{"id":"W3115233994","doi":"10.1109/iemcon51383.2020.9284875","title":"Quarantine Quibbles: A Sentiment Analysis of COVID-19 Tweets","year":2020,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Sentiment analysis; Coronavirus disease 2019 (COVID-19); Social media; Microblogging; Pessimism; Pandemic; Computer science; Order (exchange); 2019-20 coronavirus outbreak; Quarantine; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Data science; Internet privacy; World Wide Web; Artificial intelligence; Business; Outbreak; Medicine; Virology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002739065,0.0001144892,0.0003948803,0.0003329345,0.00005974747,0.0000755363,0.0006001171,0.00002939559,0.0009213656],"category_scores_gemma":[0.00005353173,0.00009611458,0.000351968,0.003096825,0.00002606624,0.0001672812,0.000210283,0.0000435088,0.00005472443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001653316,"about_ca_system_score_gemma":0.00006409543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001124957,"about_ca_topic_score_gemma":0.00001347954,"domain_scores_codex":[0.9985397,0.00006320904,0.0004134495,0.0004063662,0.0004010005,0.0001763115],"domain_scores_gemma":[0.9990281,0.00008826726,0.0001782495,0.0003869405,0.00005657886,0.0002618594],"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":[0.0001082231,0.001107958,0.4899668,0.0003012559,0.02330416,0.0001120086,0.04562547,0.09319866,0.02423746,0.2362466,0.06868906,0.01710229],"study_design_scores_gemma":[0.000512336,0.00007881132,0.003147991,0.00000419434,0.000625681,4.972857e-7,0.000437284,0.9763933,0.00395678,0.00006455962,0.01456702,0.000211506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03375369,0.0001386868,0.9532108,0.01103431,0.00005319639,0.00007525196,0.000002700755,0.0001203481,0.00161102],"genre_scores_gemma":[0.972553,0.00001682915,0.02255006,0.004510677,0.00003259621,0.000002990278,0.00001497029,0.000004383936,0.0003144973],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9387993,"threshold_uncertainty_score":0.999992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05395947702658516,"score_gpt":0.3132858193641779,"score_spread":0.2593263423375928,"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."}}