RUAL: Uncertainty-Aware Learning for Robust Multimodal Sentiment Analysis

Multimodal sentiment analysis (MSA) has made significant progress in integrating heterogeneous information from text, speech, and vision. However, real-world multimodal data often suffer from modality noise, semantic inconsistency, and incomplete modality information, which can weaken cross-modal fusion and reduce the reliability of sentiment prediction. To address these challenges, this paper proposes RUAL, a robust uncertainty-aware learning framework for multimodal sentiment analysis. Specifically, RUAL first employs a Gathered Multi-Head Attention Pooling (GMHA) module to aggregate intra-modal features and estimate modality uncertainty based on attention entropy. Then, an Uncertainty-Aware Cross-Modal Coupled Layer (UACCL) is introduced to dynamically regulate cross-modal residual fusion according to sample confidence, thereby reducing the negative influence of unreliable modalities on fused representations. In addition, uncertainty-weighted learning and uncertainty-guided self-distillation (UWL and U-SD) are jointly integrated through an optimization strategy to further improve training stability and generalization in complex scenarios. Experimental results on CMU-MOSI, CMU-MOSEI, and MVSA-Single demonstrate that RUAL achieves strong overall performance and maintains stable prediction results under missing-modality and Gaussian-noise conditions, validating the effectiveness and robustness of the proposed framework for multimodal sentiment analysis.