Detecting Intensity of Anxiety in Language of Student Veterans with Social Anxiety Using Text Analysis.
| Autor: | Byers, Morgan, Trahan, Mark, Nason, Erica, Eigege, Chinyere, Moore, Nicole, Washburn, Micki, Metsis, Vangelis |
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| Předmět: |
DEEP learning
DECISION trees PSYCHOLOGY of college students CONFIDENCE intervals MACHINE learning REGRESSION analysis RANDOM forest algorithms PSYCHOLOGY of veterans SOCIAL anxiety INTRACLASS correlation CHI-squared test DESCRIPTIVE statistics RESEARCH funding ANXIETY STATISTICAL models LOGISTIC regression analysis SECONDARY analysis ALGORITHMS |
| Zdroj: | Journal of Technology in Human Services; Apr-Jun2023, Vol. 41 Issue 2, p125-147, 23p, 6 Charts, 9 Graphs |
| Abstrakt: | Approximately one-third of the veteran population suffers from post-traumatic stress disorder, a mental illness that is often co-morbid with social anxiety disorder. Student veterans are especially vulnerable as they struggle to adapt to a new, less structured lifestyle with few peers who understand their difficulties. To support mental health experts in the treatment of social anxiety disorder, this study utilized machine learning to detect anxiety in text transcribed from interviews with patients and applied topic modeling to highlight common stress factors for student veterans. We approach our anxiety detection task by exploring both deep learning and traditional machine learning strategies such as transformers, transfer learning, and support vector classifiers. Our models provide a tool to support psychologists and social workers in treating social anxiety. The results detailed in this paper could also have broader impacts in fields such as pedagogy and public health.1 [ABSTRACT FROM AUTHOR] |
| Databáze: | Complementary Index |
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