在社交媒体上验证情绪分析 检测心理痛苦的文本:一项跨部门调查
Sehee Kim1, Seungjea Lee2, Elina Lee3
1Department of Nursing, Pai Chai University, Daejeon, South Korea.
Issues in mental health nursing
|April 23, 2025
概括
社交媒体的帖子揭示了与心理痛苦相关的情绪. 情绪分析准确地预测心理健康状况,有助于早期发现和干预.
科学领域:
- 计算语言学计算语言学
- 精神病学是一个精神病学.
- 社交媒体分析
背景情况:
- 社交媒体平台越来越多地用于沟通,可能反映用户的心理状态.
- 了解社交媒体文本的情感内容可以提供对心理健康的见解.
- 现有的评估心理痛苦的方法通常依赖于自我报告,这可能是耗时和主观的.
研究的目的:
- 调查自我报告的心理痛苦与社交媒体帖子中表达的情绪之间的相关性.
- 评估基于深度学习的情绪分析模型对预测心理困扰的有效性.
- 探索社交媒体情绪分析在心理健康监测和早期发现方面的潜力.
主要方法:
- 一项涉及87名参与者进行的横截面研究,他们完成了心理困扰调查,并同意分析他们的Instagram和Threads帖子.
- 使用深度学习模型 (KoBERT) 在2610个文本句子中分类七种情绪 (快乐,悲伤,愤怒,中立,焦虑,厌恶,惊喜).
- 使用SPSS软件进行了统计分析,包括皮尔森的相关性,t测试和ROC曲线 (AUC 0.8450.924,p < 0.001).
主要成果:
- 在社交媒体帖子中的情绪表达和自我报告的心理痛苦之间发现了显著的关联.
- 幸福和悲伤等情绪与各种类型的心理痛苦有着最强烈的相关性.
- 情绪分析模型对心理痛苦的预测准确度很高,AUC值超过0.845.
结论:
- 对社交媒体文本的情感分析是识别和监测心理痛苦的可行工具.
- 这些发现支持将社交媒体情绪分析整合到心理健康干预中,以进行早期检测.
- 这种方法提供了一种新的,数据驱动的方法,用于理解和潜在地应对心理健康挑战.
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