{"id":"W2040467972","doi":"10.1111/j.1467-8640.2012.00460.x","title":"CROWDSOURCING A WORD–EMOTION ASSOCIATION LEXICON","year":2012,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2584,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"National Research Council Canada","keywords":"Lexicon; Crowdsourcing; Computer science; Annotation; Word (group theory); Natural language processing; Polarity (international relations); Term (time); Sentiment analysis; Association (psychology); Artificial intelligence; Quality (philosophy); Psychology; Linguistics; World Wide Web","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006199582,0.001406892,0.0009753044,0.005771454,0.002336238,0.002806749,0.00130232,0.001190452,0.006337135],"category_scores_gemma":[0.0290142,0.0005766068,0.001259952,0.004008322,0.001156701,0.00319832,0.004650285,0.001689884,0.004142233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001580819,"about_ca_system_score_gemma":0.002594051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004063209,"about_ca_topic_score_gemma":0.007677818,"domain_scores_codex":[0.9925218,0.003229932,0.0005862102,0.001208027,0.002185818,0.0002680816],"domain_scores_gemma":[0.9810643,0.009648737,0.001117331,0.002770295,0.004849118,0.000550241],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001650195,0.0008002233,0.02015712,0.00253606,0.00041115,0.001888631,0.01182904,0.02388756,0.1046372,0.0509766,0.1448791,0.6363473],"study_design_scores_gemma":[0.0004742019,0.0003897514,0.02468498,0.0005795439,0.0003729106,0.0009486367,0.0108412,0.3581952,0.05164614,0.1993568,0.3520466,0.000464031],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1321987,0.0008602703,0.7681553,0.00443903,0.001425265,0.004171505,0.01999146,0.007718172,0.06104025],"genre_scores_gemma":[0.504193,0.000529061,0.4399024,0.001289259,0.0005178986,0.004337996,0.02791705,0.001374388,0.01993894],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006337135,"threshold_uncertainty_score":0.03278691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03803301095524386,"score_gpt":0.2981967277978994,"score_spread":0.2601637168426555,"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."}}