在文本中基于DRIVE模型的心理健康检测的AI框架:关于COVID-19期间应对策略如何表达的案例研究
Loulwah AlSumait1, Altaf AlFarhan1, Hasah AlHeneidi2
1Department of Information Science, Kuwait University, Kuwait City, Kuwait.
PeerJ. Computer science
|June 26, 2025
概括
这项研究引入了一个人工智能框架,用于从社交媒体文本中检测心理健康应对策略,揭示了70%的用户使用了积极的应对机制. 人工智能模型准确地区分了积极和消极的应对策略.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 心理健康信息学心理健康信息学
背景情况:
- 一个人工智能 (AI) 框架被提议用于检测个人在社交网络上的心理健康 (MH) 状态.
- 该框架分析用户的文本帖子以识别情绪,应对策略,资源和需求,使用需求-资源-个体效应 (DRIVE) 模型.
- 传统的情绪分析 (SA) 在检测诸如应对策略之类的微妙心理健康方面是有限的,因此需要基于应对的分析.
研究的目的:
- 从社交网络中定义一个AI框架来检测心理健康状况和应对策略.
- 为了说明SA在心理健康分析中的局限性,并证明基于应对的分析的有效性.
- 揭示个人用来表达他们环境中的心理应对的短语和主题.
主要方法:
- 从社交网络X收集有关COVID-19期间压力的文本数据 (2019年11月-2022年5月).
- 使用自然语言处理 (NLP) 处理文本,将帖子编码成正负应对类别,并应用SA和统计分析.
- 利用隐性迪里克莱特分配 (LDA) 和大图NLP来识别主题和术语,开发和测试应对分类模型.
主要成果:
- 70%的分析帖子表现出积极的应对策略,包括自我照顾,寻求帮助和积极的重构.
- 30%的帖子表明了负面的应对策略,例如阴谋思想和妄想.
- 应对分类模型的平均准确率为74.8%,超过了像TextBlob和VADER这样的SA方法,这些方法显示出高的错误分类率.
结论:
- 开发的AI框架有效地检测基于DRIVE模型的心理健康应对策略.
- 在危机期间,个人通过积极的应对策略 (如社会支持和积极的重构) 来表现出弹性.
- 应对分类模型可靠地区分了积极和消极的应对类别,提供了对心理适应的见解.
关键词:
分类 分类 分类 分类.应对情况 应对情况我们的框架框架框架.心理健康 心理健康情绪分析是一种情绪分析.社交媒体挖矿是什么意思文本挖掘 (Text Mining) 是一种文字挖掘方式.主题建模 主题建模他们的推特是Twitter.幸福 幸福是一种幸福.更多相关视频
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