A Multi-Stage Hierarchical Relational Graph Neural Network for Multimodal Sentiment Analysis
Peizhu Gong, Jin Liu, Xiliang Zhang, Xingye Li
Abstract
Multimodal sentiment analysis targets at accurately perceiving the emotional states by incorporating related information from multiple sources. However, existing methods mostly neglect the unbalanced contributions and inherent relational interactions across distinct modalities. In this paper, we propose a multi-stage hierarchical relational graph neural network (MHRG), catering to intra- and inter-modal dynamics learning with modality calibration. In the first stage, modality-specific graph convolution modules are introduced to learn the intra-modal sequential semantics. In the second, we design a modality-adaptive modification module to determine the contribution of each modality based on the prediction confidence. Finally, diverse inter-modal dynamics are considered respectively by a novel hierarchical relational graph fusion method for further aggregation according to the type of interactions. Extensive experiments on benchmark datasets demonstrate that MHRG outperforms the existing methods and achieves the state-of-the-art performance.