{"id":"W4205204575","doi":"10.1093/rof/rfab036","title":"Language and Domain Specificity: A Chinese Financial Sentiment Dictionary","year":2021,"lang":"en","type":"article","venue":"European Finance Review","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Sentiment analysis; Word2vec; Computer science; Natural language processing; Artificial intelligence; Finance; Association (psychology); Psychology; 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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.006889279,0.0002037872,0.0005091079,0.0001153874,0.0001692247,0.0001050585,0.0003824671,0.0000233253,0.0008460924],"category_scores_gemma":[0.008671164,0.0001499479,0.0001741198,0.001747114,0.00008241999,0.0001412283,0.0004531856,0.0001972438,0.0006514278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001969624,"about_ca_system_score_gemma":0.00005978591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001435236,"about_ca_topic_score_gemma":0.000002528833,"domain_scores_codex":[0.9956619,0.001979809,0.0007174033,0.0006939531,0.0006946638,0.000252343],"domain_scores_gemma":[0.9979594,0.0007045738,0.0002791962,0.0008147164,0.0001580673,0.00008405853],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000009719935,0.00005375649,0.00278175,0.0001728812,0.000006647959,0.000898139,0.0002211991,0.000001792365,0.0002353196,0.0005388801,0.06062855,0.9344513],"study_design_scores_gemma":[0.0002004368,0.00002601204,0.2011592,0.001307104,0.00001484521,0.0003191585,0.00003157698,0.00002442446,0.00002376019,0.002199081,0.7944926,0.0002017735],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.2491646,0.5296625,0.0102176,0.002852783,0.001371918,0.0005761015,0.00004612649,0.0001202762,0.205988],"genre_scores_gemma":[0.07918499,0.6291677,0.2195132,0.01268432,0.00252433,0.00006987595,0.00005887932,0.0002181263,0.05657849],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9342496,"threshold_uncertainty_score":0.9996792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05589164492620741,"score_gpt":0.3743717673397242,"score_spread":0.3184801224135168,"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."}}