{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006476899,0.0005868538,0.00031239,0.0085646,0.0009180973,0.001285735,0.0003045137,0.0004768899,0.001108174],"category_scores_gemma":[0.00512835,0.0001357554,0.0005892026,0.004892976,0.0003492371,0.00217309,0.0007607591,0.0004021718,0.0006725172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009378648,"about_ca_system_score_gemma":0.0005930648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008757392,"about_ca_topic_score_gemma":0.01397501,"domain_scores_codex":[0.9988715,0.000268813,0.0001188751,0.0001447457,0.0004904988,0.0001056291],"domain_scores_gemma":[0.9964173,0.001748374,0.0006416637,0.0001210287,0.0009393018,0.0001322909],"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":[0.002142712,0.0004546472,0.3173563,0.00412769,0.0004395799,0.005449007,0.03596886,0.008701097,0.09567343,0.02056984,0.07185069,0.4372661],"study_design_scores_gemma":[0.00004805459,0.0002960728,0.6609682,0.0003693893,0.0002913137,0.001813501,0.03123116,0.1405591,0.02272906,0.009406317,0.1320906,0.0001971108],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9272148,0.001840473,0.02629236,0.001594244,0.0003153779,0.0005896261,0.02237938,0.001185038,0.01858866],"genre_scores_gemma":[0.9618568,0.0006937061,0.01555796,0.0001197352,0.000297818,0.0004165129,0.01710032,0.0001233988,0.003833834],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008757392,"threshold_uncertainty_score":0.01741284,"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."}}