{"id":"W2990522170","doi":"10.1109/tcss.2019.2951326","title":"Improving Sentiment Polarity Detection Through Target Identification","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Computational Social Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick; Université du Québec à Montréal","funders":"","keywords":"Computer science; Lexicon; Polarity (international relations); Sentiment analysis; Identification (biology); Sentence; Artificial intelligence; Natural language processing; Data mining; Machine learning","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":[0.0003654663,0.0001647128,0.0002163057,0.0001564748,0.0006171926,0.0004067978,0.0002946136,0.0001010158,0.00004533264],"category_scores_gemma":[0.000001919716,0.0001788518,0.0002014774,0.0005458979,0.00002212274,0.000732869,0.000004187611,0.000178632,0.0004021997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002190786,"about_ca_system_score_gemma":0.00005533299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002878343,"about_ca_topic_score_gemma":0.000005979595,"domain_scores_codex":[0.9980173,0.0001615672,0.0004670119,0.0004704413,0.0006591739,0.0002244798],"domain_scores_gemma":[0.999189,0.00009685328,0.0002731908,0.0002081898,0.0001835712,0.00004922431],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003092903,0.000485653,0.0002484338,0.00009460548,0.0003712501,0.000002535473,0.002975104,0.9275101,0.01412706,0.01760196,0.0001576125,0.03639472],"study_design_scores_gemma":[0.0004375456,0.00005197068,0.0009713973,0.00001596039,0.00002333477,0.000005202876,0.0002301772,0.9920166,0.004637208,0.0008699231,0.0004960865,0.0002446458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02346842,0.00003321758,0.9725072,0.0002204338,0.002988391,0.0003462426,0.00001096483,0.0001789845,0.0002461912],"genre_scores_gemma":[0.9944186,0.000001408213,0.004800576,0.00007788559,0.0001589116,0.00003593668,0.0000148948,0.00001298115,0.0004787792],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9709502,"threshold_uncertainty_score":0.7293364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0172080794756313,"score_gpt":0.2600495495016216,"score_spread":0.2428414700259903,"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."}}