使用NLP和变压器模型分析大学生抑郁症:对职业和教育咨询的影响
Qiuxia Wan1, Yue Pan1, Sonia Zakeri2
1Chengdu Sport University, Chengdu, China.
Brain and behavior
|September 9, 2025
概括
一个新的自然语言处理 (NLP) 框架通过社交媒体帖子准确地检测大学生的抑郁症. 这种工具有助于早期干预,并支持学生的福祉.
科学领域:
- 计算语言学计算语言学
- 心理健康技术 心理健康技术
- 社交媒体分析.
背景情况:
- 大学生抑郁症是影响学术和个人发展的一个重要问题.
- 目前用于抑郁症的诊断方法通常是缓慢和主观的.
- 社交媒体平台为早期发现心理健康问题提供了潜在的途径.
研究的目的:
- 开发一种自动化系统,使用社交媒体数据检测大学生抑郁症状.
- 解决传统,缓慢和主观抑郁症诊断方法的局限性.
- 为了利用数字行为来实现可访问的心理健康监测.
主要方法:
- 一个新的自然语言处理 (NLP) 框架,集成基于RoBERTa的变压器和封闭的循环单元 (GRU) 层.
- 利用多模式嵌入,包括行为,时间和上下文元数据.
- 分析连续的用户帖子,以捕捉语言使用的时间动态.
主要成果:
- 拟议的NLP框架在推特和Reddit上的抑郁性帖子的分类中实现了90.18%的准确性.
- 该模型在各种句子复杂度中展示了强大的性能.
- 统计比较证实了这种方法在基线模型和传统深度学习架构上的优越性.
结论:
- 该框架可以在社交媒体上实时检测抑郁信号.
- 它可以成为学术和职业咨询服务的宝贵工具.
- 促进早期识别和及时干预面临风险的学生,改善福祉和留守.
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