{"id":"W3171449285","doi":"10.3390/ijerph18115993","title":"Sentiment Analysis on COVID-19-Related Social Distancing in Canada Using Twitter Data","year":2021,"lang":"en","type":"article","venue":"International Journal of Environmental Research and Public Health","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":90,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Social distance; Confusion matrix; Social media; Sentiment analysis; Support vector machine; Confusion; Computer science; Distancing; Coronavirus disease 2019 (COVID-19); Artificial intelligence; Psychology; Internet privacy; World Wide Web; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0007551613,0.0003823572,0.0002964892,0.002875724,0.002101324,0.001192038,0.0004714275,0.0003106135,0.001368739],"category_scores_gemma":[0.003346495,0.0001158564,0.0003240603,0.005293387,0.0004978153,0.0004179538,0.0007081325,0.0003786281,0.00048884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01034159,"about_ca_system_score_gemma":0.009013399,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9131058,"about_ca_topic_score_gemma":0.9369789,"domain_scores_codex":[0.9992117,0.00006854871,0.00005352598,0.00008765992,0.0003962198,0.0001824401],"domain_scores_gemma":[0.9971923,0.0003792788,0.0002430818,0.00006615534,0.001924681,0.000194451],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001076901,0.0002386828,0.7341802,0.001114939,0.0001515722,0.002584507,0.01480089,0.006337021,0.01768369,0.002516296,0.06009673,0.1592186],"study_design_scores_gemma":[0.00002174629,0.00008303944,0.8840255,0.0001796341,0.00008625197,0.0002288386,0.02685697,0.03084527,0.005445722,0.0003032406,0.05182815,0.00009557918],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9604626,0.0003576066,0.001330357,0.0007867522,0.00007037475,0.0002588297,0.02688006,0.0001569759,0.009696438],"genre_scores_gemma":[0.9582481,0.0005399784,0.003665737,0.0001273085,0.00004109802,0.0001733357,0.02918828,0.000031355,0.007984779],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08689421,"threshold_uncertainty_score":0.1748118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1962002402594325,"score_gpt":0.4338740922199852,"score_spread":0.2376738519605527,"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."}}