基于语言或评分尺度的情绪分类:语言和alexithymia的计算分析和alexithymia
Sverker Sikström1, Miriam Nicolai2, Josephine Ahrendt2
1Department of Psychology, Lund University, Lund, Sweden. sverker.sikstrom@psy.lu.se.
Npj mental health research
|July 31, 2024
概括
通过计算方法分析的基于语言的反应在分类心理健康状况方面比传统的评级表更准确. 亚历克西西米亚并没有影响分类准确性,但使叙述更难解释.
科学领域:
- 计算精神病学是一种计算精神病学.
- 在心理健康评估中的自然语言处理.
- 心理测量和情绪量化的量化.
背景情况:
- 评分表是定量心理健康评估的主要方法,通常被认为比自然语言更有效.
- 亚历克西西米亚 (难以表达情绪) 对基于语言的情感沟通的准确性的影响尚未得到充分理解.
- 当前的方法可能无法完全捕捉通过语言传达的情绪状态的细微差别.
研究的目的:
- 为了比较基于问题的语言计算分析 (QCLA) 与标准评分尺度在从叙述中分类情绪状态的准确性.
- 为了调查alexithymia对QCLA和评级尺度评估的准确性的影响.
- 通过语言的计算分析来探索心理健康评估的潜在改进.
主要方法:
- 参与者生成了描述情绪事件 (抑郁,焦虑,满意度,和) 的叙述.
- 用描述性词和评分尺度 (PHQ-9,GAD-7,SWLS,HILS) 总结了叙述中的情感.
- 潜伏语义分析 (LSA) 量化了单词,多项归还分类了情绪状态;评估了alexithymia水平.
主要成果:
- 基于语言的反应,当通过计算分析时,与传统的评分尺度相比,在分类情绪状态方面表现出更高的准确性.
- 亚历克西西米亚并没有影响基于单词或评分尺度方法的分类准确性,这表明QCLA对该特征的稳定性.
- 亚历克西西米亚较高的个体的叙述更难分类,这表明叙述表现力的潜在限制.
结论:
- 基于语言的响应的计算分析为评估心理健康状况提供了比当前评分尺度更准确的方法.
- 在分类准确性方面,QCLA似乎对alexithymia不敏感,但高的alexithymia可以阻碍叙事中的清晰情感交流.
- 将计算语言分析集成到心理健康评估中可以提高诊断精度和对情绪体验的理解.
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