{"id":"W2951934610","doi":"10.48550/arxiv.1309.6352","title":"Using Nuances of Emotion to Identify Personality","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Affect (linguistics); Admiration; Psychology; Personality; Big Five personality traits; Emotion recognition; Cognitive psychology; Social psychology; Communication","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":[0.0002769212,0.0001783161,0.0003107678,0.0003015778,0.0000941849,0.0001228171,0.001130499,0.0001250049,0.0001134458],"category_scores_gemma":[0.00001780557,0.0002025583,0.0002516001,0.0006333823,0.00004645187,0.0003939044,0.001283102,0.0001745738,0.00006597398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001057599,"about_ca_system_score_gemma":0.00007542947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006241669,"about_ca_topic_score_gemma":0.00001008946,"domain_scores_codex":[0.9985999,0.0001134358,0.0002220166,0.0007327097,0.0001322065,0.0001996601],"domain_scores_gemma":[0.9985991,0.00003773124,0.0003270068,0.0007124212,0.0002126834,0.0001111257],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001631945,0.0001901269,0.04296112,0.0002023857,0.0004147784,0.00004128425,0.002204902,0.8073083,0.002131024,0.1421204,0.0007335003,0.001675908],"study_design_scores_gemma":[0.000147755,0.00002078123,0.01671634,0.0001568144,0.00007733958,7.938163e-7,0.0002352762,0.9737097,0.0004931059,0.008040505,0.0001060718,0.0002954877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5808313,0.00002604085,0.4182935,0.00003695244,0.000308606,0.00009154018,0.000002518221,0.00003105612,0.000378507],"genre_scores_gemma":[0.992101,0.00002837165,0.007334344,0.00004012567,0.00006042947,2.235252e-7,0.000005873136,0.000006158767,0.0004234448],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4112697,"threshold_uncertainty_score":0.826009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.20456984687118,"score_gpt":0.2679143075536152,"score_spread":0.06334446068243521,"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."}}