{"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,"ca_institutions":"University of Ottawa","funders":"","keywords":"Thesaurus; Computer science; WordNet; Natural language processing; Artificial intelligence; Lexicon; Emotive; Task (project management); Information retrieval","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001299481,0.0008032791,0.0005697629,0.003692144,0.0005812922,0.00204637,0.0008422498,0.001129443,0.003661528],"category_scores_gemma":[0.009858627,0.0004124168,0.000949185,0.00174881,0.0004547245,0.003841803,0.001263718,0.0008433066,0.004979916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004668783,"about_ca_system_score_gemma":0.0005984911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00275519,"about_ca_topic_score_gemma":0.003300914,"domain_scores_codex":[0.9986235,0.0003626695,0.0002558771,0.0004130474,0.0002963214,0.00004865954],"domain_scores_gemma":[0.9979724,0.0006118331,0.0002633003,0.0004174353,0.0006632161,0.0000718345],"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":[0.0003509434,0.0002731675,0.006715426,0.00113718,0.0003085082,0.0005575768,0.001720215,0.003021452,0.08389239,0.008730079,0.03045701,0.862836],"study_design_scores_gemma":[0.0002724186,0.00135724,0.0585288,0.001219724,0.0007283935,0.005732327,0.002721183,0.3497336,0.1551237,0.0558354,0.3682053,0.0005418839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08754715,0.002230471,0.8639063,0.0008480083,0.0006570606,0.001419381,0.005487919,0.01678347,0.02112016],"genre_scores_gemma":[0.2284894,0.0006710929,0.7511139,0.0002919333,0.0001236015,0.0007969072,0.01078769,0.0005661229,0.007159489],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003692144,"threshold_uncertainty_score":0.01224899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1671758249587417,"score_gpt":0.3072526675717412,"score_spread":0.1400768426129995,"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."}}