{"id":"W2950676070","doi":"10.48550/arxiv.1906.03677","title":"Happy Together: Learning and Understanding Appraisal From Natural\\n Language","year":2019,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Embedding; Computer science; Sociality; Artificial intelligence; Agency (philosophy); Task (project management); Machine learning; Natural language processing; Focus (optics); Artificial neural network; Machine translation; Cognitive psychology; Psychology; Sociology; Engineering","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":[0.0008447092,0.001043574,0.0002672032,0.000780835,0.0002722358,0.001200394,0.0007028587,0.0006269971,0.003396976],"category_scores_gemma":[0.003678212,0.0002020884,0.0005773085,0.0005695908,0.0002844474,0.002359431,0.0008129849,0.001188623,0.001738809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000504044,"about_ca_system_score_gemma":0.0002446462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001761193,"about_ca_topic_score_gemma":0.004591607,"domain_scores_codex":[0.9995963,0.0001478814,0.00002063318,0.0001165425,0.00007119816,0.00004752292],"domain_scores_gemma":[0.999241,0.0003312973,0.0001548839,0.00008457001,0.0001255888,0.00006275486],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001087409,0.0006550277,0.06391911,0.0005212122,0.0003819018,0.0007133409,0.002904688,0.01328535,0.0271375,0.01379018,0.05837879,0.8172254],"study_design_scores_gemma":[0.0001126679,0.0007508544,0.09723998,0.0002190168,0.000236332,0.0005490195,0.004379798,0.724673,0.01879128,0.07136055,0.08151239,0.0001751226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5875684,0.004151043,0.354645,0.003061228,0.0009925779,0.0004249567,0.005946801,0.004286624,0.03892329],"genre_scores_gemma":[0.9161221,0.0006719499,0.0657807,0.0004485048,0.0003151683,0.0001908173,0.00651357,0.0001392988,0.009817883],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003396976,"threshold_uncertainty_score":0.01136404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0777231234466074,"score_gpt":0.2242216538109689,"score_spread":0.1464985303643615,"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."}}