{"id":"W2889229100","doi":"","title":"NLP for Conversations: Sentiment, Summarization, and Group Dynamics","year":2018,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; University of the Fraser Valley","funders":"","keywords":"Automatic summarization; Computer science; Natural language processing; Sentiment analysis; Artificial intelligence; Dynamics (music); Information retrieval; Group (periodic table); Psychology","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":[0.004400794,0.0009488459,0.0008258212,0.002536814,0.001494018,0.0025575,0.001070213,0.001078957,0.005163825],"category_scores_gemma":[0.02965687,0.0004442107,0.0008051083,0.002867084,0.0005975799,0.006166846,0.002051763,0.002027575,0.003252694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009015207,"about_ca_system_score_gemma":0.001071412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002286099,"about_ca_topic_score_gemma":0.002302055,"domain_scores_codex":[0.9958286,0.002307843,0.0003318266,0.0006984819,0.0006552272,0.0001780777],"domain_scores_gemma":[0.9825434,0.01296154,0.0009928436,0.001601217,0.001589223,0.0003117538],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009171231,0.0004248792,0.007998055,0.001336918,0.0002451411,0.0003764234,0.00620324,0.01671482,0.02629807,0.03730067,0.04569811,0.8564866],"study_design_scores_gemma":[0.00008897978,0.000243619,0.009763062,0.000253839,0.000184298,0.0002915314,0.003759824,0.747309,0.01625489,0.1788331,0.04291267,0.0001052944],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08452837,0.001638396,0.8854877,0.004536615,0.0004917681,0.0004463371,0.007724075,0.00525764,0.009889054],"genre_scores_gemma":[0.601488,0.001019247,0.3731925,0.0003271044,0.0009167192,0.0006941078,0.01658889,0.0007849846,0.004988445],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005163825,"threshold_uncertainty_score":0.02327389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02964105130895849,"score_gpt":0.3340051228688455,"score_spread":0.304364071559887,"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."}}