基于功能融合的变压器用于社交网络中的情绪分析
Shiyong Li1, He Li2, Juan Du3
1School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang, China.
PloS one
|November 7, 2025
概括
这项研究引入了一种新的多式联络方法,用于分析社交媒体内容以评估心理健康. 与现有方法相比,特征融合型变压器 (FFBT) 模型显著提高了情绪分析准确性和F1得分.
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.
- 计算机视觉 计算机视觉
背景情况:
- 由于数据类型多样化,用于心理健康评估的社交媒体分析具有挑战性.
- 单模态情绪分析技术与复杂的多模态社交媒体信息作斗争.
- 现有的方法可能会产生不准确或矛盾的用户情绪状态评估.
研究的目的:
- 提出一个有效的多式联络情绪分析框架来评估用户的心理健康.
- 克服社交媒体分析中传统单模式方法的局限性.
- 从社交媒体内容中提高情绪状态检测的准确性.
主要方法:
- 使用RoBERTa进行文本特征提取,使用ResNet50进行图像特征提取.
- 采用多模式变压器架构进行特征对齐和融合.
- 整合了一个完全连接的网络 (FCN) 用于最终的情绪分类.
主要成果:
- 拟议的特征融合型变压器 (FFBT) 模型表现出卓越的性能.
- 与现有的情绪分析算法相比,FFBT的准确性提高了4.1%.
- 在一个定制的社交媒体数据集上,FFBT显示F1分数有5%的改善.
结论:
- 该FFBT框架有效地融合了多式联运特征,用于准确的情绪分析.
- 多模式情绪分析对于从社交媒体获得可靠的心理健康评估至关重要.
- 在计算心理健康研究中,FFBT提供了一个有前途的进步.
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