{"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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.0007135923,0.00009604797,0.0001033476,0.0004170496,0.0005124281,0.001222547,0.0004088043,0.00003477449,0.000003779749],"category_scores_gemma":[0.00002242128,0.00008365097,0.00003060447,0.001252371,0.00008918427,0.01471865,0.000156986,0.00006987045,0.00005039903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003207652,"about_ca_system_score_gemma":0.00005325516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002100245,"about_ca_topic_score_gemma":2.106854e-7,"domain_scores_codex":[0.998783,0.00002548755,0.0003188654,0.000217534,0.0004561142,0.0001990324],"domain_scores_gemma":[0.9991781,0.00002641871,0.0001400759,0.0002289306,0.0003242067,0.0001022625],"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":[0.000001411814,0.0000186164,0.00007427,0.000004188131,0.000001759037,2.092723e-7,0.0009214166,0.003237593,0.0004571918,0.144653,0.0002444783,0.8503858],"study_design_scores_gemma":[0.0001832131,0.0001026669,0.08219997,0.00002157461,0.000002771499,0.00001197033,0.00002987157,0.9132841,0.0003266802,0.002984002,0.0007385446,0.0001146514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02420977,0.00001144007,0.9701951,0.00108515,0.0002237859,0.00008493076,3.6938e-7,0.0001171062,0.004072421],"genre_scores_gemma":[0.9653026,0.00002097105,0.03121994,0.003391006,0.00004866817,3.86207e-7,0.00001014432,9.485852e-7,0.000005294544],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9410928,"threshold_uncertainty_score":0.9998143,"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."}}