{"id":"W4311110675","doi":"10.36227/techrxiv.21694676.v1","title":"Depression sentiment analysis based on social media content like Twitter","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Mental Health via Writing","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Feeling; Social media; Computer science; Task (project management); Sentiment analysis; Focus (optics); Subject (documents); Artificial intelligence; Psychology; World Wide Web; Social 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":[0.0004347204,0.0005736,0.0003596821,0.001741312,0.0003924149,0.0009406853,0.0002247033,0.0003306911,0.004426897],"category_scores_gemma":[0.001567817,0.000110646,0.000598971,0.0009745725,0.0001470826,0.0007409648,0.0003897251,0.0003689987,0.002731464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003370637,"about_ca_system_score_gemma":0.0001886267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001802802,"about_ca_topic_score_gemma":0.003082106,"domain_scores_codex":[0.9996001,0.00007942828,0.00004445014,0.00007022957,0.0001386386,0.00006727509],"domain_scores_gemma":[0.9995623,0.0001120144,0.00006742612,0.00002000116,0.0002061957,0.00003202574],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002001091,0.0004525084,0.1634533,0.001983688,0.0005390485,0.002347714,0.002742937,0.004914019,0.1303488,0.003375163,0.1252455,0.5625963],"study_design_scores_gemma":[0.0001271705,0.0006211526,0.5940189,0.0004141642,0.0004860761,0.001704516,0.008354924,0.2112474,0.06691951,0.004947315,0.1109492,0.0002096605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8648608,0.001711403,0.04297964,0.002713985,0.001373097,0.001018235,0.030791,0.002090691,0.0524611],"genre_scores_gemma":[0.9546406,0.0008377814,0.01770511,0.0002541935,0.0003552091,0.0004601516,0.01294376,0.0001472272,0.01265603],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004426897,"threshold_uncertainty_score":0.01480943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1611363807831524,"score_gpt":0.415584756753539,"score_spread":0.2544483759703866,"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."}}