{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007199318,0.0001088964,0.0002942578,0.0006089648,0.00002699021,0.0002215988,0.0005656735,0.00003441403,0.00003631151],"category_scores_gemma":[0.00004314165,0.00007754573,0.00009447205,0.002122978,0.00002589332,0.0006476171,0.0004656601,0.00004675307,0.00001060169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004160698,"about_ca_system_score_gemma":0.00003707308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003390484,"about_ca_topic_score_gemma":0.0006997969,"domain_scores_codex":[0.9985425,0.00009564192,0.0003143511,0.0004474145,0.0004240837,0.0001760122],"domain_scores_gemma":[0.9985907,0.00005349993,0.0001621293,0.001002961,0.0001107212,0.00007997546],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007880351,0.001841528,0.7173023,0.00005272287,0.00584175,0.00009021277,0.01364574,0.004907779,0.03203369,0.02597057,0.02176241,0.1764725],"study_design_scores_gemma":[0.0005505939,0.00002724822,0.01622205,0.00001693115,0.0003323235,8.864916e-7,0.002456856,0.9618166,0.01626965,0.0002059462,0.001859076,0.0002418499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6343058,0.0001233872,0.3611363,0.003655971,0.0002440925,0.0001105843,0.00001090742,0.00004178122,0.0003710905],"genre_scores_gemma":[0.9721557,0.00001286429,0.025888,0.0000875968,0.00002740812,0.000001776699,0.0001385746,0.000003791008,0.001684306],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9569088,"threshold_uncertainty_score":0.3162223,"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."}}