{"id":"W1961993270","doi":"10.1002/asi.23533","title":"A machine‐learning approach to negation and speculation detection for sentiment analysis","year":2015,"lang":"en","type":"article","venue":"Journal of the Association for Information Science and Technology","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":96,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Negation; Speculation; Scope (computer science); Computer science; Artificial intelligence; Baseline (sea); Task (project management); Sentiment analysis; Natural language processing; Machine learning; Identification (biology); Programming language","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001732745,0.0008344424,0.0007323397,0.002226845,0.0005612858,0.001133863,0.001007333,0.0009210619,0.003296623],"category_scores_gemma":[0.004755773,0.0002847668,0.0008227614,0.0009985527,0.0003068735,0.00143027,0.0006195407,0.001106992,0.002332029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005266091,"about_ca_system_score_gemma":0.0006807263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001237456,"about_ca_topic_score_gemma":0.002030125,"domain_scores_codex":[0.9989366,0.0002848578,0.0001210233,0.0002826375,0.0003091491,0.00006580188],"domain_scores_gemma":[0.9978049,0.0008975106,0.0002553815,0.0001781016,0.0007952056,0.00006891425],"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":[0.0002902416,0.0003787097,0.005281884,0.0003989008,0.0001503915,0.0002499821,0.0003508078,0.005941258,0.07930043,0.003036698,0.01345185,0.891169],"study_design_scores_gemma":[0.00005750694,0.00041496,0.01005672,0.0001315149,0.0001376622,0.0005259072,0.0002348225,0.8964946,0.0587391,0.01102068,0.0221016,0.00008488251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05244625,0.001371744,0.9278527,0.0009379539,0.0004080212,0.0006116225,0.001320263,0.00822704,0.006824408],"genre_scores_gemma":[0.3642092,0.0005649604,0.6266094,0.0003195715,0.0003786957,0.0004273911,0.002279465,0.000137077,0.005074163],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003296623,"threshold_uncertainty_score":0.01102829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01581222229712936,"score_gpt":0.2586530080846184,"score_spread":0.242840785787489,"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."}}