{"id":"W4414969774","doi":"10.48550/arxiv.2510.03633","title":"Predicting Stock Price Movement with LLM-Enhanced Tweet Emotion Analysis","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Stock price; Stock (firearms); Sentiment analysis; Volatility (finance); Classifier (UML); Eye movement; Time series","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":[0.0003120004,0.0004882763,0.0002661772,0.0007022842,0.0001285096,0.0004836383,0.0003183608,0.0003781799,0.001245012],"category_scores_gemma":[0.001533298,0.0001581407,0.0003503791,0.0004294403,0.0001024951,0.0007505671,0.0003642996,0.0006706815,0.0008049592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003543899,"about_ca_system_score_gemma":0.0002315126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003728671,"about_ca_topic_score_gemma":0.005176193,"domain_scores_codex":[0.9998909,0.00001856191,0.000009586806,0.00002953986,0.00002979984,0.00002165172],"domain_scores_gemma":[0.9996631,0.0001439536,0.00005460671,0.00002996127,0.00008830815,0.00002004901],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007115708,0.0004613442,0.04491289,0.0001440886,0.0001791613,0.0003207974,0.0002449057,0.1861875,0.06630374,0.002498524,0.01073124,0.6873043],"study_design_scores_gemma":[0.000004748105,0.0000310887,0.003638629,0.000003882418,0.00001099417,0.00001591002,0.00001866996,0.9916275,0.003425159,0.000717712,0.000499698,0.000006006484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5784889,0.001023804,0.4070467,0.001248413,0.0002970822,0.00009429122,0.002076685,0.003380537,0.006343564],"genre_scores_gemma":[0.9532467,0.0001850366,0.04217033,0.000110478,0.00009655635,0.00004205181,0.001163756,0.00003557775,0.002949439],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003728671,"threshold_uncertainty_score":0.007413924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1281331243977541,"score_gpt":0.3988583599284989,"score_spread":0.2707252355307448,"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."}}