{"id":"W2032189893","doi":"10.5539/cis.v2n3p71","title":"Text Emotion Computing under Cognition Vision","year":2009,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Cognition; Emotion recognition; Computation; Emotion detection; Cognitive computing; Affective computing; Cognitive psychology; Artificial intelligence; Cognitive science; Emotional intelligence; Natural language processing; Psychology; Algorithm; Social psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003031768,0.0003088167,0.000235126,0.0005888248,0.000334741,0.002107443,0.0004036183,0.0004941132,0.002763794],"category_scores_gemma":[0.001449013,0.00008359459,0.0004860383,0.0004320646,0.0006936788,0.003146708,0.0006739374,0.0007086772,0.0005554539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008832099,"about_ca_system_score_gemma":0.0002624917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00151499,"about_ca_topic_score_gemma":0.0007614804,"domain_scores_codex":[0.9997299,0.00005508168,0.00001473994,0.0001019102,0.00006392415,0.00003440073],"domain_scores_gemma":[0.9997712,0.00005578484,0.00002919931,0.00003245893,0.00007567539,0.00003570257],"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":[0.0004537009,0.0001361647,0.002482646,0.000355748,0.0001025015,0.0002362405,0.001678329,0.009445736,0.05771704,0.424015,0.02223136,0.4811456],"study_design_scores_gemma":[0.00006458539,0.0001983085,0.009043625,0.00008590522,0.000147475,0.0003336435,0.001019658,0.3052221,0.0242884,0.6111534,0.04838382,0.00005903154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.10804,0.004327897,0.8203763,0.005533232,0.0007766777,0.0001363086,0.0003909319,0.002326388,0.05809228],"genre_scores_gemma":[0.8856214,0.001432096,0.1027079,0.0006480761,0.0004552577,0.00008958256,0.0003208276,0.0001084796,0.008616562],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002763794,"threshold_uncertainty_score":0.009245872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00964154893826375,"score_gpt":0.251901892938125,"score_spread":0.2422603439998612,"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."}}