{"id":"W2122412231","doi":"10.2466/pr0.105.2.509-521","title":"Using the Revised Dictionary of Affect in Language to Quantify the Emotional Undertones of Samples of Natural Language","year":2009,"lang":"en","type":"article","venue":"Psychological Reports","topic":"Emotions and Moral Behavior","field":"Psychology","cited_by":214,"is_retracted":false,"has_abstract":true,"ca_institutions":"Laurentian University","funders":"","keywords":"Affect (linguistics); Natural language; Natural (archaeology); Psychology; Normative; Linguistics; Natural language processing; Word (group theory); Computer science; Dimension (graph theory); Artificial intelligence; Cognitive psychology; Communication; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006668909,0.0009554921,0.0008055515,0.004989172,0.0007397432,0.002013703,0.000873287,0.0007149022,0.003671713],"category_scores_gemma":[0.02699609,0.0003727329,0.0009546098,0.00332206,0.001914433,0.002075799,0.002042317,0.001795223,0.001705252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001241201,"about_ca_system_score_gemma":0.001323579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001793509,"about_ca_topic_score_gemma":0.004326642,"domain_scores_codex":[0.9921876,0.002594682,0.002347961,0.0005058956,0.002146272,0.0002175477],"domain_scores_gemma":[0.9808395,0.00867783,0.00241678,0.001965312,0.005685752,0.0004149075],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008230905,0.00026859,0.05769597,0.003992043,0.0002644787,0.0007991288,0.02051945,0.002428053,0.02977338,0.04793447,0.05079196,0.7847094],"study_design_scores_gemma":[0.0001773113,0.001853368,0.3462839,0.002266666,0.0002262167,0.007758037,0.00990791,0.007519949,0.0107673,0.04481434,0.5676752,0.000749843],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.313565,0.01653725,0.5273941,0.003737202,0.004357032,0.01097367,0.01927864,0.001710197,0.1024469],"genre_scores_gemma":[0.3939803,0.006640029,0.5605342,0.001362472,0.0005575831,0.01208641,0.007099803,0.0006896199,0.01704966],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006668909,"threshold_uncertainty_score":0.03526896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1264591701163034,"score_gpt":0.4635331539970555,"score_spread":0.337073983880752,"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."}}