{"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,"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","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":[0.007217932,0.0001002626,0.0004378361,0.002302445,0.0001408703,0.0002238107,0.001237115,0.00005862428,0.0001864888],"category_scores_gemma":[0.0003521981,0.00008003078,0.0004284105,0.003872633,0.0001361259,0.0005576493,0.0002733444,0.0003571259,0.00004132438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004369549,"about_ca_system_score_gemma":0.0001221729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002320993,"about_ca_topic_score_gemma":0.0000190418,"domain_scores_codex":[0.9962405,0.0002850119,0.001177809,0.0001885505,0.001728562,0.0003795446],"domain_scores_gemma":[0.9972973,0.0004860308,0.0003172211,0.0004309442,0.001288486,0.0001800061],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007221759,0.0004317302,0.01015969,0.00001894182,0.001648374,0.00001826176,0.002890509,0.02407097,0.01236056,0.0981238,0.0004640892,0.8497409],"study_design_scores_gemma":[0.000028817,0.0005277306,0.003912264,0.00004936266,0.0001811171,0.000007125131,0.000841019,0.8474557,0.1414292,0.003621453,0.001797931,0.0001483217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2589856,0.0001092665,0.7387027,0.0004522035,0.0002406387,0.00006350721,5.921028e-7,0.000007264086,0.001438261],"genre_scores_gemma":[0.9913398,0.00005721714,0.008332551,0.000023209,0.0001536995,9.575153e-7,8.630357e-7,0.000004074527,0.00008762798],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8495925,"threshold_uncertainty_score":0.326356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1410959079151924,"score_gpt":0.4268329994839138,"score_spread":0.2857370915687214,"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."}}