{"id":"W3034858636","doi":"10.2196/16862","title":"Using Natural Language Processing and Sentiment Analysis to Augment Traditional User-Centered Design: Development and Usability Study","year":2020,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute; National Institute on Aging; National Institute on Drug Abuse; Wellcome Trust; Dartmouth College","keywords":"Usability; Computer science; Sentiment analysis; Heuristic evaluation; User-centered design; Human–computer interaction; Artificial intelligence","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":[0.0229239,0.001120607,0.0006648289,0.001489361,0.000789901,0.001756728,0.001074045,0.0006754585,0.002063045],"category_scores_gemma":[0.03659136,0.0006301942,0.001100158,0.0007049778,0.001096806,0.002071788,0.00194381,0.0009223797,0.0007233701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001068375,"about_ca_system_score_gemma":0.00165924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000851426,"about_ca_topic_score_gemma":0.001547485,"domain_scores_codex":[0.9887338,0.008060521,0.0008128309,0.0008895828,0.001163077,0.0003402646],"domain_scores_gemma":[0.9541375,0.03441866,0.001292001,0.002242798,0.007244973,0.0006640513],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001798245,0.007655846,0.05163615,0.007345972,0.0002975155,0.002507868,0.1752701,0.004836833,0.08208957,0.003978847,0.009982273,0.6526009],"study_design_scores_gemma":[0.005916191,0.04667086,0.2452199,0.00607704,0.002108284,0.007453698,0.1368356,0.1726092,0.158434,0.01788264,0.1993322,0.00146048],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8633053,0.0002943712,0.117023,0.0003966194,0.00008653155,0.01329488,0.0004874956,0.0008950072,0.004216807],"genre_scores_gemma":[0.6126368,0.0004674237,0.3633624,0.0004939554,0.00004737549,0.01915015,0.0007004662,0.0003802963,0.002761141],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0229239,"threshold_uncertainty_score":0.1212347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2331635771528415,"score_gpt":0.4859047337073467,"score_spread":0.2527411565545051,"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."}}