{"id":"W3006726006","doi":"10.1109/bigdata47090.2019.9006342","title":"Stock Prediction using Deep Learning and Sentiment Analysis","year":2019,"lang":"en","type":"article","venue":"","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Stock market; Computer science; Sentiment analysis; Stock (firearms); Stock market prediction; Econometrics; Artificial intelligence; Deep learning; Predictive power; Machine learning; Economics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00467136,0.00009618813,0.0002566561,0.000559143,0.0001361594,0.0001984442,0.0001493697,0.00005263929,0.002945646],"category_scores_gemma":[0.001543185,0.00007092231,0.0001111269,0.001587533,0.00002866811,0.0001976345,0.0001661688,0.0001141634,0.00008017469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003878046,"about_ca_system_score_gemma":0.00001296936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003998171,"about_ca_topic_score_gemma":0.00001194761,"domain_scores_codex":[0.9976705,0.0004643649,0.0003779559,0.0004602136,0.0008468002,0.000180188],"domain_scores_gemma":[0.997937,0.001385118,0.0001732545,0.0002793494,0.0001427805,0.00008250371],"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":[0.0000120941,0.000007273406,0.8920739,0.000001185946,0.00009261542,5.411836e-7,0.0001537415,0.009204973,0.001074252,0.00004284396,0.00003489719,0.09730168],"study_design_scores_gemma":[0.0001421813,0.00004049472,0.2299165,0.000003092439,0.0001280816,0.000006133022,0.000425519,0.7667966,0.0001783412,0.0005637489,0.001721015,0.00007827277],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.729431,0.00003731509,0.257399,0.00002104053,0.0001821633,0.0001049988,6.222905e-7,0.00004324764,0.01278061],"genre_scores_gemma":[0.9322116,0.000001520207,0.05729145,0.00002332675,0.00003081694,0.000001660964,9.064528e-7,0.000006845632,0.01043182],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7575916,"threshold_uncertainty_score":0.9979658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09282569742488601,"score_gpt":0.4124607944035287,"score_spread":0.3196350969786427,"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."}}