{"id":"W6993946052","doi":"","title":"#Emotional Tweets","year":2012,"lang":"nl","type":"article","venue":"National Research Council Canada (Government of Canada)","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":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":[0.0005714208,0.0007641074,0.0003423863,0.002294369,0.0009733629,0.001358362,0.0003786088,0.0005161392,0.04606392],"category_scores_gemma":[0.003771583,0.0002876379,0.0003306927,0.001748407,0.0001626382,0.001233322,0.001146847,0.0005734485,0.03230467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004662304,"about_ca_system_score_gemma":0.0004189342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001491602,"about_ca_topic_score_gemma":0.00309904,"domain_scores_codex":[0.9989467,0.0001860009,0.0001633108,0.0001689636,0.0004068814,0.0001282157],"domain_scores_gemma":[0.9983571,0.0004700438,0.000192063,0.0001874803,0.0006764304,0.0001168249],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009394361,0.0002705002,0.039638,0.001959849,0.00008584325,0.0008405818,0.002100618,0.001128338,0.03124643,0.01080506,0.5131878,0.3977975],"study_design_scores_gemma":[0.00006497388,0.0001543699,0.05433189,0.0001805765,0.00008261341,0.0008095216,0.001381418,0.007762393,0.0198386,0.005410178,0.9099046,0.00007880547],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.1842241,0.001453511,0.04046927,0.004049724,0.003217554,0.002742486,0.4877582,0.009775016,0.2663101],"genre_scores_gemma":[0.3621632,0.001720365,0.06369332,0.001615355,0.001329357,0.003863266,0.3808778,0.002190857,0.1825464],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.04606392,"threshold_uncertainty_score":0.1540992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1516396559792366,"score_gpt":0.3089078420307986,"score_spread":0.157268186051562,"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."}}