Modal Interactive Feature Encoder for Multimodal Sentiment Analysis  

作　　者：Xiaowei Zhao Jie Zhou Xiujuan Xu 

机构地区：[1]School of Software,Dalian University of Technology,Dalian 116620,China [2]Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province,Dalian 116620,China

出　　处：《国际计算机前沿大会会议论文集》2023年第2期285-303,共19页International Conference of Pioneering Computer Scientists, Engineers and Educators（ICPCSEE）

摘　　要：Multimodal Sentiment analysis refers to analyzing emotions in infor-mation carriers containing multiple modalities.To better analyze the features within and between modalities and solve the problem of incomplete multimodal feature fusion,this paper proposes a multimodal sentiment analysis model MIF(Modal Interactive Feature Encoder For Multimodal Sentiment Analysis).First,the global features of three modalities are obtained through unimodal feature extraction networks.Second,the inter-modal interactive feature encoder and the intra-modal interactive feature encoder extract similarity features between modal-ities and intra-modal special features separately.Finally,unimodal special features and the interaction information between modalities are decoded to get the fusion features and predict sentimental polarity results.We conduct extensive experi-ments on three public multimodal datasets,including one in Chinese and two in English.The results show that the performance of our approach is significantly improved compared with benchmark models.

关 键 词：Multimodal Sentiment Analysis Modal Interaction Feature ENCODER 

分 类 号：TS1[轻工技术与工程—纺织科学与工程]