TriSAT: Trimodal Representation Learning for Multimodal Sentiment Analysis

Transformer-based multimodal sentiment analysis frameworks commonly facilitate cross-modal interactions between two modalities through the attention mechanism. However, such interactions prove inadequate when dealing with three or more modalities, leading to increased computational complexity and network redundancy. To address this challenge, this paper introduces a novel framework, Trimodal representations for Sentiment Analysis from Transformers (TriSAT), tailored for multimodal sentiment analysis. TriSAT incorporates a trimodal transformer featuring a module called Trimodal Multi-Head Attention (TMHA). TMHA considers language as the primary modality, combines information from language, video, and audio using a single computation, and analyzes sentiment from a trimodal perspective. This approach significantly reduces the computational complexity while delivering high performance. Moreover, we propose Attraction-Repulsion (AR) loss and Trimodal Supervised Contrastive (TSC) loss to further enhance sentiment analysis performance. We conduct experiments on three public datasets to evaluate TriSAT's performance, which consistently demonstrates its competitiveness compared to state-of-the-art approaches.