{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006491843,0.0008088463,0.0008357402,0.005484037,0.0007777702,0.002397097,0.0009777239,0.0008068783,0.001906176],"category_scores_gemma":[0.009626948,0.0003403737,0.001384556,0.002658494,0.0004796672,0.002148946,0.001375567,0.0007743071,0.001019389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00127617,"about_ca_system_score_gemma":0.001226246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00280704,"about_ca_topic_score_gemma":0.003886338,"domain_scores_codex":[0.9948298,0.00215472,0.0003942579,0.0004168163,0.001978867,0.0002255393],"domain_scores_gemma":[0.9933758,0.003270488,0.0004367871,0.0002941611,0.002385052,0.0002377205],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001141385,0.0004170894,0.02689121,0.002291891,0.000768056,0.0001801603,0.002492904,0.007596267,0.007619666,0.005728784,0.01574096,0.9291317],"study_design_scores_gemma":[0.0005632327,0.002724502,0.2853138,0.002303003,0.001334019,0.001621855,0.01669559,0.4777155,0.02607214,0.03755054,0.1474731,0.0006326143],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5432182,0.02693555,0.2994159,0.00590961,0.002176847,0.002508223,0.002901418,0.0040996,0.1128346],"genre_scores_gemma":[0.7411186,0.01143773,0.2343694,0.000705339,0.0006850601,0.0007961158,0.00336995,0.0004197689,0.007098092],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006491843,"threshold_uncertainty_score":0.03433251,"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."}}