{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001653972,0.0001996002,0.0002163109,0.0011046,0.00053483,0.001919706,0.0002150439,0.0004249199,0.001284457],"category_scores_gemma":[0.01019391,0.0001077126,0.0002312051,0.002002677,0.0003247942,0.001890312,0.0003566507,0.0004262647,0.0006273435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000568243,"about_ca_system_score_gemma":0.0004577658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006368462,"about_ca_topic_score_gemma":0.01015296,"domain_scores_codex":[0.9986976,0.000551455,0.0001167792,0.0001679351,0.0003161366,0.0001500483],"domain_scores_gemma":[0.9937015,0.002984828,0.001182578,0.000164281,0.001748674,0.0002181672],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005822823,0.0001404359,0.7881746,0.0002607369,0.000189332,0.0004332913,0.009123164,0.001990634,0.005612059,0.002551588,0.01599948,0.1749424],"study_design_scores_gemma":[0.00003292625,0.0003249154,0.8415113,0.0002137697,0.000212862,0.0004255424,0.05585602,0.05862681,0.006530246,0.007128727,0.02904541,0.00009143843],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9837285,0.0002917344,0.003415673,0.002896677,0.00006558558,0.00004443738,0.001944501,0.00003533477,0.007577619],"genre_scores_gemma":[0.996482,0.0002187228,0.001702594,0.0001838356,0.00006280921,0.00002288154,0.0004969781,0.00001032001,0.0008198904],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006368462,"threshold_uncertainty_score":0.01266277,"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."}}