{"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,"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","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001980422,0.0001713958,0.0002335679,0.0001567313,0.000191599,0.0001921472,0.0001339728,0.0001793182,0.00000615351],"category_scores_gemma":[0.00001443611,0.0001583302,0.0002488918,0.0006928365,0.00002995187,0.0002656088,0.0001139539,0.0004258779,4.504266e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001611699,"about_ca_system_score_gemma":0.0000204597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004688236,"about_ca_topic_score_gemma":0.00003897855,"domain_scores_codex":[0.9987488,0.0000723276,0.0001904982,0.0005226537,0.0003044532,0.0001612674],"domain_scores_gemma":[0.9995256,0.00008071449,0.0001208559,0.0001359078,0.00005899945,0.00007793839],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005022249,0.001130815,0.2612569,0.0003418228,0.009630932,0.0001201372,0.03022126,0.0497944,0.02087922,0.6011606,0.001075371,0.02388625],"study_design_scores_gemma":[0.0003667515,0.0000361536,0.07703399,0.0000361135,0.0001338579,0.000001538194,0.0005051651,0.9209302,0.0002110546,0.0005782836,0.0000029606,0.0001639323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2807736,0.0001650289,0.716103,0.002302903,0.0002136996,0.0001744022,0.00006424203,0.00005136978,0.0001518508],"genre_scores_gemma":[0.9681072,0.00003859084,0.03073185,0.0008879966,0.00004630848,0.000003575057,0.0001492153,0.000007205277,0.00002806919],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8711358,"threshold_uncertainty_score":0.6456519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01457641908456721,"score_gpt":0.2801459802758872,"score_spread":0.26556956119132,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}