{"id":"W4406000332","doi":"10.2196/51154","title":"Investigating Reddit Data on Type 2 Diabetes Management During the COVID-19 Pandemic Using Latent Dirichlet Allocation Topic Modeling and Valence Aware Dictionary for Sentiment Reasoning Analysis: Content Analysis","year":2025,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Regional Municipality of Waterloo; University of Toronto; McMaster University; Public Health Ontario; University Health Network","funders":"","keywords":"Sentiment analysis; Pandemic; Latent Dirichlet allocation; Type 2 diabetes; Social media; Context (archaeology); Coronavirus disease 2019 (COVID-19); Anxiety; Topic model; Coping (psychology); Psychology; Medicine; Computer science; Diabetes mellitus; Disease; Clinical psychology; Artificial intelligence; Psychiatry; World Wide Web; Geography; Pathology; Infectious disease (medical specialty)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.002270657,0.0005633284,0.0003571767,0.003353335,0.0005344748,0.0008972107,0.0003794565,0.000806756,0.002323229],"category_scores_gemma":[0.009731729,0.0001397544,0.0006101022,0.002262603,0.0003440405,0.001039716,0.001143399,0.0008194994,0.001448355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007808142,"about_ca_system_score_gemma":0.0004146144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004426707,"about_ca_topic_score_gemma":0.00813626,"domain_scores_codex":[0.9987116,0.0005213547,0.0001205963,0.0002342818,0.0002886188,0.0001236484],"domain_scores_gemma":[0.9916521,0.005913811,0.0008005518,0.0004039426,0.0009173428,0.000312337],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003191842,0.001135895,0.4254973,0.007432485,0.0004174211,0.003106157,0.02540039,0.009373427,0.02930064,0.004345381,0.2428762,0.2479229],"study_design_scores_gemma":[0.000172366,0.0006038875,0.7254593,0.001015339,0.0001838286,0.0009181948,0.0213387,0.06601197,0.008448859,0.003034209,0.1726176,0.0001956524],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7413805,0.001576985,0.008081059,0.002007535,0.0007204798,0.0007501023,0.2334477,0.001314748,0.01072076],"genre_scores_gemma":[0.7217442,0.0007811648,0.02764182,0.0005890308,0.0004703836,0.001762613,0.2401324,0.0002528126,0.006625563],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004426707,"threshold_uncertainty_score":0.01200849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2402302599110167,"score_gpt":0.4363376247510936,"score_spread":0.1961073648400768,"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."}}