PGMCR: Polarity-Guided Multimodal Consistency Regularization with Reliability Fusion for Multimodal Sentiment Analysis

Multimodal sentiment analysis must combine textual, visual, and acoustic evidence whose reliability varies across utterances. We propose PGMCR, which uses a predicted three-way polarity distribution to compose sample-level modality-reliability weights and applies a training-only consistency constraint to text-preserving visual and acoustic perturbations. Experiments on CMU-MOSI and CMU-MOSEI show that polarity-guided fusion is the principal architectural contribution, while the text-guided refinement and consistency branch have metric-dependent effects. The learned weights vary across polarity groups, but proxy-corruption diagnostics also reveal strong dependence on textual evidence and non-negligible polarity-estimation errors. The results support polarity as a useful conditioning signal for multimodal fusion while delimiting the scope of the nonverbal robustness claim.