在新浪微博上预测抑郁的自然语言处理:方法研究和分析
Zhenwen Zhang1, Jianghong Zhu1, Zhihua Guo1
1Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
JMIR mental health
|September 5, 2024
概括
这项研究介绍了一种使用自然语言处理来检测新浪微博上抑郁风险的新型在线方法. 这种先进的模型显著提高了检测准确度,为基于社交媒体的心理健康查提供了新的见解.
科学领域:
- 计算语言学计算语言学
- 心理健康信息学心理健康信息学
- 社交媒体分析.
背景情况:
- 抑郁症是全球主要的公共卫生问题,影响着数百万人.
- 目前的诊断和治疗面临障碍,加剧了危机.
- 需要可访问的,大规模的抑郁风险检测方法.
研究的目的:
- 开发一种新的在线抑郁风险检测方法.
- 使用自然语言处理 (NLP) 技术.
- 在中国社交媒体平台新浪微博上识别有抑郁风险的个人.
主要方法:
- 在新浪微博上收集了来自3200名用户的527,333条帖子 (1600名有抑郁症,1600名没有).
- 开发了一个带有词级,后级和语义聚合编码器的等级变压器网络.
- 运用统计和语言分析 (中文语言调查和单词计数) 来研究语言行为.
主要成果:
- 该模型在没有采样技术的情况下实现了84.62%的准确性.
- 基于检索的采样策略提高了性能,精度达到95.46%.
- 抑郁症患者增加了否定词和负面情绪词汇的使用.
结论:
- 深度学习方法在检测抑郁风险方面是可行的和有效的.
- 这些发现支持通过社交媒体进行大规模,自动化和非侵入性抑郁症预测.
- 语言行为分析提供了对抑郁症患者心理状态的见解.
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