{"id":"W2250850523","doi":"10.18653/v1/w15-2916","title":"How much does word sense disambiguation help in sentiment analysis of micropost data?","year":2015,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Word-sense disambiguation; Sentiment analysis; Computer science; Word (group theory); SemEval; Natural language processing; Social media; Short Message Service; Microblogging; Artificial intelligence; Information retrieval; World Wide Web; Linguistics","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":[0.009419648,0.001573227,0.001747597,0.005239266,0.001722699,0.005713598,0.00100372,0.001364419,0.003137727],"category_scores_gemma":[0.02834753,0.0006769174,0.001573122,0.005058431,0.00126959,0.009867777,0.001656055,0.00203666,0.007552393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007439255,"about_ca_system_score_gemma":0.001203143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002722546,"about_ca_topic_score_gemma":0.004766232,"domain_scores_codex":[0.9948187,0.002064016,0.0005168732,0.001101631,0.001202713,0.0002960246],"domain_scores_gemma":[0.9837236,0.008544595,0.001515388,0.001899058,0.003960229,0.0003571327],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009840211,0.0003027972,0.03688568,0.001942355,0.0006204264,0.0003047599,0.003133068,0.002056743,0.05451318,0.004924762,0.03426955,0.8600627],"study_design_scores_gemma":[0.0003823627,0.001176161,0.111387,0.00258339,0.001600821,0.0022651,0.02607296,0.1807155,0.2121715,0.1554565,0.3051501,0.001038846],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3111358,0.01679852,0.5749309,0.03824433,0.00476032,0.0007768264,0.007291035,0.01200185,0.0340604],"genre_scores_gemma":[0.5077355,0.005631144,0.4682884,0.003902927,0.002181205,0.0003196747,0.005163684,0.001524979,0.005252426],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009419648,"threshold_uncertainty_score":0.04981649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06536210261191946,"score_gpt":0.3047737175620968,"score_spread":0.2394116149501773,"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."}}