Detecting Intensity of Anxiety in Language of Student Veterans with Social Anxiety Using Text Analysis
Detecting Intensity of Anxiety in Language of Student Veterans with Social Anxiety Using Text Analysis
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DOI：
10.1080/15228835.2022.2163452
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发表时间：
2023-04
影响因子：
1.5
通讯作者：
Morgan Byers;Mark H. Trahan;E. Nason;Chinyere Y. Eigege;Nicole E. Moore;Micki Washburn;V. Metsis
中科院分区：
文献类型：
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作者：
Morgan Byers;Mark H. Trahan;E. Nason;Chinyere Y. Eigege;Nicole E. Moore;Micki Washburn;V. Metsis
摘要大约三分之一的退伍军人患有创伤后应激障碍，这是一种经常与社交焦虑障碍共病的精神疾病。学生退伍军人特别容易受到伤害，因为他们努力适应一种新的，结构较少的生活方式，很少有同龄人理解他们的困难。为了支持心理健康专家治疗社交焦虑症，这项研究利用机器学习来检测从患者访谈中转录的文本中的焦虑，并应用主题建模来突出退伍军人学生的常见压力因素。我们通过探索深度学习和传统的机器学习策略（如transformers，transfer learning和support vector classifiers）来完成焦虑检测任务。我们的模型提供了一个工具，以支持心理学家和社会工作者在治疗社交焦虑。本文中详细介绍的结果也可能在教育学和公共卫生等领域产生更广泛的影响。1
Abstract 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