{"id":"W3123889119","doi":"10.2139/ssrn.2652876","title":"Econometrics Meets Sentiment: An Overview of Methodology and Applications","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke; Center for Interuniversity Research and Analysis on Organizations; HEC Montréal","funders":"","keywords":"Sentiment analysis; Econometrics; Field (mathematics); Computer science; Econometric model; Software; Data science; Artificial intelligence; Economics; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.00220195,0.00007984405,0.0002106707,0.0003011299,0.00006990626,0.00005918434,0.0004297679,0.00003734248,0.00004024618],"category_scores_gemma":[0.00001051984,0.00007185867,0.00007674842,0.0004808717,0.00001733274,0.0003518593,0.00009201262,0.0002994153,0.0000193491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008631068,"about_ca_system_score_gemma":0.0002470181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001007841,"about_ca_topic_score_gemma":0.00001478529,"domain_scores_codex":[0.9986312,0.0001413822,0.0002704658,0.0002180028,0.0001360481,0.0006029006],"domain_scores_gemma":[0.9992732,0.0001005307,0.0002385519,0.0002694656,0.00005677519,0.00006149799],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000001799615,0.0000358776,0.004081173,0.000006057025,0.0001063617,1.28884e-7,0.00008670893,0.00005357762,0.0003733301,0.9002177,0.00000515285,0.09503211],"study_design_scores_gemma":[0.002335642,0.001413679,0.009347597,0.00004742971,0.0002130538,0.0006133622,0.002004132,0.04894209,0.002363768,0.895669,0.03627562,0.0007745678],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1060511,0.03122947,0.8608379,0.0006346984,0.0001764806,0.0001875768,8.785159e-7,0.00002434499,0.0008576099],"genre_scores_gemma":[0.9432933,0.02333103,0.03232935,0.0001610508,0.0001353068,0.000006618881,0.000004654604,0.00001277699,0.0007258999],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8372422,"threshold_uncertainty_score":0.2930312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09112290359342227,"score_gpt":0.3420389202828799,"score_spread":0.2509160166894576,"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."}}