{"id":"W4391272168","doi":"10.48550/arxiv.2401.13805","title":"Longitudinal Sentiment Topic Modelling of Reddit Posts","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Tone (literature); Social media; Longitudinal study; Gauge (firearms); Longitudinal data; 2019-20 coronavirus outbreak; Topic model; Sentiment analysis; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Psychology; Media studies; History; Sociology; World Wide Web; Computer science; Linguistics; Medicine; Information retrieval","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001957728,0.0004576218,0.000305482,0.001639339,0.0005965243,0.001290645,0.000422276,0.0005008197,0.002202835],"category_scores_gemma":[0.00699542,0.0001416255,0.0004462365,0.001644701,0.0003332089,0.0008597989,0.0004716574,0.0008223985,0.001258511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001048123,"about_ca_system_score_gemma":0.0009531861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02797738,"about_ca_topic_score_gemma":0.04085001,"domain_scores_codex":[0.9992481,0.0002815262,0.00003727292,0.0001781833,0.000144925,0.0001099725],"domain_scores_gemma":[0.995698,0.002801892,0.00045566,0.0001764985,0.0006988489,0.0001691324],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001518217,0.0006082224,0.6512416,0.0007911925,0.0003700257,0.0008547205,0.01269201,0.03624591,0.02568602,0.0105325,0.03250592,0.2269537],"study_design_scores_gemma":[0.00004191868,0.0002313405,0.483318,0.0001327381,0.0001623943,0.0002847768,0.006465371,0.4600656,0.005420533,0.007112207,0.0366817,0.00008348579],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9456552,0.001087429,0.03273532,0.001816534,0.0002224564,0.0001541222,0.01101838,0.0002934772,0.007017068],"genre_scores_gemma":[0.9823558,0.0003103828,0.005953616,0.0001040584,0.0001390548,0.0001308271,0.006840227,0.00004069256,0.004125394],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02797738,"threshold_uncertainty_score":0.05562907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2024027943885875,"score_gpt":0.2742950576568612,"score_spread":0.07189226326827372,"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."}}