{"id":"W3157029782","doi":"10.7717/peerj-cs.786","title":"AdCOFE: Advanced Contextual Feature Extraction in conversations for emotion classification","year":2021,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Feature extraction; Computer science; Feature (linguistics); Artificial intelligence; Extraction (chemistry); Pattern recognition (psychology); Natural language processing; Psychology; Linguistics; Chemistry; Chromatography","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":[0.0006093253,0.001119407,0.0006239953,0.001338555,0.0004820776,0.0006623542,0.0005798888,0.000676733,0.003959919],"category_scores_gemma":[0.00229241,0.0001655787,0.0008503157,0.0006803886,0.0001755968,0.001043771,0.001116678,0.0008454154,0.002075042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003332038,"about_ca_system_score_gemma":0.0004197877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003011192,"about_ca_topic_score_gemma":0.004254116,"domain_scores_codex":[0.9995337,0.0000963586,0.00003288814,0.0001405146,0.00009735973,0.00009925744],"domain_scores_gemma":[0.9995435,0.0001702227,0.00004289465,0.00006492971,0.0001440214,0.00003434387],"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":[0.0006516811,0.0004295606,0.00728332,0.0003535568,0.0001539902,0.0003693039,0.0008824079,0.005874946,0.07889049,0.002582591,0.02737144,0.8751567],"study_design_scores_gemma":[0.0001042496,0.0006496556,0.04052404,0.000158193,0.0002309558,0.0009877683,0.001510363,0.7975214,0.07306901,0.01822089,0.06684399,0.0001794229],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09293097,0.001499988,0.8794326,0.0005319942,0.000357714,0.0005442106,0.00689213,0.01159095,0.006219415],"genre_scores_gemma":[0.5608758,0.0006585882,0.4129653,0.0002850757,0.0002815427,0.001039684,0.01496179,0.0004287531,0.008503536],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003959919,"threshold_uncertainty_score":0.01324725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03654557621958029,"score_gpt":0.3188550560258584,"score_spread":0.2823094798062781,"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."}}