{"id":"W4383617168","doi":"10.1007/978-3-031-35915-6_42","title":"Customer Review Classification Using Machine Learning and Deep Learning Techniques","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Customer intelligence; Customer base; Computer science; Customer retention; Voice of the customer; Customer to customer; Customer advocacy; Context (archaeology); Competitive advantage; Marketing; Product (mathematics); Service (business); Confusion; Business; Service quality","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001624231,0.000397549,0.0005636281,0.0009397187,0.0004412312,0.0004646959,0.001099486,0.0001952633,0.00002410952],"category_scores_gemma":[0.0001546507,0.0003646217,0.0001245281,0.001005639,0.0002783575,0.0004638188,0.001024808,0.0009738085,0.00004125691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001475526,"about_ca_system_score_gemma":0.00009615954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002048303,"about_ca_topic_score_gemma":0.0000171794,"domain_scores_codex":[0.9969158,0.00008544941,0.0005488891,0.001249921,0.000766782,0.0004331128],"domain_scores_gemma":[0.9984075,0.0003221609,0.0004677302,0.000519063,0.0001695363,0.0001140443],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001165201,0.000007756631,0.0008317562,0.0001974405,0.00001973147,0.0000248805,0.0002634295,0.001781411,0.0007315819,0.004473865,0.000004140291,0.9916629],"study_design_scores_gemma":[0.00007360638,0.00005954674,0.00008441966,0.002640877,0.00003243672,0.00003809094,4.313249e-7,0.9837505,0.0005590089,0.003191689,0.009034812,0.0005345781],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0000172096,0.008777485,0.9887773,0.0004459929,0.0003585603,0.0002502314,4.199386e-7,0.0003252548,0.001047507],"genre_scores_gemma":[0.012677,0.03308913,0.9494004,0.001578674,0.0006180372,0.00001999204,0.00003842442,0.0001199889,0.0024583],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9911283,"threshold_uncertainty_score":0.9998806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04088826005156904,"score_gpt":0.3050112540376721,"score_spread":0.2641229939861031,"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."}}