From Feature Alignment to Multimodal Fusion: A Two-Stage Primary Modality-Guided Approach for MSA

Multimodal Sentiment Analysis (MSA) aims to leverage heterogeneous data-typically language, vision, and acoustic modalities-to accurately interpret human emotional states. Despite recent advances, challenges persist due to the feature distribution difference caused by intrinsic modality heterogeneity. Prior works either neglect the contribution disparity among modalities, especially the dominant role of language in sentiment reasoning, or emphasize language dominance in fusion-space alignment, ignoring coordination in the early feature space. To address these limitations, we propose a novel Two-Stage Primary Modality-Guided (TSPMG) framework, which introduces primary-modality supervision into both feature-space distribution alignment and fusion-space attention modulation. This dual-level cooperative mechanism progressively amplifies the dominant modality's influence throughout the entire representation learning pipeline. Extensive experiments on two benchmark datasets demonstrate that TSPMG achieves superior or comparable results to state-of-the-art baselines, with ablation studies further validating the effectiveness of primary-modality-guided strategies for robust and interpretable multimodal sentiment analysis. The code is available at https://github.com/Kaisa777/TSPMG.