{"id":"W1577585001","doi":"10.1609/icwsm.v4i1.14073","title":"Generating Domain-Specific Clues Using News Corpus for Sentiment Classification","year":2010,"lang":"en","type":"article","venue":"Proceedings of the International AAAI Conference on Web and Social Media","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Sentence; Sentiment analysis; Domain (mathematical analysis); Natural language processing; Collocation (remote sensing); Set (abstract data type); Artificial intelligence; Event (particle physics); Subject (documents); Expression (computer science); Machine learning; World Wide Web; Mathematics","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":[0.001426875,0.001481554,0.001138786,0.006572106,0.001206706,0.0012575,0.0008013578,0.0009766272,0.003949034],"category_scores_gemma":[0.008787495,0.0006788569,0.000807324,0.003630853,0.0003677001,0.002276542,0.001136122,0.001269884,0.003013429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005647113,"about_ca_system_score_gemma":0.001112809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002159663,"about_ca_topic_score_gemma":0.004336455,"domain_scores_codex":[0.9991406,0.0002197942,0.00009408128,0.0002292224,0.000257861,0.00005841266],"domain_scores_gemma":[0.9945304,0.002635512,0.000311525,0.0004984844,0.001798232,0.0002258037],"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":[0.001356538,0.000797742,0.01274588,0.001418684,0.0001843205,0.001762784,0.001334493,0.01355021,0.1314773,0.005522595,0.03812135,0.7917281],"study_design_scores_gemma":[0.000416029,0.0009636102,0.02050748,0.0003885461,0.0005289409,0.00176837,0.002187216,0.7442001,0.1259521,0.01826138,0.08456463,0.0002615847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3247955,0.002296689,0.6327946,0.001170093,0.00111055,0.001503437,0.01231466,0.01478548,0.009228921],"genre_scores_gemma":[0.3256294,0.00110341,0.6340766,0.0001868074,0.0004977566,0.0008443902,0.03293709,0.0005929248,0.004131691],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006572106,"threshold_uncertainty_score":0.01321089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06962750853515684,"score_gpt":0.293273848033807,"score_spread":0.2236463394986502,"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."}}