{"id":"W4416109878","doi":"10.54254/2754-1169/2025.bj29289","title":"Financial Market Trend Forecasting using Text Sentiment Analysis: Social Media, News and Economic Texts with Transformer-based Neural Networks","year":2025,"lang":"","type":"article","venue":"Advances in Economics Management and Political Sciences","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Sentiment analysis; Stock market; Financial market; Tone (literature); Big data; Economic forecasting; Stock (firearms); Market research","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.004682962,0.0006253413,0.001204064,0.001893695,0.001180408,0.00116899,0.0008598418,0.000190658,0.0001773487],"category_scores_gemma":[0.0002974552,0.0005430168,0.0002464867,0.002160172,0.00282552,0.001482013,0.0003454454,0.0003285233,9.800007e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003520348,"about_ca_system_score_gemma":0.0002175598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001697852,"about_ca_topic_score_gemma":0.008044749,"domain_scores_codex":[0.9938937,0.0005221224,0.001644574,0.001857773,0.0003861662,0.001695614],"domain_scores_gemma":[0.9941686,0.004652262,0.0005150436,0.0003246086,0.00003518307,0.0003043137],"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":[0.000409374,0.00008256631,0.1560322,0.00007523414,0.0001780148,0.00001592161,0.0002440264,0.1038547,1.286206e-7,0.07742812,0.00003451584,0.6616452],"study_design_scores_gemma":[0.001417744,0.0001561526,0.03881885,0.000117375,0.000704469,0.000005431169,0.001980523,0.9103891,0.000005232222,0.04368412,0.002115691,0.0006052654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8943679,0.003379832,0.04551607,0.001743007,0.00171991,0.0009199992,0.00007891931,0.00003187075,0.05224245],"genre_scores_gemma":[0.986002,0.000687587,0.0122515,0.0005724898,0.0002224617,0.00003381016,0.000003982985,0.00001647404,0.0002097192],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8065345,"threshold_uncertainty_score":0.9998882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04370209628420246,"score_gpt":0.3459265984774238,"score_spread":0.3022245021932213,"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."}}