{"id":"W4381512438","doi":"10.1177/03611981231179167","title":"Using Twitter to Gauge Customer Satisfaction Response to a Major Transit Service Change in Calgary, Canada","year":2023,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lexicon; Sentiment analysis; Customer satisfaction; Service quality; Schedule; Computer science; Service (business); Social media; Reliability (semiconductor); Public transport; Loyalty; Marketing; Business; Engineering; Transport engineering; Artificial intelligence; World Wide Web","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.000539368,0.0002276219,0.0002169876,0.0008809135,0.00158313,0.0013621,0.0004714127,0.000314494,0.002085587],"category_scores_gemma":[0.00231322,0.0001368821,0.0001515573,0.002498632,0.0004885475,0.0003986115,0.0005363996,0.0004376389,0.0004975959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01360116,"about_ca_system_score_gemma":0.01010189,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9817981,"about_ca_topic_score_gemma":0.9886253,"domain_scores_codex":[0.9994868,0.00005994584,0.00002290518,0.00007740541,0.0002215539,0.0001314853],"domain_scores_gemma":[0.9979061,0.0002313109,0.0001514474,0.00004027292,0.001486464,0.0001843826],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005011626,0.0002248525,0.8670607,0.0001851851,0.00007208463,0.0005821355,0.01472709,0.0019085,0.005792721,0.000506206,0.0207023,0.08773699],"study_design_scores_gemma":[0.00001542181,0.00008683309,0.953366,0.00003695027,0.00002924214,0.0000419526,0.02538021,0.0106539,0.001637128,0.00007328464,0.008641789,0.00003722803],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9897271,0.00006614301,0.0004473875,0.0004219584,0.00001665634,0.00009190898,0.003999812,0.000040921,0.005188165],"genre_scores_gemma":[0.9896246,0.0001617823,0.00109951,0.0001964647,0.00001067362,0.0000676062,0.002947923,0.00001975598,0.005871817],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01820189,"threshold_uncertainty_score":0.09868371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1871371268052244,"score_gpt":0.4163829707673877,"score_spread":0.2292458439621634,"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."}}