{"id":"W2950151462","doi":"","title":"AffectiveTweets: a Weka package for analyzing affect in tweets","year":2019,"lang":"en","type":"article","venue":"NPARC","topic":"Mental Health via Writing","field":"Psychology","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Affect (linguistics); Computer science; R package; Artificial intelligence; Natural language processing; Information retrieval; Programming language; Linguistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008244138,0.002045379,0.0007654447,0.002601695,0.0007247173,0.001550995,0.001112027,0.0006207985,0.03844324],"category_scores_gemma":[0.006489447,0.001159372,0.002019422,0.001320481,0.0002635723,0.0014175,0.001419525,0.002198749,0.02799423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005740424,"about_ca_system_score_gemma":0.001097034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006079397,"about_ca_topic_score_gemma":0.01470469,"domain_scores_codex":[0.9993967,0.0001423488,0.00009271227,0.0001497102,0.0001619951,0.00005657527],"domain_scores_gemma":[0.9980097,0.001253191,0.0001785896,0.0001835883,0.0003252658,0.00004965067],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000798428,0.0003332011,0.01029049,0.003143339,0.001040416,0.0008409493,0.001607257,0.01585762,0.02104064,0.005707571,0.7005469,0.2387932],"study_design_scores_gemma":[0.0006623027,0.0002943172,0.03578814,0.0004726128,0.0009802576,0.0008287055,0.0007816004,0.2669982,0.04736897,0.0419687,0.6032001,0.0006560453],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"software","genre_gemma":"software","genre_scores_codex":[0.01604737,0.0004673327,0.3216521,0.0007549053,0.0004021071,0.001980558,0.1384853,0.5073454,0.01286481],"genre_scores_gemma":[0.1356539,0.0008906068,0.5804164,0.001030599,0.0001838174,0.01232259,0.163641,0.06652039,0.03934064],"genre_candidate":"software","genre_consensus":"software","teacher_disagreement_score":0.03844324,"threshold_uncertainty_score":0.1286054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0319584371172445,"score_gpt":0.3729191907356667,"score_spread":0.3409607536184222,"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."}}