在COVID-19大流行期间,使用自然语言处理来识别与抑郁,焦虑和压力症状相关的模式
Abigail Beech1, Haoxue Fan2, Jocelyn Shu2
1Department of Psychology, Tufts University, 490 Boston Ave, Medford, MA 02155, USA; Department of Psychology, Harvard University, 33 Kirkland, St Cambridge, MA 02138, USA.
Journal of affective disorders
|February 1, 2025
概括
在COVID-19大流行期间,叙事反应中的语言模式与抑郁和焦虑症状有关. 机器学习模型显示,语言使用解释了症状变异的很大一部分.
科学领域:
- 心理语言学 心理语言学
- 计算语言学 计算语言学
- 心理健康研究 心理健康研究
背景情况:
- 研究语言使用与心理健康之间的联系至关重要,尤其是在流行病等紧张时期.
- 传统方法 (词汇列表) 和数据驱动的自然语言处理 (NLP) 可以提供互补的见解.
研究的目的:
- 在COVID-19大流行期间,探索语言模式与抑郁,焦虑和压力 (DASS) 症状之间的联系.
- 通过机器学习来确定语言使用对DASS症状的预测能力.
主要方法:
- 1106名参与者描述了大流行体验,并填写了DASS问卷 (2020-2021).
- 语言调查和词汇计数 (LIWC),情感分析和NLP技术被用来分析语言.
- 建立了机器学习回归模型,以评估语言和DASS症状之间的关系.
主要成果:
- 在DASS症状与使用第一人称单数代名词,绝对语言和负情感词语之间发现了显著的正相关性.
- 当控制人口因素并使用负面情绪得分时,这些相关性保持.
- 机器学习模型表明,语言使用占DASS症状变异的36-62%.
结论:
- 这项研究独特地结合了传统和数据驱动的方法来分析大流行期间的语言和DASS症状.
- 这些发现证实了先前关于负面情绪和绝对语言的研究,同时确定了流行病相关压力的新的语言相关性.
- 这些结果有助于更广泛地理解语言与心理健康之间的关系.
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