{"id":"W2568636341","doi":"10.1371/journal.pone.0168843","title":"Measuring Emotion in Parliamentary Debates with Automated Textual Analysis","year":2016,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Social and Cultural Dynamics","field":"Social Sciences","cited_by":112,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Parliament; Mood; Polarity (international relations); Sentiment analysis; Politics; Social psychology; State (computer science); Recession; Positive economics; Political science; Political economy; Psychology; Cognitive psychology; Sociology; Law; Economics; Natural language processing; Computer science; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001615447,0.00005747248,0.0001364707,0.00006183141,0.0001532654,0.00002655035,0.00008589605,0.00004614265,0.0001422007],"category_scores_gemma":[0.00005993045,0.0000354631,0.00003173993,0.0006349034,0.00008827905,0.0001820496,0.00001166213,0.00003505982,0.00003156592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000153057,"about_ca_system_score_gemma":0.00002843415,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004962163,"about_ca_topic_score_gemma":0.03485817,"domain_scores_codex":[0.9991665,0.00008665299,0.00009781116,0.0001266006,0.0003367758,0.0001856873],"domain_scores_gemma":[0.9997478,0.00004483449,0.00004137003,0.00005495928,0.00005946231,0.00005154527],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003779012,0.0006778588,0.9689411,0.00001112562,0.0009695412,0.000007387062,0.01210675,0.00002823053,0.007525122,0.0007835467,0.00004003257,0.008871506],"study_design_scores_gemma":[0.0009833862,0.00011426,0.9632805,0.0003963713,0.001004899,1.575364e-7,0.02737748,0.00276156,0.002525063,0.00101831,0.00006604516,0.0004719455],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966836,0.00002220916,0.00002756951,0.001233175,0.00000858301,0.0001011267,0.000004786387,0.0001971907,0.001721742],"genre_scores_gemma":[0.9990434,0.00006701135,0.000251239,0.00004374324,0.00005577321,0.00001045379,0.000006198507,0.000004052161,0.0005181527],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02989601,"threshold_uncertainty_score":0.9827532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06994116544060205,"score_gpt":0.2486623110027469,"score_spread":0.1787211455621448,"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."}}