{"id":"W4388192518","doi":"10.2196/49531","title":"Momentary Depressive Feeling Detection Using X (Formerly Twitter) Data: Contextual Language Approach","year":2023,"lang":"en","type":"article","venue":"JMIR AI","topic":"Mental Health via Writing","field":"Psychology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Mitacs","keywords":"Feeling; Lexicon; Transfer of learning; Psychology; Artificial intelligence; Context (archaeology); Computer science; Machine learning; Distress; Categorization; Binary classification; Convolutional neural network; Social psychology; Clinical psychology; Support vector machine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004534337,0.0005224422,0.0002367926,0.001019701,0.0002880157,0.0005185157,0.0003130393,0.0003641682,0.001247837],"category_scores_gemma":[0.001876066,0.00009514099,0.0002875693,0.0006496524,0.0001987849,0.0006565361,0.0007986458,0.0004180496,0.0008004889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002741079,"about_ca_system_score_gemma":0.0003848629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00506379,"about_ca_topic_score_gemma":0.01015257,"domain_scores_codex":[0.9997002,0.0001000066,0.00002987479,0.00008094629,0.00004379645,0.00004511909],"domain_scores_gemma":[0.9993778,0.0002686736,0.0001027264,0.00007654095,0.0001391572,0.00003504735],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002138807,0.0005949048,0.2223206,0.001569718,0.0002183311,0.002374627,0.002167526,0.0226741,0.09520254,0.005925904,0.03951565,0.6052974],"study_design_scores_gemma":[0.0001078333,0.000851078,0.2519019,0.0002940023,0.0002649966,0.001648213,0.006330091,0.6225766,0.0501326,0.009885374,0.0558201,0.0001872689],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8899679,0.001292769,0.07722086,0.001188929,0.0002838109,0.0003605676,0.01824418,0.003104846,0.008336135],"genre_scores_gemma":[0.9284077,0.0004011671,0.05791184,0.0002022613,0.000142564,0.0001905441,0.01052445,0.00006634333,0.002153207],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00506379,"threshold_uncertainty_score":0.01006866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1240098328586816,"score_gpt":0.4383199983535391,"score_spread":0.3143101654948575,"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."}}