{"id":"W4280558757","doi":"10.2196/32335","title":"Public Opinion and Sentiment Before and at the Beginning of COVID-19 Vaccinations in Japan: Twitter Analysis","year":2022,"lang":"en","type":"article","venue":"JMIR Infodemiology","topic":"Vaccine Coverage and Hesitancy","field":"Social Sciences","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Vaccination; Coronavirus disease 2019 (COVID-19); Sentiment analysis; Pandemic; Public opinion; Medicine; Political science; Computer science; Virology; Politics; Disease; Artificial intelligence; Law; Infectious disease (medical specialty); Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001091996,0.0000573477,0.0001837449,0.0002136421,0.0005690095,0.00001372221,0.0001263089,0.00005037653,0.0004825796],"category_scores_gemma":[0.0002064251,0.00004678627,0.00004474709,0.0005498106,0.00004296847,0.00008273396,0.0003195654,0.0001299526,7.399478e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001564155,"about_ca_system_score_gemma":0.00009379648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009636233,"about_ca_topic_score_gemma":0.005158025,"domain_scores_codex":[0.9989145,0.0004133715,0.0002196974,0.0001634237,0.0001091877,0.0001797985],"domain_scores_gemma":[0.9993596,0.0002985915,0.0001274639,0.0001244367,0.00002415399,0.00006573343],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000006902354,0.00001554012,0.9619692,0.00000625916,0.00003598724,6.467179e-7,0.02334351,0.000319822,0.000006928638,0.01203034,0.001856673,0.0004081956],"study_design_scores_gemma":[0.0002871933,0.00005243846,0.9488337,0.000001164274,0.00002755272,0.000002592488,0.006903494,0.0005548628,5.824966e-7,0.004148851,0.03912603,0.000061542],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9509549,0.0003443682,0.00007078966,0.04764837,0.00005151996,0.0002125624,0.000006714125,0.00001115299,0.0006995907],"genre_scores_gemma":[0.9973701,0.00009471463,0.00002794249,0.00203635,0.00002950269,0.0001051099,0.00001769975,0.000002444644,0.0003161421],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04641517,"threshold_uncertainty_score":0.5283908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06509322435306095,"score_gpt":0.3722945626278956,"score_spread":0.3072013382748346,"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."}}