{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008399787,0.0001503771,0.0003015686,0.0001829674,0.00004455016,0.00001634598,0.0001588786,0.0001223856,0.002245432],"category_scores_gemma":[0.00006737386,0.0001554269,0.00008735018,0.0002456552,0.00002251741,0.00008535492,0.00004205982,0.0002200441,0.001457247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001642567,"about_ca_system_score_gemma":0.00002382177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004059935,"about_ca_topic_score_gemma":0.00005968404,"domain_scores_codex":[0.9983749,0.0002159497,0.0002740142,0.0004286905,0.0001199934,0.0005864136],"domain_scores_gemma":[0.9987272,0.0007024933,0.00009976874,0.0003527676,0.00002064896,0.00009710537],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004571477,0.0003287973,0.8376887,0.0005892546,0.00007384213,0.00004554832,0.003674019,0.00000230925,0.06884072,0.01193062,0.006194154,0.07017482],"study_design_scores_gemma":[0.01008368,0.001685169,0.9575194,0.0007135235,0.00004634474,0.00003963875,0.002450136,0.0004100213,0.008063354,0.007827586,0.01030104,0.0008601205],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.836182,0.00008305135,0.0003892211,0.0002130351,0.0005761105,0.001270582,0.00001674853,0.00005982478,0.1612094],"genre_scores_gemma":[0.9955943,0.00000208906,0.001033464,0.0003263454,0.0001535662,0.0002630975,0.00001490445,0.00003999674,0.002572246],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1594123,"threshold_uncertainty_score":0.9993202,"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."}}