{"id":"W640143065","doi":"","title":"How Are We Doing? Opinion Mining Customer Sentiment in US Transit Agencies and Airlines via Twitter","year":2012,"lang":"en","type":"article","venue":"Transportation Research Board 91st Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sentiment analysis; Lexicon; Cluster analysis; Social media; Simple (philosophy); Advertising; Transit (satellite); Computer science; Public opinion; Business; Data science; Political science; Artificial intelligence; World Wide Web; Public transport; Law","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.008587228,0.0004873218,0.0006194276,0.002052565,0.001840274,0.0004617108,0.0004989785,0.0005312286,0.0001940139],"category_scores_gemma":[0.0002360061,0.0005211299,0.0001925753,0.003140685,0.001368986,0.002515522,0.00001030482,0.001415328,0.0000464126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003594847,"about_ca_system_score_gemma":0.0004722944,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01292006,"about_ca_topic_score_gemma":0.0456067,"domain_scores_codex":[0.9891557,0.001874081,0.001131388,0.001028959,0.004370792,0.002439064],"domain_scores_gemma":[0.9950639,0.001105423,0.000301314,0.0003827105,0.002184373,0.00096227],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005525077,0.0003820279,0.7806096,0.0005422394,0.00008160657,0.00007804536,0.2025503,0.00371175,0.0006851336,0.002055545,0.004821918,0.003929289],"study_design_scores_gemma":[0.001703914,0.0001662343,0.7104014,0.0006793131,0.00004289825,6.582772e-7,0.1538765,0.000464829,0.0004991112,0.0001466719,0.1313048,0.0007136408],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9736496,0.002058665,0.005403914,0.01351104,0.0006421416,0.002179265,0.0002099309,0.0004220525,0.001923426],"genre_scores_gemma":[0.9875962,0.003700873,0.005221669,0.0001268242,0.0005563304,0.0004050262,0.0004463099,0.00009857823,0.00184819],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1264829,"threshold_uncertainty_score":0.999724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09195928560226266,"score_gpt":0.3939173482296893,"score_spread":0.3019580626274266,"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."}}