{"id":"W4387042344","doi":"10.1109/access.2023.3319455","title":"TEmoX: Classification of Textual Emotion Using Ensemble of Transformers","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Athabasca University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Engineering and Technology, Lahore","keywords":"Bengali; Artificial intelligence; Computer science; Natural language processing; Sentiment analysis; Disgust; Categorization; Classifier (UML); Machine learning; Transformer; Anger; Information retrieval; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002456415,0.00005506578,0.0001294701,0.0002436577,0.00004349786,0.00004272748,0.0004007778,0.00003231949,0.00001097719],"category_scores_gemma":[0.000007730648,0.00005176076,0.0000721641,0.001062997,0.00002399693,0.0005497739,0.00003055696,0.00003078522,0.000008044312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001244105,"about_ca_system_score_gemma":0.00003109817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005036547,"about_ca_topic_score_gemma":0.000004645723,"domain_scores_codex":[0.9992054,0.00002824315,0.00026627,0.0001580918,0.0002320033,0.0001100555],"domain_scores_gemma":[0.9995173,0.00004120858,0.0001612778,0.0001798407,0.00007755039,0.00002278131],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001275778,0.0001108865,0.02256238,0.0001162739,0.0001035102,0.000002040872,0.002119697,0.008503307,0.7970509,0.00666193,0.001059736,0.1616966],"study_design_scores_gemma":[0.0002305625,0.00002854853,0.0360958,0.00005061879,0.00002118121,8.905905e-7,0.0003312841,0.7188737,0.2438551,0.0003287845,0.00008290213,0.0001006697],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6334501,0.000008768691,0.3656958,0.00006872479,0.0001587804,0.0000428477,9.025717e-7,0.00002624198,0.0005478859],"genre_scores_gemma":[0.998596,0.00001737449,0.001292946,0.0000107166,0.00002758909,0.000001303979,0.000004886078,0.000003673529,0.00004547791],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7103704,"threshold_uncertainty_score":0.2110742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.125216788340946,"score_gpt":0.3655042593502999,"score_spread":0.2402874710093539,"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."}}