{"id":"W2161926933","doi":"","title":"When Specialists and Generalists Work Together: Overcoming Domain Dependence in Sentiment Tagging","year":2008,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":165,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Software portability; Computer science; Lexicon; WordNet; Classifier (UML); Artificial intelligence; Annotation; Natural language processing; Domain (mathematical analysis); Weighting; Sentiment analysis; Precision and recall; Training set; Information retrieval; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003879673,0.0001464065,0.000205801,0.000181008,0.0001580187,0.0001584274,0.000382299,0.00004525944,0.000140053],"category_scores_gemma":[0.00001210322,0.000135675,0.00006061343,0.0003729642,0.00004097843,0.0004293028,0.0003051516,0.00009357546,0.00002575905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005770084,"about_ca_system_score_gemma":0.00002431142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001789682,"about_ca_topic_score_gemma":0.00009943821,"domain_scores_codex":[0.9985272,0.00008083867,0.0002980182,0.0004555567,0.0003366842,0.0003016604],"domain_scores_gemma":[0.9994075,0.00005946144,0.00008042232,0.0003328924,0.00002814165,0.00009158381],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002528797,0.0002141523,0.8581666,0.00001739738,0.0001070311,0.0006071043,0.02051322,0.000758911,0.00439764,0.07357489,0.01052248,0.03109525],"study_design_scores_gemma":[0.007640999,0.0001854685,0.647219,0.0006954171,0.00005663216,0.0005618653,0.001779231,0.1783038,0.02226781,0.01514389,0.1218216,0.004324284],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8269113,0.0003877055,0.1672056,0.001473321,0.0002340491,0.0001115279,2.479674e-7,0.00007332246,0.003602843],"genre_scores_gemma":[0.8054125,0.00008645782,0.1912404,0.001172759,0.0001346385,0.000006399074,0.000002110382,0.000009083601,0.001935673],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2109476,"threshold_uncertainty_score":0.5532667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02636169918772258,"score_gpt":0.2488749297044931,"score_spread":0.2225132305167705,"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."}}