{"id":"W2744881172","doi":"10.1016/j.ipm.2017.07.004","title":"Modeling Arabic subjectivity and sentiment in lexical space","year":2017,"lang":"en","type":"article","venue":"Information Processing & Management","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Subjectivity; Arabic; Natural language processing; Computer science; Space (punctuation); Artificial intelligence; Linguistics; Philosophy; Epistemology","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":[0.0004633609,0.0005236097,0.0003577175,0.00120324,0.0004805092,0.001630316,0.000373784,0.0004301672,0.002365414],"category_scores_gemma":[0.001908932,0.0001861855,0.0007257042,0.001084551,0.0002807458,0.001731693,0.0006836349,0.0006638523,0.0007701915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000658234,"about_ca_system_score_gemma":0.0005056081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01246541,"about_ca_topic_score_gemma":0.01225716,"domain_scores_codex":[0.999727,0.000106848,0.00001582292,0.0000687032,0.00004573426,0.00003587738],"domain_scores_gemma":[0.99937,0.0003800491,0.0000535068,0.0000312576,0.000126356,0.00003874056],"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":[0.001903917,0.0007363902,0.07602341,0.0003225793,0.0004205552,0.001035172,0.001540256,0.4646835,0.02952578,0.04782913,0.01010319,0.3658761],"study_design_scores_gemma":[0.000006683445,0.00004050521,0.002971389,0.000007396792,0.00002226057,0.00003258458,0.0001502926,0.9885217,0.0007744962,0.006731148,0.0007339576,0.000007605632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7350367,0.0007799194,0.2547223,0.0006343918,0.0001223943,0.00005722495,0.001592964,0.0009193519,0.006134696],"genre_scores_gemma":[0.9773592,0.0001619079,0.01930987,0.00002339335,0.0000335784,0.00003161596,0.0008496793,0.00003710567,0.002193692],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01246541,"threshold_uncertainty_score":0.0247857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01928436550720981,"score_gpt":0.2747371884251029,"score_spread":0.2554528229178931,"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."}}