{"id":"W4411114428","doi":"10.21015/vtse.v13i2.2103","title":"Covid-19 Sentiment Analysis on X (formerly Twitter) Using Machine Learning Classifiers: Performance Comparison and Key Insights","year":2025,"lang":"en","type":"article","venue":"VFAST Transactions on Software Engineering","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Key (lock); Coronavirus disease 2019 (COVID-19); Sentiment analysis; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Computer science; Artificial intelligence; Natural language processing; Data science; Biology; 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":[0.002488276,0.0008735204,0.0009533783,0.002140494,0.0004669591,0.001149408,0.0004320218,0.0006565759,0.001421385],"category_scores_gemma":[0.004469262,0.0001367921,0.0006100124,0.001125549,0.0001442761,0.001155459,0.0006001699,0.0005885563,0.001515168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005321707,"about_ca_system_score_gemma":0.0004988222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004201972,"about_ca_topic_score_gemma":0.003304759,"domain_scores_codex":[0.9984599,0.0004787269,0.0001921077,0.0002252039,0.0004525367,0.0001915247],"domain_scores_gemma":[0.9981211,0.0008044859,0.0001948267,0.0001395589,0.0006113765,0.000128716],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003305973,0.0009528005,0.1277956,0.0009603431,0.0006577324,0.0003431832,0.0004316893,0.02983146,0.0348204,0.001534638,0.03662434,0.7627419],"study_design_scores_gemma":[0.00008451669,0.001067765,0.05911518,0.00009215229,0.0001546332,0.0002010648,0.0006105291,0.9076551,0.02271396,0.000915413,0.007326199,0.00006355528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.918577,0.003070846,0.0550595,0.001025702,0.0007979413,0.0003928652,0.004963604,0.00376445,0.01234811],"genre_scores_gemma":[0.9372787,0.0009992471,0.04910375,0.0001710458,0.0002595356,0.0001737997,0.008679694,0.00007529413,0.003258864],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004201972,"threshold_uncertainty_score":0.01315939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02650380666096772,"score_gpt":0.2734290867632074,"score_spread":0.2469252801022397,"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."}}