{"id":"W4416159922","doi":"10.4236/ajibm.2025.1511087","title":"Research on the Nonlinear Impact of Investor Sentiment on Stock Returns Based on Deep Learning and Text Mining","year":2025,"lang":"","type":"article","venue":"American Journal of Industrial and Business Management","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Sentiment analysis; Stock market; Stock (firearms); Deep learning; sort; Empirical research; Investment decisions; Support vector machine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001247449,0.0004805411,0.0003677271,0.001444852,0.0001839699,0.0009889968,0.0003605563,0.0003539371,0.0008508632],"category_scores_gemma":[0.005558904,0.0001950364,0.0005344898,0.001716186,0.0002998876,0.002045559,0.000324741,0.0006175087,0.000198729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004358264,"about_ca_system_score_gemma":0.0004570063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002150247,"about_ca_topic_score_gemma":0.002785794,"domain_scores_codex":[0.9995677,0.0001174232,0.00004209032,0.00008925031,0.0001389042,0.00004457341],"domain_scores_gemma":[0.9972851,0.001861731,0.0003467879,0.0001139822,0.000342636,0.00004974207],"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":[0.0001939258,0.0003666652,0.201496,0.000794292,0.0004820973,0.0006098054,0.0007577268,0.08460888,0.01767284,0.02417053,0.00309311,0.6657542],"study_design_scores_gemma":[0.00001047446,0.0001120468,0.09051004,0.0001248286,0.0001535081,0.0001659777,0.0002482711,0.8826821,0.006698771,0.01619907,0.003058134,0.00003666976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7211044,0.006198965,0.258348,0.002626977,0.0001877212,0.00009928081,0.0006250784,0.0002414875,0.01056793],"genre_scores_gemma":[0.9704588,0.003574739,0.02353016,0.0001397394,0.0001385179,0.00002667197,0.0003901925,0.00001802613,0.001723124],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002150247,"threshold_uncertainty_score":0.006597221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1747910939123401,"score_gpt":0.4431133618977228,"score_spread":0.2683222679853826,"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."}}