{"id":"W4313441566","doi":"10.17163/ings.n29.2023.10","title":"Sentimental analysis of COVID-19 twitter data using deep learning and machine learning models","year":2023,"lang":"en","type":"article","venue":"Ingenius","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Sentiment analysis; Social media; Artificial intelligence; Random forest; Naive Bayes classifier; Machine learning; Computer science; Support vector machine; Logistic regression; Coronavirus disease 2019 (COVID-19); Deep learning; Gradient boosting; Natural language processing; World Wide Web; Medicine; Infectious disease (medical specialty)","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":[],"consensus_categories":[],"category_scores_codex":[0.001085054,0.00006478571,0.0001505962,0.0003533621,0.0005104844,0.00008993785,0.0001605315,0.00004665759,0.0003576751],"category_scores_gemma":[0.0004623961,0.00006474127,0.00003782118,0.001120258,0.00009371573,0.0004681022,0.0001824711,0.0001128309,0.00001242359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005654539,"about_ca_system_score_gemma":0.00007542038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003141023,"about_ca_topic_score_gemma":0.0006199068,"domain_scores_codex":[0.9989992,0.0001512977,0.000190541,0.0001423952,0.0003146776,0.0002019051],"domain_scores_gemma":[0.9994484,0.000111551,0.0001296129,0.0001229139,0.00002777093,0.0001597222],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004282704,0.00003463845,0.08004628,0.00007133669,0.001027858,0.00001583378,0.5052766,0.3897931,0.001710057,0.0007788494,0.0006506296,0.02055196],"study_design_scores_gemma":[0.0001679266,0.000008540553,0.0007211458,0.000004662426,0.0001903735,8.730058e-7,0.02295043,0.9660928,0.00002810281,0.00007303309,0.009670882,0.00009120587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9856097,0.0001949792,0.008529396,0.000286377,0.00005643602,0.00008277217,0.00001534861,0.0001106808,0.005114389],"genre_scores_gemma":[0.9983767,0.0001749863,0.0003702946,0.0001696151,0.00002901045,2.354709e-7,0.0001382425,0.000005905727,0.000735022],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5762997,"threshold_uncertainty_score":0.4748307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2294664708420546,"score_gpt":0.4117349147168313,"score_spread":0.1822684438747766,"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."}}