{"id":"W1667895860","doi":"10.5220/0004745301780186","title":"Context-Specific Sentiment Lexicon Expansion via Minimal User Interaction","year":2014,"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":"Dalhousie University","funders":"","keywords":"Lexicon; Computer science; Sentiment analysis; Context (archaeology); Natural language processing; Polarity (international relations); Process (computing); Domain (mathematical analysis); Task (project management); Artificial intelligence; Visualization; Quality (philosophy); User interface; Human–computer interaction; Information retrieval","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":[0.001909879,0.001777382,0.0007537435,0.001112916,0.0004068685,0.00133428,0.00110848,0.0006735271,0.02106249],"category_scores_gemma":[0.01150753,0.0004770661,0.0006862201,0.0006031794,0.0003166255,0.002153531,0.002377129,0.0006696692,0.007522751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001857712,"about_ca_system_score_gemma":0.0003500756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003075697,"about_ca_topic_score_gemma":0.0008717261,"domain_scores_codex":[0.9985904,0.0007159777,0.0001057331,0.0002718633,0.0002544977,0.00006150082],"domain_scores_gemma":[0.9945268,0.00387896,0.0001665549,0.0006561879,0.0006466554,0.0001247992],"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":[0.001709613,0.0007372566,0.002595134,0.001519103,0.00009339259,0.0009110539,0.005015825,0.002712498,0.2209696,0.004510698,0.03843022,0.7207956],"study_design_scores_gemma":[0.001010236,0.001470302,0.01334071,0.0006266118,0.0002981308,0.003055018,0.003629227,0.4727586,0.2093527,0.03285368,0.2611924,0.0004124313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07643075,0.0002825159,0.8353751,0.0003869264,0.0001244105,0.001371598,0.001334612,0.07138505,0.01330895],"genre_scores_gemma":[0.2838672,0.0002010255,0.6999642,0.0003517852,0.000058873,0.002478739,0.002683435,0.003608559,0.006786194],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02106249,"threshold_uncertainty_score":0.07046103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02349723462266059,"score_gpt":0.261036744068626,"score_spread":0.2375395094459654,"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."}}