{"id":"W4386566452","doi":"10.18653/v1/2023.findings-eacl.136","title":"Best Practices in the Creation and Use of Emotion Lexicons","year":2023,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Lexicon; Set (abstract data type); Computer science; Sentiment analysis; Tracking (education); Work (physics); Emotion classification; Word (group theory); Cognitive psychology; Emotion detection; Artificial intelligence; Natural language processing; Psychology; Emotion recognition; Linguistics","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":[0.09473101,0.002094017,0.001583984,0.007937859,0.003024888,0.02305382,0.006650418,0.00492249,0.005998766],"category_scores_gemma":[0.2652323,0.002188947,0.002437148,0.005113686,0.01184449,0.0231689,0.01104111,0.008967706,0.01087711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003728516,"about_ca_system_score_gemma":0.004392107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003465096,"about_ca_topic_score_gemma":0.003951462,"domain_scores_codex":[0.867516,0.08265209,0.01444177,0.009644687,0.02372523,0.002020246],"domain_scores_gemma":[0.744396,0.1196724,0.008838125,0.07556939,0.04887409,0.002649953],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003524768,0.0004105839,0.003272363,0.002870254,0.000331109,0.0005375035,0.0300724,0.004823359,0.0131201,0.2724179,0.05037818,0.6214139],"study_design_scores_gemma":[0.0001404484,0.0001324724,0.001739165,0.004260207,0.000205568,0.0009757338,0.008520938,0.02209127,0.02119156,0.5093792,0.4309967,0.0003666304],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00519631,0.002148853,0.937929,0.02101894,0.0006293591,0.0008154425,0.0003661546,0.004129741,0.02776613],"genre_scores_gemma":[0.04601851,0.001754658,0.9424071,0.002505001,0.0002737448,0.0009554974,0.0007061416,0.001796433,0.003582818],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.09473101,"threshold_uncertainty_score":0.5009915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.168320874261917,"score_gpt":0.3703668228333954,"score_spread":0.2020459485714784,"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."}}