{"id":"W7132662964","doi":"","title":"#Emotional Tweets","year":2012,"lang":"en","type":"article","venue":"NPARC","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Lexicon; Microblogging; Social media; WordNet; Affect (linguistics); Association (psychology)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0005237255,0.0007853864,0.0003445123,0.002193965,0.0009101649,0.001265196,0.0003822622,0.0005016797,0.04033731],"category_scores_gemma":[0.003517478,0.0002803144,0.0003314258,0.001595974,0.0001571435,0.001212005,0.001048975,0.0005512796,0.0269089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004296006,"about_ca_system_score_gemma":0.000378019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001280202,"about_ca_topic_score_gemma":0.002588313,"domain_scores_codex":[0.9990245,0.0001706407,0.0001469109,0.0001608149,0.0003817338,0.0001153673],"domain_scores_gemma":[0.9985241,0.0004465305,0.000164638,0.0001751856,0.0005929499,0.00009664228],"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":[0.001031111,0.0003217021,0.04085416,0.001983473,0.00009518904,0.0009298225,0.00189817,0.001396746,0.04075648,0.01194998,0.4337601,0.4650231],"study_design_scores_gemma":[0.00007840178,0.0001977758,0.06295352,0.0001884959,0.0001052325,0.00107977,0.001445767,0.01366039,0.03089932,0.007139384,0.8821578,0.00009416706],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2240722,0.001471413,0.05514612,0.00371813,0.003023349,0.002921635,0.4422895,0.01160089,0.2557567],"genre_scores_gemma":[0.4102343,0.001540167,0.07415084,0.001320276,0.00118556,0.003622155,0.3469047,0.002082813,0.1589593],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04033731,"threshold_uncertainty_score":0.1349418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02788719215221436,"score_gpt":0.2661422046080296,"score_spread":0.2382550124558153,"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."}}