{"id":"W2604547414","doi":"10.1108/pmm-07-2016-0031","title":"Constructing a sentiment analysis model for LibQUAL+ comments","year":2017,"lang":"en","type":"article","venue":"Performance Measurement and Metrics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Sentiment analysis; Originality; Terminology; Service (business); Set (abstract data type); Process (computing); Information retrieval; Data science; Operations research; World Wide Web; Artificial intelligence; Linguistics; Qualitative research; Sociology; Marketing; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001685249,0.0001608736,0.0003075025,0.0005443697,0.0009439888,0.0006817491,0.0006450562,0.00004161294,0.000004023487],"category_scores_gemma":[0.0001300092,0.0001446086,0.0001667332,0.0005278462,0.00004359762,0.0006762873,0.0002619541,0.00007095558,0.000003064288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008655807,"about_ca_system_score_gemma":0.00003671815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007420938,"about_ca_topic_score_gemma":0.000008002889,"domain_scores_codex":[0.9982904,0.00001815757,0.0003198704,0.0003817822,0.0006774857,0.0003123297],"domain_scores_gemma":[0.9985622,0.00003571472,0.000361738,0.0006506227,0.0002789259,0.0001108116],"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":[0.00001452765,0.000101593,0.7109649,0.00005817558,0.0009683214,5.53127e-7,0.0005015543,0.001262868,0.0001469789,0.005539189,0.0004639512,0.2799774],"study_design_scores_gemma":[0.0006351784,0.00003679935,0.009352732,0.00001834683,0.0003440421,5.114247e-7,0.00004524589,0.9871823,0.001494573,0.00007688352,0.0006246608,0.0001886852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.123317,0.0003942118,0.874027,0.0004804223,0.000278956,0.0002264522,0.000003438255,0.00004307912,0.001229475],"genre_scores_gemma":[0.9487962,0.0001451984,0.05063444,0.0001029635,0.00004412646,0.00002519475,0.000004830884,0.000006201763,0.000240869],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9859195,"threshold_uncertainty_score":0.7260494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1484309806034734,"score_gpt":0.3142024847413011,"score_spread":0.1657715041378278,"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."}}