{"id":"W2471707884","doi":"10.1016/j.dss.2016.06.010","title":"Modeling customer satisfaction from unstructured data using a Bayesian approach","year":2016,"lang":"en","type":"article","venue":"Decision Support Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":90,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Customer satisfaction; Computer science; Bayesian probability; Set (abstract data type); Sentiment analysis; Probabilistic logic; Unstructured data; The Internet; Customer intelligence; Data mining; Service (business); Product (mathematics); Bayesian network; Variety (cybernetics); Data set; Data science; Artificial intelligence; Service quality; Customer retention; World Wide Web; Big data; Marketing; Mathematics","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":[0.003307156,0.0007852183,0.001429702,0.001306739,0.0004145688,0.00161533,0.001404517,0.001413939,0.001436427],"category_scores_gemma":[0.01309734,0.001106301,0.001213044,0.001474604,0.000576412,0.002660292,0.0007920653,0.001838216,0.0004444863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009780453,"about_ca_system_score_gemma":0.0009780467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01284152,"about_ca_topic_score_gemma":0.0143581,"domain_scores_codex":[0.998566,0.0006805644,0.00008276483,0.0002178237,0.0003109845,0.000142025],"domain_scores_gemma":[0.9912096,0.007487981,0.0004616217,0.0001722874,0.0005601388,0.0001084039],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001671623,0.0002049643,0.006000773,0.00009929942,0.0002054598,0.000131475,0.0001405288,0.9299975,0.0008873335,0.01517895,0.001322296,0.04566432],"study_design_scores_gemma":[0.000005405737,0.0000101292,0.0003305287,0.00000381289,0.000008980999,0.00000575,0.000006549894,0.9944956,0.00005662644,0.004988891,0.00008312038,0.00000473002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09282687,0.0004436673,0.9038727,0.0006835309,0.00004659175,0.0000806631,0.0005584874,0.00024623,0.001241258],"genre_scores_gemma":[0.8833651,0.0007835208,0.1113695,0.0002169382,0.0001935026,0.0002914258,0.001317409,0.00005617451,0.00240648],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01284152,"threshold_uncertainty_score":0.02553356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08640592321187143,"score_gpt":0.3129794778408794,"score_spread":0.226573554629008,"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."}}