{"id":"W2274912527","doi":"10.1613/jair.4787","title":"How Translation Alters Sentiment","year":2016,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Research","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":194,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; National Research Council Canada","funders":"","keywords":"Sentiment analysis; Computer science; Focus (optics); Lexicon; Natural language processing; Annotation; Artificial intelligence; Arabic; Machine translation; Linguistics; Social media; Resource (disambiguation); World Wide Web","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":[0.002394137,0.0006790144,0.0003552284,0.0009247794,0.001173072,0.004771045,0.0004263674,0.00070978,0.01702052],"category_scores_gemma":[0.01474901,0.0004287495,0.0005320505,0.001254502,0.001463052,0.003518916,0.001529894,0.001296376,0.01270334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001279678,"about_ca_system_score_gemma":0.00090314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002319514,"about_ca_topic_score_gemma":0.002002365,"domain_scores_codex":[0.9977748,0.00106805,0.0001317173,0.0003306279,0.0005039575,0.0001908782],"domain_scores_gemma":[0.9953225,0.001418985,0.0003359242,0.0007901826,0.001999461,0.0001328394],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008309794,0.0002845692,0.02354581,0.001387391,0.0002117722,0.001993182,0.02977808,0.003664358,0.1133438,0.0991704,0.1018908,0.6238989],"study_design_scores_gemma":[0.0001987266,0.0004035354,0.05012467,0.0006311639,0.0003306621,0.002071302,0.02497181,0.03484488,0.08888031,0.1564288,0.6408977,0.0002164685],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4577627,0.002374702,0.1150093,0.02060382,0.005271306,0.0004782967,0.003121463,0.004523551,0.390855],"genre_scores_gemma":[0.9166805,0.00178429,0.03191848,0.003071344,0.0006489494,0.0002130937,0.002206742,0.002472757,0.0410038],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01702052,"threshold_uncertainty_score":0.0569393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2633291444881119,"score_gpt":0.4243514885302698,"score_spread":0.1610223440421579,"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."}}