{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002335374,0.00005828304,0.00009703837,0.000182532,0.0003600421,0.000583669,0.001194541,0.00001694436,0.00002570763],"category_scores_gemma":[0.000727669,0.00004295011,0.00004534997,0.00006462501,0.00007646785,0.001001978,0.0003399169,0.00007705349,0.000008231304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000969905,"about_ca_system_score_gemma":0.00004116827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000476933,"about_ca_topic_score_gemma":0.0001401442,"domain_scores_codex":[0.9985402,0.00008821405,0.000453054,0.00005237976,0.0007723679,0.00009374518],"domain_scores_gemma":[0.9983665,0.00008392212,0.0007675006,0.0002915674,0.0004326826,0.00005786852],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0006448841,0.001061059,0.0267703,0.00004048248,0.002692983,0.0007546147,0.1114225,0.00285594,0.000004989033,0.01166555,0.008803714,0.833283],"study_design_scores_gemma":[0.009781637,0.0009608637,0.2016706,0.001130495,0.001177769,0.0001396829,0.5601195,0.01080223,0.0001284066,0.01066363,0.2024748,0.0009503579],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9582514,0.000002032323,0.006180461,0.002364028,0.002175692,0.0002819536,0.00002504971,0.000004407364,0.030715],"genre_scores_gemma":[0.9987088,0.000009438578,0.0004962286,0.000340599,0.0002728805,8.496024e-7,0.000002518542,0.000002357955,0.0001663626],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8323326,"threshold_uncertainty_score":0.5628334,"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."}}