{"id":"W2467370890","doi":"10.1007/s11042-016-3643-4","title":"The role of social sentiment in stock markets: a view from joint effects of multiple information sources","year":2016,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science; Sentiment analysis; Social media; Stock (firearms); Stock market; Financial market; Data science; Artificial intelligence; Econometrics; World Wide Web; Economics; Finance","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":[0.004058998,0.0007286175,0.0008170794,0.002031792,0.0006379526,0.004688578,0.0008037971,0.001296658,0.002799621],"category_scores_gemma":[0.01643908,0.0006561805,0.001067823,0.001655909,0.001393703,0.0058467,0.001850462,0.001236347,0.0002431125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007347906,"about_ca_system_score_gemma":0.0006414931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00364197,"about_ca_topic_score_gemma":0.003693237,"domain_scores_codex":[0.9983299,0.0008261938,0.00008748923,0.0002661283,0.0003728723,0.0001173684],"domain_scores_gemma":[0.9861456,0.01038155,0.001116932,0.0008090191,0.001187974,0.0003590252],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001215396,0.0008712971,0.1181316,0.0009982456,0.001925552,0.001552241,0.003602223,0.1521053,0.03673796,0.3426431,0.008161116,0.332056],"study_design_scores_gemma":[0.00007581473,0.0004057461,0.07021397,0.0002290267,0.001052983,0.000326661,0.001316202,0.6833874,0.005239147,0.2307355,0.00687066,0.0001469801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5840728,0.00632868,0.363323,0.009048271,0.000433379,0.0001713479,0.001247755,0.0002682336,0.03510655],"genre_scores_gemma":[0.9833192,0.001379582,0.01267067,0.0001460507,0.0003357321,0.00003820067,0.0001499019,0.00004246087,0.001918147],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004688578,"threshold_uncertainty_score":0.02146631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03930137581247266,"score_gpt":0.3230011539908792,"score_spread":0.2836997781784065,"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."}}