MSDC:基于多尺度双通道特征融合的方面级情绪分析模型
Xiaoye Lou1, Guangzhong Liu1, Yangshuyi Xu1
1College of Information Engineering, Shanghai Maritime University, Shanghai, China.
PloS one
|October 21, 2025
概括
这项研究引入了一种新的方面级情绪分析模型 (MSDC),通过融合多层次的双通道信息来增强特征提取. 该模型显著提高了准确性,F1在细粒度情绪分析任务中的得分.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 计算语言学 计算语言学
背景情况:
- 面向层面的情绪分析需要精细的文字处理.
- 现有的模型在高维语法依赖和特征提取方面扎.
- 多个意见词引入噪音,复杂的方面-术语情感理解.
研究的目的:
- 提出一种新的方面级情绪分析模型 (MSDC),解决现有的单道方法的局限性.
- 通过多规模的双通道融合来增强功能提取和情感理解.
- 为了提高精细的情绪分析的准确性和F1得分.
主要方法:
- 实施了多规模的双通道特征融合方法.
- 利用多头门式自我注意和图形神经网络通道来增强特征表示.
- 引入了自适应特征融合机制,以动态调整方面对上下文权重.
- 使用囊网络进行集成数据处理.
主要成果:
- 拟议的MSDC模型在公共数据集上表现出卓越的有效性.
- 与现有技术相比,观察到精度和F1值的显著改善.
- 该模型在精细的文本情绪分析任务中表现出色.
结论:
- MSDC模型有效地解决了面向层面情绪分析的局限性.
- 多尺度双通道融合和自适应加权增强了情绪理解.
- 该模型为细粒度情绪分析应用提供了有前途的进步.
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