{"id":"W3089901159","doi":"10.1145/3377812.3390790","title":"Semantic analysis of issues on Google play and Twitter","year":2020,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; Kelowna General Hospital; University of British Columbia","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Automatic summarization; Computer science; World Wide Web; App store; Semantics (computer science); Mobile apps; Information retrieval; Social media; Resource (disambiguation); Sentiment analysis; Semantic analysis (machine learning); Data science; Natural language processing","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00004446569,0.00004044325,0.0001192025,0.00006069364,0.00001207774,0.00002569961,0.0001780331,0.00001403122,0.00005848077],"category_scores_gemma":[0.00001003692,0.00003146374,0.00003042618,0.00029661,0.000008201422,0.00006587727,0.00009044779,0.00002707223,0.00001131449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000213587,"about_ca_system_score_gemma":0.000003847123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004579492,"about_ca_topic_score_gemma":0.000005020341,"domain_scores_codex":[0.999554,0.00001201752,0.00009382723,0.0001757873,0.000102459,0.00006195857],"domain_scores_gemma":[0.99972,0.000020823,0.00002137239,0.0001877792,0.00001232789,0.00003772021],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004471302,0.0003266753,0.2030486,0.0002839829,0.003389698,0.0001019252,0.08809315,0.07784383,0.03234586,0.4237264,0.02603827,0.144757],"study_design_scores_gemma":[0.00006153155,0.00003779293,0.00645123,0.000003184473,0.0000378429,2.11378e-7,0.00002852985,0.9903075,0.002234488,0.0001958868,0.000588925,0.0000528593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4058413,0.00003969703,0.5829822,0.008579241,0.00001996983,0.00002665473,2.57269e-7,0.00004055406,0.002470197],"genre_scores_gemma":[0.9782626,0.000003652183,0.01975187,0.001672952,0.00001583915,4.48364e-7,2.413955e-7,0.000001336318,0.000291041],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9124637,"threshold_uncertainty_score":0.1283054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04085643520667663,"score_gpt":0.2763065765115711,"score_spread":0.2354501413048944,"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."}}