Dynamic Invariant-Specific Representation Fusion Network for Multimodal Sentiment Analysis

Multimodal sentiment analysis (MSA) aims to infer emotions from linguistic, auditory, and visual sequences. Multimodal information representation method and fusion technology are keys to MSA. However, the problem of difficulty in fully obtaining heterogeneous data interactions in MSA usually exists. To solve these problems, a new framework, namely, dynamic invariant-specific representation fusion network (DISRFN), is put forward in this study. Firstly, in order to effectively utilize redundant information, the joint domain separation representations of all modes are obtained through the improved joint domain separation network. Then, the hierarchical graph fusion net (HGFN) is used for dynamically fusing each representation to obtain the interaction of multimodal data for guidance in the sentiment analysis. Moreover, comparative experiments are performed on popular MSA data sets MOSI and MOSEI, and the research on fusion strategy, loss function ablation, and similarity loss function analysis experiments is designed. The experimental results verify the effectiveness of the DISRFN framework and loss function.