{"id":"W4396912905","doi":"10.48550/arxiv.2405.06692","title":"Analyzing Language Bias Between French and English in Conventional Multilingual Sentiment Analysis Models","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sentiment analysis; Linguistics; Computer science; Natural language processing; Artificial intelligence; Philosophy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008993691,0.0006031339,0.0004832899,0.001171834,0.0006995944,0.001895212,0.0003441376,0.0004437228,0.001327401],"category_scores_gemma":[0.02016034,0.000151203,0.0005638096,0.001026906,0.0006644457,0.001483326,0.001023917,0.0007477775,0.0004797599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001458034,"about_ca_system_score_gemma":0.0007772252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01621,"about_ca_topic_score_gemma":0.01837198,"domain_scores_codex":[0.9969435,0.001868498,0.0001320955,0.0004220412,0.0004484485,0.000185422],"domain_scores_gemma":[0.991084,0.006015761,0.0007803838,0.0006619717,0.001312589,0.0001453156],"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":[0.002011139,0.0003371965,0.5385154,0.0005006527,0.001002839,0.000691437,0.004867614,0.04884221,0.01475604,0.02611951,0.01678353,0.3455725],"study_design_scores_gemma":[0.0001367948,0.0006225327,0.2624448,0.0003798678,0.0004423107,0.000814862,0.003525973,0.6275599,0.01577235,0.05305414,0.03506909,0.0001773817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9049248,0.001157165,0.08213557,0.001612864,0.00012238,0.0000896315,0.001711544,0.0003609894,0.007885084],"genre_scores_gemma":[0.985882,0.0001863249,0.01104012,0.0002101115,0.00005464693,0.00005096973,0.001617074,0.00003933478,0.0009192821],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01621,"threshold_uncertainty_score":0.04756373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09082824059050736,"score_gpt":0.233674772441511,"score_spread":0.1428465318510037,"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."}}