Applied Machine Intelligence, Bern University of Applied Sciences, Biel, Switzerland.
Stud Health Technol Inform. 2022 May 16;292:43-48. doi: 10.3233/SHTI220318.
Burnout syndrome and depression are prevalent mental health problems in many societies today. Most existing methods used in clinical intervention and research are based on inventories. Natural Language Processing (NLP) enables new possibilities to automatically evaluate text in the context of clinical Psychology. In this paper, we show how affective word list ratings can be used to differentiate between texts indicating depression or burnout, and a control group. In particular, we show that depression and burnout show statistically significantly higher arousal than the control group.
burnout 综合征和抑郁症是当今许多社会普遍存在的心理健康问题。大多数现有的临床干预和研究中使用的方法都是基于量表。自然语言处理（NLP）为自动评估临床心理学背景下的文本提供了新的可能性。在本文中，我们展示了如何使用情感词列表评分来区分表示抑郁或倦怠的文本和对照组。特别是，我们表明，抑郁和倦怠比对照组表现出统计学上显著更高的唤醒。
