CSGI-Net: A Cross-Sample Graph Interaction Network for Multimodal Sentiment Analysis

With the widespread application of multimodal data in sentiment analysis, effectively integrating information from different modalities to improve the accuracy and robustness of sentiment analysis has become a critical issue. Although current fusion methods using Transformer architectures have enhanced inter-modal interaction and alignment to some extent, challenges such as the neglect of intra-modal feature complexity and the imbalance in multimodal data optimization limit the full utilization of modality-specific information by multimodal models. To address these challenges, we propose a novel multimodal sentiment analysis model: Cross-Sample Graph Interaction Network (CSGI-Net). Specifically, CSGI-Net facilitates interaction and learning between each sample and its similar samples within the same modality, thereby capturing the common emotional characteristics among similar samples. During the training process, CSGI-Net quantifies and calculates the optimization differences between modalities and dynamically adjusts the optimization amplitude based on these differences, thereby providing under-optimized modalities with more opportunities for improvement. Experimental results demonstrate that CSGI-Net achieves superior performance on two major multimodal sentiment analysis datasets: CMU-MOSI and CMU-MOSEI.