Enhanced Multimodal Sentiment Analysis via Tensor Decomposition and Hypergraph Convolutional Networks

Multimodal sentiment analysis aims to accurately identify emotional tendencies and expressions from three modalities: text, audio and video, which is useful for opinion guidance and mental health analysis. Current studies have focused on fusing different sources of information from certain practical problems, focusing on the textual modality and ignoring the interaction and complementary information between modalities. In light of the aforementioned limitations, we propose a general model of hypergraph neural network. This model employs a tensor structure to fully obtain the interaction and complementary information during fusion, a low-rank decomposition of the tensor to achieve downscaling and a more accurate feature representation, and a hypergraph convolution and an attention mechanism to facilitate the capture of complex relationships in emotional expression. The experimental results on two benchmark datasets demonstrate the effectiveness of our model. Our approach outperforms existing methods by improving performance while reducing computation time, exhibiting strong generalization ability and high competitiveness.