{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074807,0.0004342216,0.0003205787,0.001991086,0.0006136731,0.001075187,0.0002600986,0.0003174722,0.001689815],"category_scores_gemma":[0.001738903,0.0001102223,0.0004191536,0.0010418,0.0001866686,0.0007444134,0.0004533428,0.0004531771,0.00104855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003210669,"about_ca_system_score_gemma":0.0003342062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00176146,"about_ca_topic_score_gemma":0.004033987,"domain_scores_codex":[0.9996519,0.00006674972,0.00003846712,0.00006560655,0.0001276822,0.00004957889],"domain_scores_gemma":[0.9992523,0.0002579939,0.0001105993,0.0000356722,0.0002920108,0.00005151888],"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":[0.001129517,0.0007018671,0.1510812,0.0005302313,0.000279879,0.001251473,0.004882857,0.003399335,0.1349712,0.003045372,0.02644071,0.6722864],"study_design_scores_gemma":[0.00009559988,0.001402164,0.362141,0.0002123784,0.0003237759,0.001666064,0.01445442,0.4622675,0.08417844,0.008190479,0.06491379,0.0001543869],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9114672,0.0005019252,0.06692123,0.001238421,0.0003909284,0.000651311,0.005019451,0.001734227,0.01207528],"genre_scores_gemma":[0.9145902,0.0003267857,0.07175835,0.0001966565,0.0002202242,0.0001628245,0.005880499,0.00009916984,0.00676525],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001991086,"threshold_uncertainty_score":0.005653024,"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."}}