通过情感表示来增强TextGCN在社交媒体上检测抑郁症的功能
1School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China.
Frontiers in psychology
|September 11, 2025
概括
这项研究使用TextGCN与情绪化社交媒体数据增强了抑郁症检测. 整合像MentalBERT这样的预训练模型显著提高了准确性,保留了表情符号,以便更好地分析情感暗示.
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
- 人工智能的人工智能
背景情况:
- 抑郁症是一种广泛的心理健康问题,需要有效的检测策略.
- 全球心理健康障碍负担迫使诊断工具的进步.
- 社交媒体数据为理解和检测抑郁症提供了丰富的来源.
研究的目的:
- 使用TextGCN模型提高抑郁症检测准确度.
- 为了提高表现,利用社交媒体的情感表现.
- 在TextGCN.中探索微调预训练语言模型的集成.
主要方法:
- 开发了一个增强的TextGCN模型,包含了情感嵌入.
- 使用了微调的模型:MentalBERT,MentalRoBERTa,以及RoBERTa的抑郁检测.
- 保留了表情符号和表情符号作为代币,将它们视为情感线索.
主要成果:
- 增强的TextGCN模型显示了显著的性能改善.
- 与MentalBERT,MentalRoBERTa和RoBERTaDepressionDetection嵌入式的集成提高了准确性.
- 与基线模型相比,在五个基准数据集中验证了性能增长.
结论:
- 预先训练有素的模型可以在TextGCN中显著提高情绪表现,用于抑郁症检测.
- 拟议的方法为未来的心理健康研究和应用提供了坚实的基础.
- 未来的工作包括改进模型,以平衡的数据集为有针对性的心理健康应用.
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