{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005088147,0.000141427,0.0002648717,0.0002292983,0.0001537412,0.00004743629,0.0003684438,0.0001604403,0.0002760654],"category_scores_gemma":[0.00002202137,0.0001614993,0.0003225408,0.0005508887,0.000153248,0.0000542853,0.0005807816,0.0003098283,0.00005178865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001890699,"about_ca_system_score_gemma":0.0002659396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002660253,"about_ca_topic_score_gemma":0.0003388606,"domain_scores_codex":[0.9987438,0.0002133962,0.000185133,0.0005113899,0.0001587499,0.0001875115],"domain_scores_gemma":[0.9991768,0.0001470897,0.000150874,0.0002393537,0.0001910491,0.00009483825],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001143775,0.0000448528,0.001364001,0.00006926715,0.0002095227,0.00007456612,0.001049998,0.7266119,0.000004476399,0.2696188,0.0001169577,0.0008241857],"study_design_scores_gemma":[0.0001018566,0.00001460197,0.0004849142,0.0001425992,0.0004063665,3.846153e-7,0.0005108235,0.4919918,0.00003720362,0.5047718,0.001293818,0.0002437927],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7650321,0.0003407834,0.2041553,0.0003272663,0.0007563582,0.0001825262,0.00001951587,0.00009013646,0.02909604],"genre_scores_gemma":[0.9865875,0.0001838775,0.001569013,0.000009609057,0.0001937481,3.005817e-7,0.00001413942,0.000008181172,0.01143366],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.235153,"threshold_uncertainty_score":0.6585748,"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."}}