{"id":"W2469813598","doi":"","title":"A Pragma-Semantic Analysis of the Emotion/SentimentRelation in Debates","year":2016,"lang":"en","type":"preprint","venue":"Institutional Research Information System University of Turin (University of Turin)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Sentiment analysis; Argumentative; Computer science; Relation (database); Polarity (international relations); Emotion detection; Natural language processing; Emotion classification; Artificial intelligence; Emotion recognition; Semantics (computer science); Linguistics; Data mining","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":[0.001492083,0.0003567548,0.000357036,0.00387654,0.0014187,0.003332436,0.0006507607,0.000681114,0.006762996],"category_scores_gemma":[0.005791852,0.0002376176,0.001373671,0.002977016,0.0008677726,0.00479929,0.001855612,0.001175213,0.001435635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001021244,"about_ca_system_score_gemma":0.0009915475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002178119,"about_ca_topic_score_gemma":0.002043431,"domain_scores_codex":[0.9987219,0.0005337843,0.00009491412,0.0003162516,0.000225817,0.0001073014],"domain_scores_gemma":[0.9983917,0.0006159958,0.0001895214,0.0002344729,0.0004620365,0.0001063727],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0009343066,0.0005160772,0.04891846,0.0006875699,0.0003720239,0.0005057981,0.006911915,0.008085302,0.03449282,0.3782991,0.01859995,0.5016766],"study_design_scores_gemma":[0.00008171186,0.0002390332,0.07892943,0.0002050239,0.000413883,0.0005641706,0.007247601,0.5128871,0.01781733,0.3216943,0.05980381,0.0001166368],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.401071,0.0008965909,0.5490139,0.00296453,0.0002785345,0.0005254329,0.006168285,0.002550967,0.03653085],"genre_scores_gemma":[0.8670954,0.0001621697,0.1260335,0.00009154185,0.0001166486,0.0001629083,0.002985122,0.0001613752,0.003191351],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006762996,"threshold_uncertainty_score":0.02262443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03747747139533111,"score_gpt":0.2576125103003175,"score_spread":0.2201350389049863,"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."}}