摘要
现有分析社会媒体中用户情感的方法,大多依赖于某一种模态数据的信息,缺少多种模态数据的信息融合,并且现有方法缺少分析多种模态数据的信息层次结构之间的关联。针对上述问题,该文提出多层次特征融合注意力网络,在分别提取社会媒体中文本和图像多层次特征的基础上,通过计算“图文”特征与“文图”特征,实现多模态的情感特征互补,从而准确感知社会媒体中用户的情感。在Yelp和MultiZOL数据集上的实验结果表明,该文方法可有效提升多模态数据情感分类的准确率。
Existing methods for sentiment analysis in social media usually deal with single modal data,without capturing the relationship between multimodal information.This paper propose to treat the hierarchical structure relations between texts and images in social media as complementarity.This paper designs a multi-level feature fusion attention network to capture both the‘images-text’and the‘text-images’relations to perceive the user’s sentiments in social media.Experimental results on Yelp and MultiZOL datasets show that this method can effectively improve the sentiment classification accuracy for multimodal data.
作者
王靖豪
刘箴
刘婷婷
王媛怡
柴艳杰
WANG Jinghao;LIU Zhen;LIU Tingting;WANG Yuanyi;CHAI Yanjie(Faculty of Information Science and Technology,Ningbo University,Ningbo,Zhejiang 315211,China;College of Science and Technology,Ningbo University,Cixi,Zhejiang 315300,China)
出处
《中文信息学报》
CSCD
北大核心
2022年第10期145-154,共10页
Journal of Chinese Information Processing
关键词
情感分析
注意力机制
多模态
sentiment analysis
attention mechanism
multimodal
