Multi-Task Hypergraph-Attention Framework for Multimodal Sentiment Analysis

Multimodal sentiment analysis has emerged as a critical research area. However, existing methods face significant challenges: (1) Unimodal feature extraction techniques often fail to capture the topological structure within data, and do not effectively integrate local and global information, leading to information loss. (2) Traditional multimodal fusion methods, such as concatenation, addition, and multiplication, struggle to model modality differences and inter-modal correlations. In this paper, we propose a novel multi-task hypergraph-attention framework (MTHA) to improve feature discrimination and model performance. Experimental results demonstrate that MTHA outperforms most baseline models in both sentiment classification and regression.