codex":0.040706135,"about_ca_system_score_gemma":0.07425718,"threshold_uncertainty_score":0.29534507},"labels":[],"label_agreement":null},{"id":"W7025252875","doi":"","title":"@USA-Canada-fb-helpline-number!@!@1-844-762-8448@facebook technical support phone number","year":2016,"lang":"en","type":"other","venue":"OSF Preprints (OSF Preprints)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Phone; Cyberpsychology; Social media; Helpline; Service (business); Phone call","score_opus":0.013895312027394758,"score_gpt":0.27185641029982494,"score_spread":0.2579610982724302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7025252875","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00056225585,0.000093446484,0.000207565,0.0006279665,0.00057366665,0.000099203455,0.0023315283,0.0023128868,0.99319154],"genre_scores_gemma":[0.0006481821,0.000042501037,0.000103646795,0.000217882,0.000033285844,0.000026469039,0.0005741373,0.00038813264,0.9979658],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99939823,0.000038531987,0.0000105303025,0.00008389195,0.00028806896,0.00018081337],"domain_scores_gemma":[0.99655247,0.00020676374,0.000062388346,0.00023955616,0.0016129189,0.001325906],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00031327188,0.0012672088,0.00082484755,0.0015254405,0.005171844,0.0036600395,0.0012028338,0.0021914586,0.95276296],"category_scores_gemma":[0.0025665627,0.0007493511,0.00065861206,0.0011483933,0.00052321807,0.0021067243,0.0025611985,0.001489989,0.93447083],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011210039,0.00003958862,0.00019278184,0.00001791518,0.0000011760153,0.000017868513,0.000024146279,0.000016283559,0.00008651751,0.00029027078,0.9803019,0.01900033],"study_design_scores_gemma":[0.000014135205,0.000015446922,0.00085024093,0.000040167382,0.0000029491036,0.000024010948,0.00019489355,0.00007525046,0.00013595917,0.00018401575,0.9984529,0.000010133073],"about_ca_topic_score_codex":0.11078115,"about_ca_topic_score_gemma":0.32804024,"teacher_disagreement_score":0.11078115,"about_ca_system_score_codex":0.002854256,"about_ca_system_score_gemma":0.006150117,"threshold_uncertainty_score":0.22027266},"labels":[],"label_agreement":null},{"id":"W7026647022","doi":"","title":"Automatic classification of fish and bubbles at pixel-level precision in multi-frequency acoustic echograms using U-Net convolutional neural networks","year":2022,"lang":"en","type":"dissertation","venue":"UVic’s Research and Learning Repository (University of Victoria)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; ENCODE; Underwater; Echo sounding; Identification (biology); Task (project management); Backscatter (email); Artificial neural network","score_opus":0.06537945165340468,"score_gpt":0.313299565225359,"score_spread":0.24792011357195431,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7026647022","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.753902,0.0004862678,0.23272513,0.00024408931,0.00017172175,0.00015995973,0.002305078,0.0060824174,0.0039233705],"genre_scores_gemma":[0.8388794,0.00024825084,0.15083714,0.00014841715,0.000034831362,0.00009921623,0.0037731535,0.00014798946,0.0058315317],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998024,0.000009453816,0.000009103385,0.000085473665,0.00003578864,0.00005781739],"domain_scores_gemma":[0.99972385,0.00005989016,0.000033872035,0.000038012597,0.000120673445,0.000023602604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032100474,0.0006768165,0.0003800057,0.00088338327,0.00022882236,0.0006541013,0.0007592081,0.00055166514,0.001079061],"category_scores_gemma":[0.0006818115,0.00027008902,0.00045090815,0.00047421173,0.00022968273,0.0005914284,0.0005588647,0.00053892686,0.0006743126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005598588,0.00031650052,0.02989253,0.00014787208,0.00012117649,0.00033743784,0.000258033,0.07308392,0.1988586,0.001033331,0.00718558,0.68820524],"study_design_scores_gemma":[0.000009604222,0.00005026569,0.01895717,0.000019333609,0.00003470895,0.000044198765,0.00007316001,0.9410808,0.037627704,0.00052682817,0.0015584978,0.000017668559],"about_ca_topic_score_codex":0.027510982,"about_ca_topic_score_gemma":0.052491937,"teacher_disagreement_score":0.027510982,"about_ca_system_score_codex":0.0008639852,"about_ca_system_score_gemma":0.0006067282,"threshold_uncertainty_score":0.054701686},"labels":[],"label_agreement":null},{"id":"W7039085820","doi":"","title":"La signalisation des rues dans les villes africaines et en Israël / Palestine Street signage in urban Africa and Israel/Palestine","year":2017,"lang":"fr","type":"other","venue":"OpenEdition (OpenEdition)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Palestine; Tourism; Chinatown; Quarter (Canadian coin); Urban tourism","score_opus":0.03921260406646865,"score_gpt":0.271609132789016,"score_spread":0.23239652872254735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7039085820","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98396444,0.00046841596,0.0005668594,0.0005672704,0.00006834845,0.000020713514,0.0044318084,0.000026167689,0.009886005],"genre_scores_gemma":[0.9894744,0.00033075805,0.0006656181,0.000041250096,0.000031674936,0.000015925336,0.0021039518,0.000013426319,0.007322896],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99975747,0.00007292322,0.00001415146,0.000037292702,0.000050246603,0.0000677882],"domain_scores_gemma":[0.999119,0.00034832006,0.00018887664,0.000023703691,0.00022543677,0.000094615505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004062914,0.0001502347,0.00011812301,0.0012193879,0.00060805184,0.0011271498,0.00020645594,0.00036892184,0.0039579123],"category_scores_gemma":[0.0017467147,0.00009464078,0.00015895405,0.0018442211,0.00026394802,0.0006841801,0.0005655438,0.0004544069,0.0005152026],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00060887745,0.00009216727,0.7986957,0.0005952807,0.00010701058,0.0021602581,0.04118622,0.0015945769,0.010347428,0.002922322,0.024887439,0.116802715],"study_design_scores_gemma":[0.000006787801,0.000045867215,0.93974066,0.00014797522,0.000023389714,0.0002304918,0.03404333,0.0012958583,0.0010540249,0.00014808279,0.023247983,0.000015642085],"about_ca_topic_score_codex":0.08871462,"about_ca_topic_score_gemma":0.17366126,"teacher_disagreement_score":0.08871462,"about_ca_system_score_codex":0.00064396235,"about_ca_system_score_gemma":0.00049601967,"threshold_uncertainty_score":0.17639649},"labels":[],"label_agreement":null},{"id":"W7042488585","doi":"","title":"Population Genetic Investigation of the White-Nose Syndrome pathogen, Pseudogymonascus destructans, in North America","year":2020,"lang":"en","type":"dissertation","venue":"MacSphere (McMaster University)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Epizootic; Outbreak; Population; Genotyping; Adaptation (eye); Transmission (telecommunications); Genetic variation","score_opus":0.011007401858649289,"score_gpt":0.19067992378857007,"score_spread":0.17967252192992078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7042488585","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99926203,0.000064313856,0.00021236157,0.000030162506,0.0000032073772,0.000010775367,0.0001137683,0.000002251925,0.00030117453],"genre_scores_gemma":[0.99870455,0.0001414224,0.00054715876,0.000045407272,0.000004536955,0.000019015633,0.00023981012,0.0000026029722,0.00029552222],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997404,0.000046607307,0.000010887067,0.00011701375,0.00005995499,0.000025184776],"domain_scores_gemma":[0.999681,0.000054988504,0.00008010308,0.000022750612,0.000109571796,0.000051680963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037274839,0.00014574037,0.00016215538,0.0007696427,0.00050983595,0.00032333136,0.00020003616,0.0002355498,0.0004238108],"category_scores_gemma":[0.00052560616,0.00015624941,0.00017489801,0.0006760822,0.00022172969,0.0001881471,0.00024528784,0.00025175742,0.00006820151],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007223215,0.00007563881,0.9703386,0.000017693466,0.000073429204,0.0002509666,0.0018564687,0.00012638979,0.017998034,0.000077652316,0.00027177605,0.0088410415],"study_design_scores_gemma":[0.0000015718335,0.00003763801,0.9982356,0.0000035670432,0.0000101312735,0.00014855688,0.00067448744,0.00021929671,0.00023786572,0.000017784942,0.00041102647,0.0000024634826],"about_ca_topic_score_codex":0.020512847,"about_ca_topic_score_gemma":0.04188437,"teacher_disagreement_score":0.9794872,"about_ca_system_score_codex":0.00044241265,"about_ca_system_score_gemma":0.00033686784,"threshold_uncertainty_score":0.040786922},"labels":[],"label_agreement":null},{"id":"W7083689022","doi":"10.54518/rh.5.2.2025.596","title":"Strengthening Indonesia's Cryptocurrency Regulation to Combat Money Laundering: A Comparative Analysis of Canada and South Korea's Approaches","year":2025,"lang":"en","type":"article","venue":"Research Horizon","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Money laundering; Cryptocurrency; Legislation; Asset (computer security); Digital currency; Anonymity; SAFER; Compliance (psychology)","score_opus":0.11398942578506259,"score_gpt":0.34916626180988225,"score_spread":0.23517683602481965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7083689022","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94009304,0.001186429,0.00030769213,0.002137288,0.000040545794,0.00007033353,0.00020136207,0.000011474321,0.05595184],"genre_scores_gemma":[0.9949366,0.00089864654,0.00018841148,0.0002693416,0.0000033880249,0.000010724969,0.00010151444,0.000008255249,0.003583105],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986499,0.00018905425,0.00004746226,0.000100854035,0.000498189,0.00051444315],"domain_scores_gemma":[0.99651414,0.00069979276,0.0005063008,0.00008658243,0.0015351801,0.00065807503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015590629,0.00021263842,0.0002112588,0.0016438218,0.0048402874,0.0042319633,0.0004916,0.0003655583,0.002614272],"category_scores_gemma":[0.0031791076,0.00010824117,0.00020576302,0.0038132549,0.0020000832,0.0012821618,0.0011448511,0.0010503198,0.00015445848],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00081176567,0.0003415616,0.4261225,0.001399005,0.00017826667,0.0038044513,0.21157095,0.0012509351,0.004996049,0.09549385,0.024422714,0.22960807],"study_design_scores_gemma":[0.000024069508,0.00009041121,0.41368264,0.000504303,0.00013779438,0.0003251563,0.4566794,0.0015334979,0.0019965603,0.0009741921,0.12395191,0.000100071506],"about_ca_topic_score_codex":0.83144397,"about_ca_topic_score_gemma":0.9293946,"teacher_disagreement_score":0.16855603,"about_ca_system_score_codex":0.029174503,"about_ca_system_score_gemma":0.042375337,"threshold_uncertainty_score":0.33909738},"labels":[],"label_agreement":null},{"id":"W7084030977","doi":"10.64628/aam.4tnfs36en","title":"Hurricanes to deliver a bigger punch to coasts","year":2019,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"","score_opus":0.007506716756986635,"score_gpt":0.24019760137976126,"score_spread":0.23269088462277462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7084030977","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33329096,0.003980176,0.012866263,0.16748382,0.036778994,0.00030272204,0.0074069193,0.0020694856,0.43582067],"genre_scores_gemma":[0.66585237,0.002523841,0.010192019,0.025947597,0.005632901,0.00010829608,0.0058083865,0.00045625624,0.2834783],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993692,0.000103372506,0.000030387231,0.00007143606,0.00032571514,0.00009988154],"domain_scores_gemma":[0.9983053,0.00018233246,0.00020747735,0.0000664062,0.00093851966,0.00030005773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080834003,0.00042185892,0.00022677984,0.00072845886,0.0024246143,0.0024068158,0.0003053882,0.00089531485,0.011951476],"category_scores_gemma":[0.003193474,0.00013540738,0.0002877912,0.00073604967,0.00049052364,0.0011156922,0.00087499147,0.001959121,0.0034366157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038591315,0.00010073523,0.039530385,0.0003354989,0.0001344898,0.0011939496,0.003939498,0.0011257908,0.007895403,0.008209007,0.7892223,0.14792708],"study_design_scores_gemma":[0.000026504951,0.00019614788,0.06923201,0.000250123,0.00006378103,0.0003105657,0.025408847,0.0038711075,0.003598436,0.0039410694,0.8930294,0.000071978975],"about_ca_topic_score_codex":0.02297706,"about_ca_topic_score_gemma":0.045073938,"teacher_disagreement_score":0.02297706,"about_ca_system_score_codex":0.0015675671,"about_ca_system_score_gemma":0.0014166266,"threshold_uncertainty_score":0.045686603},"labels":[],"label_agreement":null},{"id":"W7096587432","doi":"","title":"Learning Incident Causes","year":2008,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sentence; Parsing; Grammar; Domain (mathematical analysis); WordNet","score_opus":0.032285333501598436,"score_gpt":0.2596126495007018,"score_spread":0.22732731599910336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7096587432","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33417922,0.005623416,0.39890373,0.009079521,0.0032478098,0.0023396937,0.15734811,0.014223468,0.07505498],"genre_scores_gemma":[0.68584627,0.0021595776,0.11290236,0.00045304606,0.0008611212,0.0006949171,0.18155657,0.0003971514,0.0151290465],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9980629,0.0003223193,0.0001939874,0.0007980235,0.00044988893,0.00017298774],"domain_scores_gemma":[0.9934238,0.0031849805,0.000781535,0.0005739798,0.0016958172,0.00033990058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017148603,0.0014175707,0.0006262467,0.011303565,0.0009509207,0.0029521133,0.0013621664,0.0010288355,0.020936787],"category_scores_gemma":[0.012714727,0.00048652964,0.0018233939,0.0038851094,0.000376168,0.0028688225,0.001634317,0.0017482276,0.00687859],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003925959,0.0007192816,0.24908675,0.00078456785,0.0005861657,0.0018535351,0.000741349,0.030733595,0.0013117236,0.018048633,0.14542882,0.55031294],"study_design_scores_gemma":[0.00022216418,0.00038855727,0.16573068,0.001364993,0.0010897056,0.0019708013,0.0068072355,0.52987945,0.008196685,0.09144524,0.19271193,0.00019257316],"about_ca_topic_score_codex":0.011671467,"about_ca_topic_score_gemma":0.013467813,"teacher_disagreement_score":0.020936787,"about_ca_system_score_codex":0.0015534993,"about_ca_system_score_gemma":0.003279242,"threshold_uncertainty_score":0.070040524},"labels":[],"label_agreement":null},{"id":"W7099412334","doi":"","title":"EPIGENETICS OF STRESS ADAPTATION IN ARABIDOPSIS: THE CASE OF","year":2013,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Epigenetics; Stress (linguistics); Adaptation (eye); Inclusion (mineral); Fight-or-flight response","score_opus":0.026147356548676694,"score_gpt":0.2564322331175948,"score_spread":0.23028487656891808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7099412334","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9638289,0.0041869543,0.008203399,0.003715884,0.00023081635,0.000020331967,0.00069890666,0.0002468199,0.01886789],"genre_scores_gemma":[0.9941884,0.00074511947,0.0011384741,0.00039923438,0.000022631266,0.0000069194793,0.00022610092,0.000064461805,0.003208647],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.999853,0.000023380777,0.00000550732,0.00005642465,0.00003353261,0.000028198756],"domain_scores_gemma":[0.9996203,0.000104142884,0.00008948735,0.00007036802,0.00006041387,0.000055271696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027917288,0.00013152689,0.00026670741,0.00018997736,0.0005354676,0.0009643624,0.00041205465,0.0006652568,0.0018188806],"category_scores_gemma":[0.0006364383,0.00015166323,0.00028331042,0.00025972322,0.00054632203,0.00060335535,0.0005103309,0.0009997274,0.00039403397],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020788125,0.000030166042,0.0057427776,0.00010277961,0.00003611046,0.0014972262,0.0011717506,0.00074736396,0.96888095,0.006420796,0.0019009052,0.013261242],"study_design_scores_gemma":[0.00007719156,0.0003227555,0.45667115,0.00013949646,0.00028484213,0.0036293413,0.0075574443,0.018970968,0.38431954,0.026361361,0.10139965,0.0002662437],"about_ca_topic_score_codex":0.0035711825,"about_ca_topic_score_gemma":0.0043256655,"teacher_disagreement_score":0.0035711825,"about_ca_system_score_codex":0.0009317745,"about_ca_system_score_gemma":0.0002471591,"threshold_uncertainty_score":0.0071008205},"labels":[],"label_agreement":null},{"id":"W7099894807","doi":"","title":"National Library of Canada to","year":2015,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"","score_opus":0.03287629081799429,"score_gpt":0.24420232818017534,"score_spread":0.21132603736218106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7099894807","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00035169206,0.0010721206,0.00026903852,0.0031875619,0.0010521758,0.00012845744,0.023834039,0.001024755,0.96908027],"genre_scores_gemma":[0.0005496821,0.00054675486,0.00015877609,0.000400918,0.000028824777,0.000019412226,0.0030237117,0.00015231926,0.9951196],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99846435,0.00006328196,0.0000542366,0.00019531723,0.0008961034,0.00032660633],"domain_scores_gemma":[0.9944317,0.000306864,0.000113424656,0.0004680586,0.0039878236,0.0006920341],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008147245,0.0014062136,0.001801937,0.008409458,0.0067306263,0.009344045,0.0022831245,0.0031959082,0.8100628],"category_scores_gemma":[0.005693644,0.0008350752,0.001235871,0.009326629,0.0014503408,0.0022103398,0.0024724714,0.0021487938,0.6480349],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028461025,0.000022515811,0.00018764439,0.00016584735,0.0000050177414,0.000041311894,0.000050718878,0.000051232288,0.00008804727,0.003424955,0.9576722,0.03826211],"study_design_scores_gemma":[0.000007940269,0.000004199719,0.00065364194,0.00012703668,0.000006068423,0.000012518701,0.0001034969,0.00004418003,0.000077226054,0.00048737554,0.998467,0.00000905093],"about_ca_topic_score_codex":0.8448858,"about_ca_topic_score_gemma":0.93390465,"teacher_disagreement_score":0.8448858,"about_ca_system_score_codex":0.019704234,"about_ca_system_score_gemma":0.05864311,"threshold_uncertainty_score":0.3120553},"labels":[],"label_agreement":null},{"id":"W7104179864","doi":"10.5267/j.ijdns.2025.10.011","title":"Sentiment analysis on social media using VADER and LSTM to optimise the marketing strategy for SOE energy products","year":2025,"lang":"en","type":"article","venue":"International Journal of Data and Network Science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Social media; Sentiment analysis; Digital marketing; Marketing strategy; Key (lock); Consumer behaviour; Digital media; Energy (signal processing); Marketing research","score_opus":0.08149922381149659,"score_gpt":0.36741251517555734,"score_spread":0.28591329136406074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7104179864","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6118687,0.0006700425,0.37538046,0.0011353123,0.00032102515,0.00023984151,0.0005992591,0.0009895676,0.008795897],"genre_scores_gemma":[0.93187374,0.0002440709,0.06468522,0.00014386306,0.00006173003,0.00008407415,0.0003486515,0.00003186758,0.0025267121],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997888,0.00007081697,0.000017330398,0.000047256584,0.00004668087,0.000029149776],"domain_scores_gemma":[0.9994622,0.00027505725,0.000064207095,0.000021703328,0.00016297426,0.000013855995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076078955,0.0004555959,0.00026433438,0.00071074587,0.00020705721,0.0007969091,0.00027744498,0.00047656745,0.0013553759],"category_scores_gemma":[0.0019887525,0.00014543107,0.00049703766,0.00042162254,0.00019799116,0.0010333438,0.00036155368,0.0005392516,0.00045986142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00074573135,0.00048790005,0.026221616,0.00041605294,0.0002706201,0.00043566397,0.0013767544,0.1028051,0.07182496,0.00582766,0.007883873,0.781704],"study_design_scores_gemma":[0.000012790914,0.00011262625,0.0048346077,0.00002220267,0.00004222949,0.00003460992,0.0003028808,0.9829108,0.007301297,0.0026927397,0.0017197129,0.000013468296],"about_ca_topic_score_codex":0.0021255072,"about_ca_topic_score_gemma":0.0039719474,"teacher_disagreement_score":0.0021255072,"about_ca_system_score_codex":0.00052950496,"about_ca_system_score_gemma":0.00032654504,"threshold_uncertainty_score":0.004534185},"labels":[],"label_agreement":null},{"id":"W7111786345","doi":"","title":"METIS: Multiple Extraction Techniques for Informative Sentences","year":2004,"lang":"en","type":"article","venue":"Research Explorer (The University of Manchester)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Pattern recognition (psychology); Feature extraction; Matching (statistics); Extraction (chemistry); Class (philosophy)","score_opus":0.10430920214388849,"score_gpt":0.33609636773546664,"score_spread":0.23178716559157814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7111786345","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010606252,0.00092121254,0.9293733,0.000519444,0.00045049752,0.00082347635,0.01812234,0.03542037,0.0037630412],"genre_scores_gemma":[0.04978472,0.0004624249,0.9113429,0.00014000713,0.000353609,0.00095541886,0.028777108,0.0028071734,0.0053765946],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99741685,0.00075279165,0.00038693743,0.00052817864,0.00075117446,0.00016399109],"domain_scores_gemma":[0.99526054,0.0023390183,0.0004997584,0.0006235987,0.0011501216,0.00012702699],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027145338,0.0027196321,0.0013264196,0.006758366,0.0015656122,0.0024158729,0.0017318586,0.0013613306,0.01592105],"category_scores_gemma":[0.009589224,0.0012683705,0.0019796612,0.0047291727,0.00045169453,0.0033527552,0.0025526737,0.0022864246,0.009485637],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062893925,0.00019202009,0.002085978,0.0019520632,0.00035690857,0.0005740013,0.001069979,0.0018272019,0.06505733,0.011184566,0.101450495,0.8136205],"study_design_scores_gemma":[0.00059226446,0.00094821525,0.012471115,0.00073067413,0.0015737871,0.00303346,0.002030667,0.2826614,0.20110041,0.059840556,0.43461844,0.00039906052],"about_ca_topic_score_codex":0.001098187,"about_ca_topic_score_gemma":0.0029183433,"teacher_disagreement_score":0.01592105,"about_ca_system_score_codex":0.00059648056,"about_ca_system_score_gemma":0.0017177264,"threshold_uncertainty_score":0.05326122},"labels":[],"label_agreement":null},{"id":"W7116357590","doi":"10.1016/j.eswa.2025.130869","title":"In-context learning enhanced by multi-perspective sequential retrieval and predictive feedback for few-shot aspect-based sentiment analysis","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Université de Montréal","funders":"National Key Research and Development Program of China; Sichuan Province Science and Technology Support Program; National Natural Science Foundation of China; Department of Science and Technology of Sichuan Province; Organization Department of Sichuan Provincial Party Committee; Ministry of Science and Technology of the People's Republic of China; Chinese Academy of Sciences","keywords":"Sentiment analysis; Benchmark (surveying); Parsing; Language model; Labeled data; Training set","score_opus":0.015722407220630865,"score_gpt":0.30492235914652605,"score_spread":0.2891999519258952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116357590","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15066147,0.0032179197,0.82928395,0.00058363564,0.00031066855,0.00035738162,0.001110229,0.009449202,0.005025459],"genre_scores_gemma":[0.71271247,0.0006693873,0.27670386,0.00067594985,0.00027395782,0.00028035452,0.0036430492,0.00028087985,0.0047601713],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991431,0.00019500169,0.00005082694,0.00031511544,0.00020035439,0.00009554914],"domain_scores_gemma":[0.99897194,0.0004462766,0.00008248976,0.00015749686,0.00027574343,0.00006612656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095761276,0.0017507876,0.0011388735,0.0015480047,0.00058608316,0.0008299043,0.0014486051,0.0011107827,0.0025878083],"category_scores_gemma":[0.0038312948,0.0003359823,0.0010645696,0.0009881406,0.0003848042,0.002377809,0.0012648682,0.0014623391,0.0015677611],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061152515,0.0007196981,0.00567023,0.00042377957,0.00020801522,0.00048069362,0.00045180245,0.04003762,0.064734794,0.0027667943,0.015292635,0.86860245],"study_design_scores_gemma":[0.000045178997,0.00025098136,0.0016362632,0.00002761152,0.00008113605,0.00017053368,0.00015111272,0.976143,0.012469089,0.005644242,0.0033478725,0.000032886135],"about_ca_topic_score_codex":0.005142357,"about_ca_topic_score_gemma":0.010786557,"teacher_disagreement_score":0.005142357,"about_ca_system_score_codex":0.00058759504,"about_ca_system_score_gemma":0.0009065493,"threshold_uncertainty_score":0.010224879},"labels":[],"label_agreement":null},{"id":"W7116374164","doi":"10.18280/ijsse.150908","title":"A Deep Learning and AutoML-Based Multimodal Text Extraction Framework for Detecting Online Gambling Advertisements in Indonesian Social Media","year":2025,"lang":"","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Indonesian; Social media; Deep learning; Feature extraction; Extraction (chemistry)","score_opus":0.012962265998052494,"score_gpt":0.3080798501814661,"score_spread":0.2951175841834136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116374164","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3066976,0.0013158605,0.6552204,0.00053653476,0.00019640652,0.0004670862,0.0050728647,0.019749051,0.010744131],"genre_scores_gemma":[0.64931744,0.0004378758,0.32027647,0.0002949529,0.00012280102,0.00042472326,0.007662701,0.0003336754,0.021129405],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977905,0.00004039021,0.000016291084,0.00007497065,0.000054834218,0.000034509147],"domain_scores_gemma":[0.9997563,0.00006420113,0.000044313885,0.000027272235,0.0000905444,0.000017297405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030646066,0.0008990146,0.00040826315,0.0013978973,0.00024095603,0.00042109675,0.00049952243,0.00042934105,0.002700538],"category_scores_gemma":[0.0007416267,0.00020764617,0.0005253627,0.00047959178,0.00021109912,0.0008445643,0.000573849,0.00046504172,0.0021211063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036907694,0.00029356426,0.005528393,0.00028958987,0.00007435575,0.00052078796,0.00023682162,0.014343164,0.07949769,0.0012901685,0.009958541,0.8875978],"study_design_scores_gemma":[0.00003586817,0.00028705935,0.018828534,0.000066190274,0.00012523866,0.0005500111,0.000315362,0.8954837,0.07076931,0.0021381027,0.011334593,0.000066071894],"about_ca_topic_score_codex":0.0045521404,"about_ca_topic_score_gemma":0.009528905,"teacher_disagreement_score":0.0045521404,"about_ca_system_score_codex":0.00044700448,"about_ca_system_score_gemma":0.0006128498,"threshold_uncertainty_score":0.009051263},"labels":[],"label_agreement":null},{"id":"W7117138955","doi":"10.1016/j.engappai.2025.113584","title":"A method for extracting emotion–cause pairs based on bidirectional machine reading comprehension","year":2025,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Hebei Province Science and Technology Support Program; Hebei Provincial Department of Bureau of Science and Technology; Guangdong University of Foreign Studies; Ministry of Education of the People's Republic of China","keywords":"Benchmark (surveying); Graph; Filter (signal processing); Task (project management); Attention network; Exploit; Comprehension","score_opus":0.03792664972755048,"score_gpt":0.3372674994288623,"score_spread":0.2993408497013118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117138955","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056707885,0.0004597191,0.9155867,0.0003125388,0.00018969314,0.0007229722,0.003330771,0.011662189,0.011027527],"genre_scores_gemma":[0.30827332,0.0003350639,0.6753268,0.000100942285,0.00019799083,0.000991444,0.0068621943,0.0007755377,0.0071366206],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885046,0.00027191575,0.00013371614,0.00043083326,0.00023125349,0.00008176087],"domain_scores_gemma":[0.9964958,0.001558502,0.0003095786,0.00024160508,0.0013013552,0.000093193536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012736062,0.0015819119,0.000736291,0.0049798777,0.000759019,0.0019717528,0.0008330215,0.00086003396,0.0136168385],"category_scores_gemma":[0.0074017034,0.00048127316,0.0011629266,0.0028740936,0.00034100396,0.0026049502,0.0012980822,0.0013678154,0.006184429],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036921646,0.00026106692,0.01046456,0.0007241624,0.00023039736,0.00047160132,0.0021191107,0.0018196536,0.05695517,0.007232024,0.011113348,0.90823966],"study_design_scores_gemma":[0.00031709802,0.0011865145,0.110478915,0.0005226647,0.0016038398,0.0025826716,0.006345203,0.5637608,0.14600411,0.08169552,0.08495098,0.00055154297],"about_ca_topic_score_codex":0.0021522031,"about_ca_topic_score_gemma":0.0035169339,"teacher_disagreement_score":0.0136168385,"about_ca_system_score_codex":0.0004568164,"about_ca_system_score_gemma":0.0014260277,"threshold_uncertainty_score":0.04555285},"labels":[],"label_agreement":null},{"id":"W7117251867","doi":"10.1145/3786590","title":"Implicit Aspect Extraction: A Systematic Review","year":2025,"lang":"en","type":"article","venue":"ACM Computing Surveys","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Leverage (statistics); Identification (biology); Task (project management); Information extraction; Context (archaeology); Natural language; Natural language understanding; Purchasing","score_opus":0.025746452905467405,"score_gpt":0.33108599218026225,"score_spread":0.30533953927479485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117251867","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007883219,0.99413514,0.001650791,0.0007645372,0.00025061716,0.0002416302,0.001013932,0.000041602838,0.0011134606],"genre_scores_gemma":[0.0074061453,0.9848726,0.0038343547,0.0012626676,0.00020995087,0.00055957003,0.0015135742,0.000040939234,0.00030023896],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.993359,0.00200704,0.0023352362,0.000737004,0.0014048982,0.00015683523],"domain_scores_gemma":[0.9420641,0.044330977,0.0054851333,0.0017182647,0.00590176,0.0004997748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011522191,0.0012657925,0.003516043,0.014578612,0.00066289573,0.0029967502,0.0024089417,0.0015891265,0.0055465177],"category_scores_gemma":[0.070086926,0.0009309124,0.004756511,0.012104256,0.0010608879,0.004975839,0.0020036164,0.0013537793,0.0012563098],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023487634,0.0000386436,0.001888378,0.5651437,0.0030962958,0.00019945647,0.00050234626,0.00025944406,0.00062267826,0.002061389,0.019524459,0.40642837],"study_design_scores_gemma":[0.00012054586,0.00020139445,0.0061269463,0.6641253,0.016412152,0.00079011323,0.0005508919,0.0003132016,0.000739202,0.0037435899,0.30680156,0.00007513548],"about_ca_topic_score_codex":0.0033903844,"about_ca_topic_score_gemma":0.013386355,"teacher_disagreement_score":0.014578612,"about_ca_system_score_codex":0.0016360272,"about_ca_system_score_gemma":0.01135834,"threshold_uncertainty_score":0.060935915},"labels":[],"label_agreement":null},{"id":"W7117567890","doi":"10.54554/jet.2025.16.1.007","title":"CROSS-CULTURAL EMOTION ANALYSIS ON X USING BIDIRECTIONAL ENCODER REPRESENTATIONS FROM TRANSFORMERS (BERT)","year":2025,"lang":"","type":"article","venue":"Journal of Engineering and Technology (JET)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Encoder; Sentiment analysis; Affect (linguistics); Transformer; Stability (learning theory); Contextual design","score_opus":0.015210342531294965,"score_gpt":0.3030903970510459,"score_spread":0.28788005451975096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117567890","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6028274,0.00025454853,0.38372648,0.0004853992,0.000078217265,0.0001811578,0.0012697991,0.0007012437,0.010475778],"genre_scores_gemma":[0.97100765,0.00012730659,0.02585756,0.000031461048,0.000011425677,0.00006485042,0.000534838,0.000028379676,0.002336442],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997662,0.00010028905,0.000010813918,0.000045982455,0.000042453154,0.000034343888],"domain_scores_gemma":[0.9994954,0.00025453,0.00007656645,0.000044581666,0.00010917901,0.000019685713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005256044,0.00039075562,0.00016327933,0.0005692431,0.00023110706,0.00080225593,0.00028573172,0.00022275996,0.0023980127],"category_scores_gemma":[0.0024154396,0.00012954409,0.00045224084,0.000562987,0.00029478566,0.0010293527,0.00065467134,0.0004008831,0.00049339305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013140182,0.0003126872,0.08482194,0.00040227207,0.00027418646,0.0012884552,0.007119446,0.24568075,0.05365902,0.04941308,0.008245071,0.547469],"study_design_scores_gemma":[0.000017430559,0.0002072744,0.01941811,0.00003839914,0.00005735561,0.00018413263,0.0017605886,0.9539804,0.005518942,0.014077558,0.0047052978,0.00003454705],"about_ca_topic_score_codex":0.008797663,"about_ca_topic_score_gemma":0.0073877666,"teacher_disagreement_score":0.008797663,"about_ca_system_score_codex":0.00054142915,"about_ca_system_score_gemma":0.00037830288,"threshold_uncertainty_score":0.01749295},"labels":[],"label_agreement":null},{"id":"W7118247593","doi":"10.65091/icicset.v2i1.5","title":"A Hybrid Attention-Driven Recurrent Neural Network Model for Sentiment Classification of Social Media Texts","year":2025,"lang":"","type":"article","venue":"Proceedings of International Conference on Innovation in Computing Science Engineering and Technology","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Social media; Recurrent neural network; Sentiment analysis; Context (archaeology); Task (project management); Representation (politics); Focus (optics); Unstructured data; Artificial neural network","score_opus":0.03489837288064822,"score_gpt":0.302668618577014,"score_spread":0.26777024569636576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7118247593","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18991205,0.0018597008,0.7978797,0.001050459,0.00043825264,0.00013220056,0.0006120633,0.0024025098,0.0057131452],"genre_scores_gemma":[0.9221533,0.0005583412,0.066658564,0.0003438152,0.00016124823,0.0001828988,0.0009588303,0.000089699206,0.008893419],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997507,0.000054398843,0.000016325444,0.000082153434,0.000047144003,0.0000491952],"domain_scores_gemma":[0.99975353,0.00008173347,0.000031283915,0.000013853876,0.00010568224,0.000013885621],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000575467,0.0009418803,0.00067712425,0.00047702945,0.00030702245,0.00060919195,0.001351344,0.0009147544,0.0012733763],"category_scores_gemma":[0.0010763125,0.00032546252,0.00080193917,0.00047944597,0.0002816224,0.0008759429,0.00050812843,0.0010447074,0.0007160491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004067375,0.00047497996,0.0043047275,0.00013635257,0.00023085426,0.00033155878,0.00019790314,0.6595568,0.026318356,0.004628826,0.007902891,0.29551002],"study_design_scores_gemma":[0.0000021044589,0.000013860213,0.000116433155,0.0000017606066,0.00000578652,0.0000048785923,0.0000022773947,0.9990897,0.0003741469,0.00030133143,0.00008518894,0.0000025592599],"about_ca_topic_score_codex":0.010709998,"about_ca_topic_score_gemma":0.012168698,"teacher_disagreement_score":0.010709998,"about_ca_system_score_codex":0.0007634894,"about_ca_system_score_gemma":0.0006210722,"threshold_uncertainty_score":0.021295309},"labels":[],"label_agreement":null},{"id":"W7124982657","doi":"10.1109/iccca66364.2025.11325205","title":"Expandor : A Novel Enhanced Exploratory Processing Augmentation for Text Data Optimization &amp; Refinement","year":2025,"lang":"","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thompson Rivers University","funders":"","keywords":"Variety (cybernetics); Generalization; Bridge (graph theory); Noise (video); Data processing; Quality (philosophy)","score_opus":0.11166017217431348,"score_gpt":0.36632703245807813,"score_spread":0.2546668602837646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7124982657","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015514025,0.0002674016,0.97259074,0.00032281192,0.00015138593,0.00028184926,0.0006085443,0.0091089895,0.0011541392],"genre_scores_gemma":[0.076389596,0.0002218726,0.9160289,0.00030488052,0.00011792714,0.00054775365,0.002571261,0.0007154029,0.0031023703],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982457,0.00042633727,0.0001715674,0.00042029447,0.0006181374,0.000117990574],"domain_scores_gemma":[0.9966917,0.0017443305,0.00024923694,0.00062072556,0.0006186353,0.000075241835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023429862,0.0016440019,0.0011975585,0.0016681515,0.0006023234,0.00139148,0.002167239,0.0012361075,0.0048008794],"category_scores_gemma":[0.007970056,0.0006825569,0.0019542852,0.0017177965,0.0009189524,0.003252137,0.002789222,0.0020630448,0.0030126455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007018675,0.000410922,0.0028189393,0.0003805475,0.00014507017,0.00042276786,0.0006099812,0.05399678,0.065067396,0.0061865444,0.018652901,0.8506062],"study_design_scores_gemma":[0.00010375598,0.00037050943,0.0011269577,0.000051940337,0.00005863029,0.0003563144,0.00021368307,0.92324746,0.046719022,0.008819325,0.01887211,0.000060333034],"about_ca_topic_score_codex":0.0017571538,"about_ca_topic_score_gemma":0.002859797,"teacher_disagreement_score":0.0048008794,"about_ca_system_score_codex":0.0003685742,"about_ca_system_score_gemma":0.0013033139,"threshold_uncertainty_score":0.01606059},"labels":[],"label_agreement":null},{"id":"W7125392507","doi":"10.18280/mmep.121209","title":"Sentiment Analysis of the Documentary Film Ice Cold on Twitter: A Comparative Study of Machine Learning and Rule-Based Methods","year":2025,"lang":"","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Multimedia University","keywords":"Sentiment analysis; Documentary film; Deep learning","score_opus":0.04086743223688722,"score_gpt":0.3147694778319024,"score_spread":0.2739020455950152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125392507","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7552105,0.013175469,0.2040572,0.0021095048,0.00057165645,0.0006425529,0.0013526393,0.0007285454,0.022151899],"genre_scores_gemma":[0.9003798,0.0041697323,0.09231316,0.0001669376,0.00025179388,0.00016461284,0.0010777998,0.000053489846,0.001422647],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997235,0.0010792839,0.00031020326,0.0002537984,0.0010125453,0.00010924337],"domain_scores_gemma":[0.9920115,0.005289261,0.0005885847,0.00020798812,0.0018036328,0.0000990648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004564064,0.00048890826,0.00073052524,0.002917131,0.00030327943,0.0014964424,0.00041404905,0.00043338994,0.00058281474],"category_scores_gemma":[0.009426716,0.0001475537,0.0006903615,0.0018688651,0.0002457033,0.001485148,0.00027136152,0.00046861992,0.00044012733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00076504645,0.00042044764,0.072615065,0.0013885435,0.00041640058,0.0002815979,0.001410028,0.012702202,0.008459256,0.0025444934,0.0055017145,0.89349526],"study_design_scores_gemma":[0.00007151577,0.00084531034,0.1298348,0.0005948057,0.0003201806,0.00051396235,0.0036026505,0.8293675,0.01619826,0.004069033,0.014468604,0.00011335395],"about_ca_topic_score_codex":0.0018858883,"about_ca_topic_score_gemma":0.0019714488,"teacher_disagreement_score":0.004564064,"about_ca_system_score_codex":0.0005282955,"about_ca_system_score_gemma":0.00038156807,"threshold_uncertainty_score":0.024137378},"labels":[],"label_agreement":null},{"id":"W7125608655","doi":"10.1109/cascon66301.2025.00072","title":"Pushing Feelings: Emotion and Sentiment in Software Commit Messages","year":2025,"lang":"","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Commit; Sentiment analysis; Software; Random forest; Implementation; Source code; Code (set theory)","score_opus":0.013091752883149642,"score_gpt":0.27140526664150777,"score_spread":0.25831351375835815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125608655","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9767229,0.00038947148,0.0066831885,0.0005352647,0.000135452,0.00007858778,0.009503171,0.00065710815,0.0052948073],"genre_scores_gemma":[0.97230476,0.00024897192,0.0074047344,0.00015184813,0.00009677411,0.000115117466,0.01564987,0.00010172518,0.003926196],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992212,0.00023952972,0.00006205795,0.00014257053,0.00025642707,0.000078166915],"domain_scores_gemma":[0.99742377,0.0011796737,0.0004998074,0.00014806211,0.000600565,0.00014807201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069412315,0.0004802329,0.00022892056,0.0010885316,0.00030028206,0.0008447788,0.00021800456,0.00043495317,0.0014414357],"category_scores_gemma":[0.005357721,0.00010195248,0.00029060047,0.00091982976,0.00027005424,0.0008283202,0.00056703243,0.0006864936,0.0008413647],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017351309,0.0006246325,0.50869983,0.0013219076,0.00024852756,0.00096079294,0.0078750765,0.004677414,0.052535545,0.0021446748,0.07675222,0.3424242],"study_design_scores_gemma":[0.000048981958,0.00035574977,0.8492261,0.00022561176,0.00013272472,0.000599011,0.0077725216,0.06641954,0.023454899,0.0031555176,0.0484922,0.00011714769],"about_ca_topic_score_codex":0.0025712764,"about_ca_topic_score_gemma":0.0053373566,"teacher_disagreement_score":0.0025712764,"about_ca_system_score_codex":0.0004191759,"about_ca_system_score_gemma":0.00022978282,"threshold_uncertainty_score":0.005112648},"labels":[],"label_agreement":null},{"id":"W7126183525","doi":"10.18280/isi.301224","title":"A Hybrid RNN-LSTM Framework for Predicting Stakeholder Engagement on Social Media Platforms","year":2025,"lang":"","type":"article","venue":"Ingénierie des systèmes d information","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Social media; Stakeholder; Stakeholder engagement; Key (lock); Context (archaeology)","score_opus":0.06707532619926225,"score_gpt":0.2911583620539885,"score_spread":0.22408303585472622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126183525","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47193003,0.0018772093,0.50223917,0.0018083433,0.0006257901,0.00018808056,0.0034056455,0.0040622326,0.01386347],"genre_scores_gemma":[0.92658883,0.00033487572,0.06415954,0.00019687081,0.00014322068,0.00011230258,0.0019940764,0.00007039682,0.006399821],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997396,0.00006757237,0.000011497446,0.000069297486,0.000047986916,0.000064085245],"domain_scores_gemma":[0.9996631,0.00015196376,0.000030974494,0.000017681703,0.00010909428,0.000027217084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061773736,0.0009257072,0.0004281515,0.0007469452,0.00031795134,0.0005888807,0.0006827787,0.00087659206,0.0019333508],"category_scores_gemma":[0.0012947257,0.00023033273,0.0005103282,0.0007651581,0.00014004398,0.0009753016,0.00066683546,0.0010469471,0.0011912135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006452809,0.000913557,0.022545103,0.00024296979,0.00034944867,0.00043195335,0.0003773375,0.21967642,0.026513612,0.002992597,0.01970102,0.70561075],"study_design_scores_gemma":[0.0000047172534,0.000039327977,0.0018003747,0.00001123705,0.000018655392,0.000015373353,0.00004136453,0.99497485,0.0013649638,0.0011010552,0.0006210431,0.000007084348],"about_ca_topic_score_codex":0.013667965,"about_ca_topic_score_gemma":0.024304513,"teacher_disagreement_score":0.013667965,"about_ca_system_score_codex":0.0005407675,"about_ca_system_score_gemma":0.00062766287,"threshold_uncertainty_score":0.027176857},"labels":[],"label_agreement":null},{"id":"W7126396968","doi":"10.21428/594757db.faf9e091","title":"Explainable Prompt-based Approaches for Sentiment Analysis of Movie Reviews","year":2024,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Sentiment analysis; Language model; Work (physics); Deep learning","score_opus":0.10246998512655217,"score_gpt":0.3120374727653433,"score_spread":0.20956748763879116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126396968","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052179664,0.00043777103,0.9315797,0.0009679106,0.0001646797,0.0003279804,0.00085983984,0.011465386,0.002016961],"genre_scores_gemma":[0.5212575,0.00041418913,0.47260338,0.00027182634,0.00018412311,0.0003183635,0.0020516505,0.0002809704,0.0026180553],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998351,0.0008159161,0.00010597229,0.0003484929,0.00031055306,0.000068244255],"domain_scores_gemma":[0.98934853,0.0063987,0.0010646096,0.0011093905,0.0018399254,0.0002388017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038766577,0.0011284174,0.0006114306,0.0013328246,0.00040887852,0.0013367618,0.0010114888,0.0006326154,0.003585321],"category_scores_gemma":[0.021476654,0.00032459025,0.0007616658,0.0007573568,0.00034584568,0.0030140334,0.0010504974,0.0022346133,0.0020048746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00095771276,0.00072078744,0.015634336,0.0007637773,0.000134803,0.00024509343,0.0015903312,0.03840185,0.031026164,0.010834076,0.010853193,0.88883793],"study_design_scores_gemma":[0.00008904713,0.00067391625,0.0059303273,0.000081971186,0.000062473,0.00014943993,0.00061693735,0.9329391,0.018287437,0.029123513,0.011976723,0.000069019676],"about_ca_topic_score_codex":0.00097353535,"about_ca_topic_score_gemma":0.0019163879,"teacher_disagreement_score":0.0038766577,"about_ca_system_score_codex":0.000841377,"about_ca_system_score_gemma":0.0011177128,"threshold_uncertainty_score":0.020501971},"labels":[],"label_agreement":null},{"id":"W7126398747","doi":"10.21428/594757db.49a48292","title":"Analysis of Canadian Wildfire Tweets Over Seven Years","year":2025,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"National Research Council Canada; Carleton University","funders":"","keywords":"Population; Government (linguistics); Feature (linguistics); Statistical analysis","score_opus":0.012062796455768146,"score_gpt":0.25782361653544394,"score_spread":0.2457608200796758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126398747","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8609169,0.000721464,0.0010727072,0.0009158592,0.00014047627,0.00017357524,0.12152016,0.0002655819,0.0142732905],"genre_scores_gemma":[0.89223546,0.0008284389,0.0034564736,0.00028895304,0.00008620923,0.00019293318,0.09377287,0.00006623154,0.009072355],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99919254,0.00004392978,0.000033931545,0.000119125754,0.00041079868,0.00019961057],"domain_scores_gemma":[0.9974208,0.0002738995,0.00021234782,0.00008653567,0.0017785075,0.00022792286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006104155,0.0005766906,0.00030060345,0.0038062586,0.0025133349,0.0010492861,0.00053100026,0.0003901757,0.0017311762],"category_scores_gemma":[0.00243172,0.00014821604,0.00027618898,0.005111961,0.0005201505,0.00047534314,0.0007099105,0.00052717456,0.0006283781],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00094342814,0.0002387175,0.7033855,0.0007747595,0.00022776646,0.0014846158,0.015801959,0.0031321885,0.016594078,0.0027106663,0.117308,0.13739833],"study_design_scores_gemma":[0.000012456215,0.00004531183,0.9156334,0.00008764113,0.000058309855,0.0001681076,0.009646999,0.005171826,0.0029099998,0.0001638797,0.06602327,0.00007883938],"about_ca_topic_score_codex":0.95682126,"about_ca_topic_score_gemma":0.9813374,"teacher_disagreement_score":0.043178737,"about_ca_system_score_codex":0.009599674,"about_ca_system_score_gemma":0.010364712,"threshold_uncertainty_score":0.08686602},"labels":[],"label_agreement":null},{"id":"W7126415882","doi":"10.21428/594757db.2eed2ef7","title":"The Use of Human Evaluators in Emotion Text-Detection","year":2024,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Affect (linguistics); Perception; Quality (philosophy)","score_opus":0.07583309902574012,"score_gpt":0.32184072132621266,"score_spread":0.24600762230047254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126415882","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46670234,0.0032148848,0.49631548,0.0011801325,0.0005276248,0.0005133096,0.00092091656,0.002297739,0.028327536],"genre_scores_gemma":[0.8839657,0.0005176984,0.10749594,0.00026709796,0.00020401487,0.0001908883,0.0006778455,0.00028975212,0.0063910466],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99432844,0.003756324,0.00023517675,0.00068037474,0.0007776737,0.00022202474],"domain_scores_gemma":[0.9804078,0.012042274,0.0011119336,0.0010046446,0.004980806,0.00045258057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008916911,0.00079370185,0.00065710675,0.001603515,0.00067655946,0.0019333878,0.0004371102,0.0006834558,0.0021615361],"category_scores_gemma":[0.023889218,0.00024771446,0.00033459262,0.00082679995,0.00043367472,0.0016659192,0.00073845003,0.0007233812,0.001411039],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014030319,0.00041542103,0.044145275,0.0006537248,0.00028743202,0.0002718558,0.0021335154,0.0035738624,0.12504,0.0032222955,0.010433496,0.8084201],"study_design_scores_gemma":[0.00015174426,0.0017637066,0.11422931,0.00046480796,0.000749731,0.0009440629,0.0025642526,0.61068356,0.2184656,0.00912313,0.040629,0.00023113909],"about_ca_topic_score_codex":0.0013130765,"about_ca_topic_score_gemma":0.002775816,"teacher_disagreement_score":0.008916911,"about_ca_system_score_codex":0.00035332053,"about_ca_system_score_gemma":0.0005713443,"threshold_uncertainty_score":0.047157705},"labels":[],"label_agreement":null},{"id":"W7126433153","doi":"10.21428/594757db.4bc3ecf6","title":"Instruction Tuning of LLMs for Multi-label EmotionClassification in Social Media Content","year":2024,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Controllability; Inference; Social media; Generative model; Test (biology); Order (exchange); Training set; Language model","score_opus":0.23695690924033014,"score_gpt":0.34852338608880123,"score_spread":0.1115664768484711,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126433153","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13159953,0.00076269417,0.8004712,0.00080297765,0.0004651562,0.00037015098,0.0011265365,0.061604444,0.0027972844],"genre_scores_gemma":[0.758441,0.00017132591,0.23101006,0.00077849464,0.00014294121,0.00054249534,0.003104293,0.0020290457,0.003780417],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990577,0.0002781373,0.00005078519,0.00039621184,0.00012157897,0.00009563624],"domain_scores_gemma":[0.99767584,0.001334856,0.00009970431,0.00036896282,0.00039513892,0.00012557651],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015172029,0.0016442953,0.0007653131,0.000734534,0.0004118654,0.0011753528,0.0018447529,0.0012937364,0.005157932],"category_scores_gemma":[0.009644864,0.0004811062,0.00085627224,0.0004274525,0.0005578689,0.0029326293,0.0014977176,0.0030531338,0.0032338398],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001248536,0.0009924198,0.010249801,0.0004233389,0.00015994853,0.00036166093,0.0007029814,0.1363396,0.057958,0.0041627735,0.018701652,0.7686993],"study_design_scores_gemma":[0.00007097152,0.00014870957,0.0009848394,0.000016045095,0.000032185028,0.00006309466,0.00010557504,0.9714471,0.0187072,0.0056413785,0.0027554256,0.000027493212],"about_ca_topic_score_codex":0.0034120574,"about_ca_topic_score_gemma":0.005293545,"teacher_disagreement_score":0.005157932,"about_ca_system_score_codex":0.000992695,"about_ca_system_score_gemma":0.0011706405,"threshold_uncertainty_score":0.017254949},"labels":[],"label_agreement":null},{"id":"W7128910823","doi":"10.13140/rg.2.2.33952.44808","title":"Charging Up Opinions: Unveiling Canadian Sentiments on Electric Vehicles (EVs) through Sentiment Analysis and Topic Modeling of Reddit Data","year":2024,"lang":"","type":"article","venue":"Open MIND","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sentiment analysis; Topic model; Big data; Social media","score_opus":0.10469860779797946,"score_gpt":0.35400713383532234,"score_spread":0.24930852603734288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7128910823","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9477925,0.0005535997,0.010755053,0.0032101127,0.0002723062,0.00010837131,0.011156529,0.0004986807,0.02565299],"genre_scores_gemma":[0.975514,0.00032057232,0.008314899,0.00021825325,0.00009470905,0.000028302024,0.009291408,0.00007269743,0.006145159],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9993259,0.00012208546,0.000022971099,0.0000736671,0.00033221106,0.00012309992],"domain_scores_gemma":[0.9973471,0.0007939843,0.00024401014,0.00013007979,0.001305586,0.00017924412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011245115,0.00034258908,0.000174794,0.0014244446,0.001110706,0.0015258155,0.00042233997,0.00041535916,0.0014726004],"category_scores_gemma":[0.006089857,0.000108552296,0.00030941732,0.002459351,0.00032572512,0.00087220885,0.0004592967,0.00087595166,0.00046087336],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014907885,0.0006525589,0.36500394,0.00044932304,0.00036227543,0.00076680357,0.0071280124,0.024384182,0.01966568,0.014762596,0.16118227,0.40415156],"study_design_scores_gemma":[0.000067300745,0.0001755301,0.5277291,0.0001198335,0.00023716787,0.00016499168,0.014004693,0.32430762,0.010843637,0.006634688,0.115509704,0.00020570258],"about_ca_topic_score_codex":0.67835903,"about_ca_topic_score_gemma":0.7634219,"teacher_disagreement_score":0.32164097,"about_ca_system_score_codex":0.0038672667,"about_ca_system_score_gemma":0.0032409255,"threshold_uncertainty_score":0.64707035},"labels":[],"label_agreement":null},{"id":"W7129430514","doi":"10.1109/icecmsn68058.2025.11382909","title":"Emotion Detection in Text using Mixup-Augmented BERT Representations","year":2025,"lang":"","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Recall; Boosting (machine learning); Preprocessor; Embedding; Classifier (UML); Emotion detection; Word embedding","score_opus":0.031795889775730966,"score_gpt":0.3268688815915852,"score_spread":0.2950729918158543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7129430514","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2932644,0.0013890753,0.6801338,0.0008268059,0.00054066174,0.00029029264,0.0033517813,0.010143893,0.010059281],"genre_scores_gemma":[0.8932221,0.00042026778,0.09290227,0.00020800064,0.00017287821,0.00018091859,0.0043341406,0.00023991123,0.008319379],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995764,0.000100504665,0.000028257115,0.00012271709,0.000109373985,0.00006270048],"domain_scores_gemma":[0.9993562,0.00023550638,0.00008146664,0.00011641757,0.00017464119,0.00003572044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049505226,0.0010943735,0.00038388584,0.0011848644,0.0002466391,0.0010638002,0.0005702146,0.0005306992,0.0032273678],"category_scores_gemma":[0.0021902795,0.00022254405,0.00055526156,0.0007327024,0.00034414642,0.0020441457,0.0011352521,0.00077635696,0.0024060842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014917138,0.00042067314,0.011681711,0.00030388733,0.00015022582,0.00047621594,0.0006367198,0.040892985,0.0770668,0.0056354734,0.013086339,0.8481571],"study_design_scores_gemma":[0.000028752758,0.00042855827,0.006780486,0.00003805084,0.00008656454,0.0003344595,0.00044791857,0.9348362,0.034718562,0.009930905,0.012318348,0.00005119402],"about_ca_topic_score_codex":0.00097029627,"about_ca_topic_score_gemma":0.0019470813,"teacher_disagreement_score":0.0032273678,"about_ca_system_score_codex":0.00043742845,"about_ca_system_score_gemma":0.00022538862,"threshold_uncertainty_score":0.010796547},"labels":[],"label_agreement":null},{"id":"W7132444294","doi":"","title":"Voices speaking to and about one another: introducing the Project Dialogism Novel Corpus","year":2022,"lang":"en","type":"article","venue":"NPARC","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Annotation; Polyphony; Software; Computational linguistics; Corpus linguistics; Order (exchange)","score_opus":0.0365974734957194,"score_gpt":0.2672480049374826,"score_spread":0.23065053144176317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132444294","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21415974,0.0062379995,0.31985968,0.013919778,0.0041430905,0.0039702575,0.31897506,0.0072693364,0.11146511],"genre_scores_gemma":[0.23457974,0.0017777876,0.4386344,0.0019875588,0.0010335656,0.010719696,0.2875743,0.0034875672,0.020205438],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9948409,0.002835734,0.0003879537,0.0008559652,0.0008905936,0.00018886555],"domain_scores_gemma":[0.9914057,0.0049381875,0.0005045164,0.0013317387,0.0012395495,0.000580322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042328336,0.000518979,0.0004266121,0.0035870965,0.0026112278,0.0032131441,0.0013677784,0.0014077063,0.010701322],"category_scores_gemma":[0.013387508,0.0005174566,0.0004527183,0.004363265,0.0021437204,0.0033206108,0.006551354,0.0026693272,0.005496735],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007648244,0.0005173842,0.014679595,0.0036449665,0.00007054659,0.0027039063,0.051378533,0.004715833,0.022461722,0.098793454,0.5472015,0.25306773],"study_design_scores_gemma":[0.00005917991,0.00005505595,0.0075759613,0.00034236925,0.000009980965,0.0009499855,0.009897686,0.005486598,0.0030742658,0.013974928,0.9584922,0.00008177911],"about_ca_topic_score_codex":0.0063799997,"about_ca_topic_score_gemma":0.01606969,"teacher_disagreement_score":0.010701322,"about_ca_system_score_codex":0.0014858716,"about_ca_system_score_gemma":0.0014345497,"threshold_uncertainty_score":0.035799503},"labels":[],"label_agreement":null},{"id":"W7132662964","doi":"","title":"#Emotional Tweets","year":2012,"lang":"en","type":"article","venue":"NPARC","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Lexicon; Microblogging; Social media; WordNet; Affect (linguistics); Association (psychology)","score_opus":0.027887192152214357,"score_gpt":0.2661422046080296,"score_spread":0.23825501245581526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132662964","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22407223,0.0014714131,0.055146124,0.0037181296,0.0030233487,0.0029216348,0.4422895,0.011600887,0.25575668],"genre_scores_gemma":[0.41023427,0.0015401671,0.07415084,0.0013202757,0.0011855602,0.0036221554,0.3469047,0.0020828126,0.15895927],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990245,0.00017064069,0.00014691093,0.00016081489,0.0003817338,0.000115367264],"domain_scores_gemma":[0.9985241,0.0004465305,0.00016463803,0.00017518557,0.00059294986,0.00009664228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005237255,0.00078538636,0.00034451226,0.0021939646,0.0009101649,0.0012651959,0.00038226222,0.0005016797,0.040337313],"category_scores_gemma":[0.0035174785,0.0002803144,0.00033142584,0.0015959743,0.0001571435,0.0012120053,0.0010489753,0.0005512796,0.026908897],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010311115,0.00032170207,0.040854163,0.0019834728,0.00009518904,0.0009298225,0.00189817,0.0013967465,0.040756475,0.011949981,0.43376008,0.46502313],"study_design_scores_gemma":[0.00007840178,0.00019777576,0.062953524,0.00018849592,0.00010523253,0.0010797696,0.0014457674,0.013660389,0.03089932,0.007139384,0.8821578,0.00009416706],"about_ca_topic_score_codex":0.0012802022,"about_ca_topic_score_gemma":0.0025883128,"teacher_disagreement_score":0.040337313,"about_ca_system_score_codex":0.00042960062,"about_ca_system_score_gemma":0.00037801897,"threshold_uncertainty_score":0.13494182},"labels":[],"label_agreement":null},{"id":"W7133198871","doi":"10.1109/ocit66168.2025.11400165","title":"LLM-Driven Text Understanding Emotional Analysis","year":2025,"lang":"","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Sentiment analysis; Software; Transformer; Character (mathematics); Text processing; Tone (literature); Cloud computing; Tag cloud","score_opus":0.053552621014047216,"score_gpt":0.30241660925620717,"score_spread":0.24886398824215994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133198871","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01712229,0.0002155223,0.95198303,0.0004915246,0.00014861517,0.0002521331,0.0023725661,0.020030469,0.0073838457],"genre_scores_gemma":[0.25350034,0.0002682831,0.72000796,0.0004298297,0.00015224304,0.0005723683,0.007487211,0.0017095897,0.015872223],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996007,0.00007080298,0.000028319742,0.000101609556,0.0001531015,0.00004545049],"domain_scores_gemma":[0.9992692,0.0002242197,0.000046907204,0.000070318754,0.00035676427,0.00003258856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004928687,0.0007099995,0.0004258018,0.0014136732,0.0003125601,0.0012251975,0.0007907418,0.00065340725,0.01518102],"category_scores_gemma":[0.0028603137,0.00022694381,0.00073093374,0.00064598164,0.00023400392,0.0012364485,0.001122523,0.00068126037,0.008005156],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027351786,0.00009935889,0.0015666329,0.00043808104,0.00004850403,0.00032008754,0.0005083581,0.02304639,0.07300439,0.008094463,0.0466215,0.8459787],"study_design_scores_gemma":[0.000026175878,0.0000740792,0.001645281,0.000055559427,0.000029079802,0.00015332567,0.0003469186,0.91481733,0.03573657,0.01316462,0.03392249,0.000028487992],"about_ca_topic_score_codex":0.0015972947,"about_ca_topic_score_gemma":0.0025983106,"teacher_disagreement_score":0.01518102,"about_ca_system_score_codex":0.00076263066,"about_ca_system_score_gemma":0.00041336555,"threshold_uncertainty_score":0.0507856},"labels":[],"label_agreement":null},{"id":"W7134163090","doi":"10.1109/bigdata66926.2025.11401508","title":"ACVA: An Agentic LLM-Based Framework for CVA Calculation","year":2025,"lang":"","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Set (abstract data type); Component (thermodynamics); Perspective (graphical); Work (physics); Stability (learning theory)","score_opus":0.044245633121498575,"score_gpt":0.3591838459255985,"score_spread":0.3149382128040999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7134163090","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020889135,0.00010600636,0.97866315,0.00013848665,0.0001248872,0.00014204699,0.0007062442,0.014132861,0.0038973547],"genre_scores_gemma":[0.11024017,0.00014916091,0.8787991,0.0002059033,0.00012601017,0.00056728837,0.0022191838,0.0016798504,0.006013464],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881625,0.0004131492,0.000098612654,0.00019545166,0.00039320593,0.00008337363],"domain_scores_gemma":[0.9981653,0.00069559226,0.00015831002,0.000285113,0.00058306695,0.000112659945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017507592,0.00090382696,0.0008549434,0.0018347278,0.0008359187,0.0033237971,0.0025143726,0.001226413,0.016152881],"category_scores_gemma":[0.00940962,0.00047960502,0.0012040277,0.0010712463,0.00039828577,0.0025002086,0.0020992195,0.0017001454,0.0057498673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043354332,0.00036044192,0.0033237005,0.0007103343,0.00027318604,0.00025972206,0.0004201425,0.10166984,0.012135327,0.11464808,0.07000062,0.695765],"study_design_scores_gemma":[0.000027854678,0.000027662963,0.00025578216,0.00004103234,0.00002516175,0.00003873477,0.00004329182,0.9378722,0.003738393,0.037339445,0.020565802,0.000024684703],"about_ca_topic_score_codex":0.0047620195,"about_ca_topic_score_gemma":0.0071090725,"teacher_disagreement_score":0.016152881,"about_ca_system_score_codex":0.001041191,"about_ca_system_score_gemma":0.0013419642,"threshold_uncertainty_score":0.054036796},"labels":[],"label_agreement":null},{"id":"W7134922341","doi":"10.1109/icdmw69685.2025.00284","title":"Aspect-Aware Sentiment Interaction Modeling with DeBERTa for Enhanced Review-Based Recommendations","year":2025,"lang":"","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Perspective (graphical); Identification (biology); Feature (linguistics); Key (lock); Focus (optics)","score_opus":0.037822685350675934,"score_gpt":0.34442308522053744,"score_spread":0.3066003998698615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7134922341","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.067820996,0.0044483542,0.9163549,0.0012054857,0.0002854691,0.00020798286,0.0011631293,0.0034238556,0.005089787],"genre_scores_gemma":[0.75186485,0.001890945,0.23331562,0.000761965,0.00045207064,0.0003093285,0.0024336232,0.00022615983,0.008745442],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994893,0.00016107854,0.00004219191,0.0001450666,0.00011780251,0.00004445225],"domain_scores_gemma":[0.9988732,0.00057652127,0.00010922431,0.000089317764,0.0003009052,0.00005090051],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096209935,0.0008615367,0.0009773658,0.0013701486,0.0003615082,0.0010744685,0.0010370965,0.00088322384,0.0015724981],"category_scores_gemma":[0.0038220172,0.0004849725,0.0010172471,0.0011092637,0.00024340478,0.0013296012,0.00064989424,0.0012470221,0.0014598985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00071732973,0.000420936,0.017616868,0.0004548865,0.00069735694,0.00051302783,0.0008929191,0.2239301,0.02838915,0.009609774,0.021910986,0.6948467],"study_design_scores_gemma":[0.000010838963,0.00003321518,0.0010107323,0.000009406006,0.000054383272,0.000049146798,0.000015005189,0.9942245,0.0008368189,0.0019839993,0.0017583495,0.00001362718],"about_ca_topic_score_codex":0.013015356,"about_ca_topic_score_gemma":0.027995652,"teacher_disagreement_score":0.013015356,"about_ca_system_score_codex":0.00060433487,"about_ca_system_score_gemma":0.0007914052,"threshold_uncertainty_score":0.025879204},"labels":[],"label_agreement":null},{"id":"W7143815617","doi":"10.71465/ajml3081","title":"Predicting Social Trends Using Machine Learning Models","year":2025,"lang":"","type":"article","venue":"American Journal of Machine Learning","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Social media; Time series; Social learning; Support vector machine; Predictive modelling","score_opus":0.027590196106625567,"score_gpt":0.3022395184861487,"score_spread":0.27464932237952316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7143815617","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53525734,0.0033517384,0.4305979,0.007828411,0.00049788936,0.00033712035,0.00547523,0.0015996807,0.015054636],"genre_scores_gemma":[0.9359519,0.0013722121,0.05523677,0.0002843408,0.00049897406,0.00018447751,0.0036586814,0.0000500028,0.002762671],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918777,0.00035185763,0.00006191607,0.00015352739,0.00016277576,0.00008202281],"domain_scores_gemma":[0.99460053,0.0040799677,0.00051733205,0.00017345244,0.0005301574,0.00009865902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019988148,0.00079798285,0.00058644835,0.0029914533,0.0005235825,0.0018080677,0.00075221475,0.0010141901,0.0015548458],"category_scores_gemma":[0.008825497,0.0002807466,0.00074753945,0.002325053,0.00029674254,0.0024342223,0.0004951099,0.0013675713,0.0009813117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017746422,0.0005543499,0.097217835,0.00022033646,0.0003308633,0.0003029346,0.00032442977,0.6769838,0.0012753295,0.01610305,0.011890433,0.19461921],"study_design_scores_gemma":[0.0000047652625,0.000016954495,0.0018483443,0.000011749211,0.000009835068,0.000011786044,0.000040780586,0.9908886,0.00012515625,0.0062234644,0.000813498,0.000004984531],"about_ca_topic_score_codex":0.009243893,"about_ca_topic_score_gemma":0.009459972,"teacher_disagreement_score":0.009243893,"about_ca_system_score_codex":0.0008943953,"about_ca_system_score_gemma":0.0006130049,"threshold_uncertainty_score":0.018380225},"labels":[],"label_agreement":null},{"id":"W7144341289","doi":"10.71465/ajml3046","title":"Enhancing Social Media Analytics with Machine Learning Techniques","year":2024,"lang":"","type":"article","venue":"American Journal of Machine Learning","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Social media; Social media analytics; Sentiment analysis; Analytics; Predictive analytics; Supervised learning; Big data","score_opus":0.011825259445127375,"score_gpt":0.2739776808386315,"score_spread":0.26215242139350414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7144341289","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021030063,0.0011402427,0.96286887,0.003287184,0.00015496425,0.00020729942,0.00067480677,0.0038899258,0.0067466265],"genre_scores_gemma":[0.38898057,0.002422645,0.6018645,0.0009206123,0.0006987498,0.00026040807,0.0017529575,0.00033733,0.0027622483],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977701,0.00097078405,0.00010776022,0.00026682307,0.00078699755,0.00009759047],"domain_scores_gemma":[0.98995155,0.0070775156,0.0005509097,0.0010455627,0.0012529449,0.000121560726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003202459,0.0015107939,0.0007756401,0.0031080642,0.00045702764,0.0023586226,0.0010759712,0.0009109717,0.0018306577],"category_scores_gemma":[0.013052406,0.00043538373,0.00081980164,0.002060391,0.00076820806,0.0050282157,0.002348804,0.002155171,0.0019210239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014488082,0.0010566518,0.011807425,0.00070371735,0.0003255019,0.00019372687,0.000543829,0.11119819,0.021210482,0.027508447,0.015250909,0.81005615],"study_design_scores_gemma":[0.000013797219,0.00006254596,0.001270659,0.000064686035,0.000033633456,0.00004895039,0.00013934486,0.9242162,0.008976357,0.05661954,0.008528291,0.000026006333],"about_ca_topic_score_codex":0.0017962732,"about_ca_topic_score_gemma":0.0028329259,"teacher_disagreement_score":0.003202459,"about_ca_system_score_codex":0.00066433364,"about_ca_system_score_gemma":0.0009034937,"threshold_uncertainty_score":0.016936421},"labels":[],"label_agreement":null},{"id":"W7160377892","doi":"10.1109/iccit68739.2025.11491444","title":"A Comparative Study of Deep Learning Models for Bias Detection in Bangla Job Advertisements","year":2025,"lang":"","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Deep learning; Bengali; Key (lock); Headline","score_opus":0.0915675975624389,"score_gpt":0.33837747571987997,"score_spread":0.24680987815744107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7160377892","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9187695,0.002219528,0.06722722,0.0008703372,0.00017960893,0.00009583894,0.00059264817,0.0011201529,0.0089251],"genre_scores_gemma":[0.9763248,0.0004929566,0.01832242,0.000119081815,0.000023764816,0.000042245938,0.00075132004,0.000041493593,0.0038819148],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956554,0.0001683285,0.000036160658,0.000092886454,0.000065549655,0.00007156664],"domain_scores_gemma":[0.99866474,0.00080006884,0.00007883755,0.00008155154,0.00032205082,0.00005283734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015776219,0.0008223225,0.00036060158,0.0005623874,0.00029615074,0.00072939,0.00053816306,0.000614075,0.0011764993],"category_scores_gemma":[0.0032091115,0.00020342444,0.0004975966,0.00043663417,0.00022522792,0.00076342066,0.00041112085,0.00079876,0.00059012655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017294615,0.00064634497,0.043183465,0.0004579744,0.0003160724,0.00044004092,0.000831845,0.33613136,0.017805135,0.0029593653,0.006284547,0.5892143],"study_design_scores_gemma":[0.000012118269,0.0001439424,0.003720115,0.000024103641,0.00004329992,0.000025429401,0.00013783343,0.99112344,0.0035654532,0.00044985133,0.000741246,0.000013227344],"about_ca_topic_score_codex":0.015908623,"about_ca_topic_score_gemma":0.013895322,"teacher_disagreement_score":0.015908623,"about_ca_system_score_codex":0.0010235313,"about_ca_system_score_gemma":0.00069565227,"threshold_uncertainty_score":0.031632066},"labels":[],"label_agreement":null},{"id":"W7160593040","doi":"10.53842/juki.v6i2.681","title":"Analisis Sentimen Berbasis Jaringan LSTM dan BERT terhadap Diskusi Twitter tentang Pemilu 2024","year":2024,"lang":"","type":"article","venue":"JUKI Jurnal Komputer dan Informatika","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Deep learning","score_opus":0.019479931932731263,"score_gpt":0.26598887241820696,"score_spread":0.2465089404854757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7160593040","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6218703,0.00920637,0.14654782,0.0076996046,0.0034036862,0.00075430126,0.09741055,0.039900698,0.07320674],"genre_scores_gemma":[0.7997894,0.0023783722,0.054994058,0.0008694552,0.00034226666,0.0004448502,0.08498513,0.0012322157,0.054964166],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913776,0.000117243595,0.00007365858,0.00025049274,0.00025384536,0.00016705794],"domain_scores_gemma":[0.9989937,0.0003673352,0.00006415853,0.00015359803,0.00035405406,0.00006709843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091799325,0.001949958,0.00068564876,0.0019949488,0.000882865,0.0022265392,0.0007852142,0.0011830515,0.01342775],"category_scores_gemma":[0.0048183035,0.00042837113,0.0010415144,0.0017973181,0.00042126258,0.0033246658,0.001222068,0.0020077233,0.009696917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017921044,0.00045568775,0.049766727,0.0012771356,0.00041873482,0.0012206745,0.0013719107,0.024185134,0.025742322,0.005355098,0.2211728,0.6672417],"study_design_scores_gemma":[0.00012358771,0.00081519596,0.09030941,0.00050908636,0.00051755743,0.0012554801,0.0057450966,0.62676865,0.06409358,0.015749069,0.19382203,0.00029120102],"about_ca_topic_score_codex":0.018653719,"about_ca_topic_score_gemma":0.02952003,"teacher_disagreement_score":0.018653719,"about_ca_system_score_codex":0.0011754776,"about_ca_system_score_gemma":0.0011835734,"threshold_uncertainty_score":0.044920325},"labels":[],"label_agreement":null},{"id":"W7164152039","doi":"10.1109/icspis68676.2025.11551779","title":"A Framework for Aspect-Based Sentiment and Opinion Mining in Persian Language: Dataset Creation and Domain Application in Hotel Reviews","year":2025,"lang":"","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Douglas College","funders":"","keywords":"Domain (mathematical analysis); Sentiment analysis; Persian; Public opinion; Topic model","score_opus":0.023499849564655097,"score_gpt":0.35093793070584917,"score_spread":0.32743808114119405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7164152039","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24982819,0.0026236256,0.6308884,0.0017516731,0.00044825443,0.0021640197,0.056375228,0.043018136,0.012902539],"genre_scores_gemma":[0.34853396,0.00069340825,0.5591457,0.0005527903,0.0001576677,0.0017646238,0.083919205,0.000543024,0.0046896297],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994443,0.000169987,0.000056715668,0.00015727586,0.00012700867,0.000044739867],"domain_scores_gemma":[0.9991443,0.00017992743,0.00010400925,0.00017315485,0.00033579592,0.00006279937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011085448,0.0007503873,0.00031213037,0.0024431509,0.00046953923,0.00083213334,0.0007005933,0.00044756834,0.0011083405],"category_scores_gemma":[0.0034788407,0.00019232596,0.00064789027,0.0011526286,0.0002707271,0.0010507602,0.0010447596,0.00066131825,0.0012197939],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007169518,0.0005940792,0.045885243,0.0013252773,0.0003456599,0.0012433057,0.001527567,0.017451147,0.068880595,0.011481128,0.13272934,0.71781975],"study_design_scores_gemma":[0.00015076854,0.000553385,0.059361875,0.00021257918,0.00015268446,0.0013759173,0.0014080104,0.7134141,0.043240406,0.017024925,0.16294444,0.00016095977],"about_ca_topic_score_codex":0.0073237107,"about_ca_topic_score_gemma":0.015924921,"teacher_disagreement_score":0.0073237107,"about_ca_system_score_codex":0.0006154179,"about_ca_system_score_gemma":0.0008991396,"threshold_uncertainty_score":0.01456219},"labels":[],"label_agreement":null},{"id":"W851958356","doi":"10.1016/j.techfore.2015.06.035","title":"#iamhappybecause: Gross National Happiness through Twitter analysis and big data","year":2015,"lang":"en","type":"article","venue":"Technological Forecasting and Social Change","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Social media; Happiness; Quarter (Canadian coin); Sentiment analysis; Turkish; Psychology; Computer science; Social psychology; Geography; Artificial intelligence; World Wide Web","score_opus":0.605223966282354,"score_gpt":0.3686569948479723,"score_spread":0.23656697143438166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W851958356","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051815264,0.0006332247,0.009254504,0.018443104,0.004085631,0.00038187156,0.6594234,0.03968931,0.2162736],"genre_scores_gemma":[0.1395264,0.0008467487,0.016939044,0.0018316024,0.0016751689,0.00043320737,0.5501365,0.009004348,0.27960703],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99963367,0.000047709156,0.000023721648,0.0000486628,0.00018318935,0.00006311064],"domain_scores_gemma":[0.9982324,0.00035987084,0.0001563977,0.00023917724,0.00064146414,0.0003706637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007164191,0.0006977265,0.0002830291,0.0016924718,0.00080518273,0.002584475,0.00052985334,0.00080832466,0.074981004],"category_scores_gemma":[0.0038518286,0.00031433903,0.0003063152,0.001815197,0.0001910442,0.0026799454,0.0017017793,0.00075418426,0.04441014],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000092701965,0.000021010239,0.009202026,0.00006912407,0.000019901545,0.000075523516,0.00015461993,0.00027244465,0.00062429794,0.0013262116,0.9630779,0.025064114],"study_design_scores_gemma":[0.00006225787,0.000075832766,0.07370705,0.00008932727,0.000036631147,0.000121208395,0.001105334,0.015311411,0.005010627,0.003758205,0.9006268,0.00009544462],"about_ca_topic_score_codex":0.023247933,"about_ca_topic_score_gemma":0.042736996,"teacher_disagreement_score":0.074981004,"about_ca_system_score_codex":0.000850146,"about_ca_system_score_gemma":0.0006988332,"threshold_uncertainty_score":0.25083643},"labels":[],"label_agreement":null},{"id":"W978775847","doi":"10.1007/978-3-319-06028-6_56","title":"An Information Retrieval-Based Approach to Determining Contextual Opinion Polarity of Words","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Polarity (international relations); Set (abstract data type); Computer science; Context (archaeology); Natural language processing; Task (project management); Artificial intelligence; Sentiment analysis; Information retrieval; Geography; Engineering; Chemistry","score_opus":0.023531342775331424,"score_gpt":0.2690822061012785,"score_spread":0.24555086332594706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W978775847","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08956828,0.0030272894,0.87502855,0.00096871454,0.0007655325,0.00096932723,0.0024528669,0.0026412723,0.024578137],"genre_scores_gemma":[0.3788116,0.0012139232,0.6078845,0.00031136314,0.00067228073,0.00053866924,0.0030192672,0.00015437217,0.0073940908],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988439,0.00024046034,0.00010369644,0.00022342343,0.0004896281,0.00009890114],"domain_scores_gemma":[0.99816555,0.0006278167,0.00015091868,0.000099808065,0.00089885446,0.000057119112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011655327,0.00078975986,0.00097501406,0.0046408856,0.0009277182,0.0021804308,0.00095060864,0.00071503787,0.004082432],"category_scores_gemma":[0.004478909,0.00028730952,0.00093683833,0.0040157307,0.00043646252,0.0020722789,0.0008306867,0.0009076742,0.002604493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005446251,0.0004046476,0.0048005185,0.00060035445,0.00019165328,0.00027181994,0.000574714,0.0032046873,0.08324061,0.010716764,0.01845842,0.87699103],"study_design_scores_gemma":[0.00021174323,0.0012115458,0.033856615,0.0002899021,0.0011276007,0.0015073739,0.00197871,0.76627433,0.092484914,0.05042747,0.05031472,0.00031507193],"about_ca_topic_score_codex":0.0031727583,"about_ca_topic_score_gemma":0.0056446884,"teacher_disagreement_score":0.0046408856,"about_ca_system_score_codex":0.0007531407,"about_ca_system_score_gemma":0.0010232974,"threshold_uncertainty_score":0.013657153},"labels":[],"label_agreement":null}]}