{"id":"W2766325541","doi":"10.1016/j.ijinfomgt.2017.09.007","title":"Using big data analytics to study brand authenticity sentiments: The case of Starbucks on Twitter","year":2017,"lang":"en","type":"article","venue":"International Journal of Information Management","topic":"Digital Marketing and Social Media","field":"Social Sciences","cited_by":117,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Sentiment analysis; Big data; Social media; Computer science; Polarity (international relations); Data science; Robustness (evolution); Support vector machine; Advertising; Information retrieval; Natural language processing; Artificial intelligence; Data mining; World Wide Web; Business","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001448524,0.0002993562,0.0002019074,0.001438561,0.00328896,0.003127431,0.0006821267,0.001374668,0.002238619],"category_scores_gemma":[0.005093218,0.0002225939,0.0001820586,0.001637596,0.001362001,0.004293179,0.001598269,0.001182635,0.0004229606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001311373,"about_ca_system_score_gemma":0.0008305791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02194816,"about_ca_topic_score_gemma":0.05298153,"domain_scores_codex":[0.9994457,0.0002531265,0.00001460276,0.00005714352,0.0001253522,0.0001040206],"domain_scores_gemma":[0.995719,0.00267444,0.0004029121,0.0002357612,0.0004434314,0.0005244476],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001238131,0.00144787,0.5916772,0.0003212983,0.000210283,0.01758237,0.1908966,0.008302563,0.01779695,0.03050535,0.02053152,0.1194898],"study_design_scores_gemma":[0.00006134849,0.0005349234,0.2356098,0.0001817009,0.0001260788,0.001695414,0.5959135,0.08928445,0.008033025,0.01484052,0.05353876,0.0001806247],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9922644,0.00003697093,0.0006102407,0.001124848,0.00001635738,0.00001916571,0.00007463805,0.00002213701,0.005831302],"genre_scores_gemma":[0.9969264,0.00005078019,0.001123079,0.00008460173,0.00000941022,0.000009439782,0.00008115255,0.00001963135,0.001695541],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02194816,"threshold_uncertainty_score":0.04364079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1558660852414062,"score_gpt":0.4150692331788663,"score_spread":0.2592031479374601,"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."}}