{"id":"W2765183831","doi":"10.1177/0047287517729757","title":"Automated Sentiment Analysis in Tourism: Comparison of Approaches","year":2017,"lang":"en","type":"article","venue":"Journal of Travel Research","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":196,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Saint Vincent University","funders":"","keywords":"Sentiment analysis; Tourism; Computer science; Artificial intelligence; Machine learning; Hospitality; Data science; Selection (genetic algorithm); Natural language processing; Software; Data mining","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":[0.004693983,0.00008439202,0.0005458839,0.001600003,0.0001986612,0.0003357291,0.001817641,0.00005736482,0.00002834499],"category_scores_gemma":[0.0001846995,0.0000671744,0.000289918,0.001002126,0.0001058583,0.0004229833,0.0003022163,0.0003809984,0.000005322546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000648336,"about_ca_system_score_gemma":0.0001193158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009836168,"about_ca_topic_score_gemma":0.00003761427,"domain_scores_codex":[0.9971137,0.0002757799,0.0007769986,0.0001903051,0.001349489,0.0002937429],"domain_scores_gemma":[0.997974,0.0001810792,0.000724145,0.0006371092,0.0003705142,0.0001131342],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001058227,0.00181701,0.9171883,0.0000661391,0.002821042,0.0001478976,0.007013031,0.01035654,0.006236589,0.005130128,0.008696904,0.04042054],"study_design_scores_gemma":[0.0004486228,0.0001206476,0.3979469,0.00004638221,0.00005793813,0.000002915515,0.0006644938,0.5904039,0.01000441,0.0001715802,0.00006635637,0.00006587942],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9497022,0.000609491,0.04361854,0.002128164,0.0001868421,0.000126793,0.000001155286,0.00001242094,0.003614389],"genre_scores_gemma":[0.9899104,0.000021363,0.009826637,0.000002901657,0.00004829403,9.755164e-7,5.785928e-7,0.000003651584,0.0001851578],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5800474,"threshold_uncertainty_score":0.337766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2825954453852928,"score_gpt":0.4573567636676105,"score_spread":0.1747613182823177,"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."}}