{"id":"W4207039276","doi":"10.1109/asew52652.2021.00053","title":"Learning Sentiment Analysis for Accessibility User Reviews","year":2021,"lang":"en","type":"article","venue":"","topic":"Digital Accessibility for Disabilities","field":"Social Sciences","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Computer science; Sentiment analysis; Naive Bayes classifier; Popularity; Support vector machine; tf–idf; Artificial intelligence; Machine learning; Bag-of-words model; Boosting (machine learning); Information retrieval; Term (time)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001962721,0.0001100729,0.0003628149,0.00005586748,0.0004321665,0.0004888634,0.0002640815,0.00006279363,0.003606774],"category_scores_gemma":[0.002867704,0.00009552351,0.0005647044,0.001107911,0.000214744,0.0007833284,0.0001182,0.00007431179,0.00006452521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00016195,"about_ca_system_score_gemma":0.0001653054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005000389,"about_ca_topic_score_gemma":0.008313983,"domain_scores_codex":[0.9981148,0.0003327365,0.0004238193,0.00047604,0.0003158601,0.0003368019],"domain_scores_gemma":[0.9987866,0.0003509085,0.0001074461,0.0003554476,0.0002712839,0.0001283109],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001044163,0.0002878077,0.9421038,0.0001012177,0.0002554037,7.235948e-7,0.00453999,0.00004282491,0.00003469814,0.02979284,0.001628312,0.02120192],"study_design_scores_gemma":[0.0002814078,0.00004017191,0.06422275,0.00002225875,0.0005385792,1.307847e-7,0.02735445,0.0001616037,0.0012122,0.02467176,0.8810703,0.0004244305],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7024682,0.0005862421,0.004157776,0.001730546,0.0002215334,0.0008156681,0.00001473275,0.0002091574,0.2897962],"genre_scores_gemma":[0.8955565,0.00007121742,0.001934679,0.0002101775,0.00008721432,0.00008994432,0.0000327827,0.000007013461,0.1020105],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.879442,"threshold_uncertainty_score":0.9973041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06717810131145494,"score_gpt":0.3932527963324494,"score_spread":0.3260746950209944,"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."}}