Choi, AdiLi, WeihuaWarren, Jim2024-02-072024-02-072024-01-25Studies in Health Technology and Informatics, ISSN: 0926-9630 (Print); 0926-9630 (Online), IOS Press, 310, 1442-1443. doi: 10.3233/SHTI2312350926-96300926-9630http://hdl.handle.net/10292/17204Digital tools for mental health show great promise, but concerns arise when they fail to recognize the user state. We train a classifier to detect the emotional context of dialogs among 6 categories, achieving 78% accuracy on top choice. Importantly greatest areas of confusion (excited-hopeful, angry-sad) are not of the most unsafe kind. Such a classifier could serve as a resource to the dialog managers of future digital mental health agents.https://creativecommons.org/licenses/by-nc/4.0/deed.en_USDialog agentsempathetic computinge-therapymachine learningDialog agentse-therapyempathetic computingmachine learning4203 Health Services and Systems42 Health SciencesMental HealthMental health3 Good Health and Well Being0807 Library and Information Studies1117 Public Health and Health ServicesMedical Informatics4203 Health services and systems4601 Applied computingDigital HealthEmotionsMental HealthMental HealthEmotionsDigital HealthMental HealthEmotionsDigital HealthJournal ArticleOpenAccess10.3233/SHTI231235