{"id":"W4405820835","doi":"10.54254/2754-1169/2024.18696","title":"A Comprehensive Evaluation of Emotion Recognition Techniques: Model and Data Analysis","year":2024,"lang":"en","type":"article","venue":"Advances in Economics Management and Political Sciences","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Convolutional neural network; Dimensionality reduction; Feature extraction; Principal component analysis; Artificial intelligence; Context (archaeology); Emotion recognition; Pattern recognition (psychology); Emotion classification; Generalization; Electroencephalography; Machine learning; Speech recognition; Psychology","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":[0.0008197437,0.00006496543,0.0001144254,0.000371205,0.00003992675,0.00004485368,0.00009379737,0.00003357262,0.00005788839],"category_scores_gemma":[0.00001069398,0.00006038455,0.0000187227,0.0002607431,0.0002438389,0.0005368831,0.00007872798,0.00004247341,0.000004457571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002530252,"about_ca_system_score_gemma":0.00001001847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002879371,"about_ca_topic_score_gemma":0.00007858512,"domain_scores_codex":[0.9991159,0.00006624808,0.0002078174,0.0003821152,0.00007961743,0.0001482682],"domain_scores_gemma":[0.9996946,0.00008081175,0.00003845578,0.0001233887,0.0000260581,0.00003669457],"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":[0.00000623061,0.00003400297,0.001005991,0.00005978995,0.00006609307,5.09133e-7,0.0001441335,0.0003879022,0.000002199734,0.3497093,0.00001605863,0.6485677],"study_design_scores_gemma":[0.0002180728,0.00006371635,0.007562303,0.00006079761,0.0004244045,0.000002647655,0.001776428,0.5473394,0.00002196327,0.4408529,0.001551859,0.0001255396],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8064518,0.003516749,0.01038943,0.0006238242,0.0003054992,0.0004802887,0.000107054,0.00004705568,0.1780783],"genre_scores_gemma":[0.9934511,0.002806627,0.003448796,0.0001167406,0.00002308616,0.0000201375,0.00007264975,0.00000275912,0.00005811658],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6484422,"threshold_uncertainty_score":0.2462411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1364905517106403,"score_gpt":0.4272675222700044,"score_spread":0.2907769705593641,"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."}}