Multimodal Sentiment Analysis Based on Cross-Modal Attention and Gated Cyclic Hierarchical Fusion Networks.

Multimodal sentiment analysis has been an active subfield in natural language processing. This makes multimodal sentiment tasks challenging due to the use of different sources for predicting a speaker's sentiment. Previous research has focused on extracting single contextual information within a modality and trying different modality fusion stages to improve prediction accuracy. However, a factor that may lead to poor model performance is that this does not consider the variability between modalities. Furthermore, existing fusion methods tend to extract the representational information of individual modalities before fusion. This ignores the critical role of intermodal interaction information for model prediction. This paper proposes a multimodal sentiment analysis method based on cross-modal attention and gated cyclic hierarchical fusion network MGHF. MGHF is based on the idea of distribution matching, which enables modalities to obtain representational information with a synergistic effect on the overall sentiment orientation in the temporal interaction phase. After that, we designed a gated cyclic hierarchical fusion network that takes text-based acoustic representation, text-based visual representation, and text representation as inputs and eliminates redundant information through a gating mechanism to achieve effective multimodal representation interaction fusion. Our extensive experiments on two publicly available and popular multimodal datasets show that MGHF has significant advantages over previous complex and robust baselines.