Learning Adaptive Cross-Modal Interactions for Multimodal Sentiment Analysis

Multimodal sentiment analysis aims to integrate heterogeneous textual, visual, and acoustic information for effective emotion understanding. However, existing methods often suffer from insufficient cross-modal interaction modeling, limited adaptability in multimodal fusion, and inadequate suppression of modality-specific noise under complex conversational scenarios. To address these challenges, this paper proposes a framework for learning adaptive cross-modal interactions for multimodal sentiment analysis. The proposed framework consists of three stages: modality-aware preprocessing, heterogeneous representation learning, and adaptive multimodal fusion. First, a unified preprocessing strategy is designed to improve cross-modal consistency through textual normalization, speaker-aware visual alignment, and utterance-level acoustic representation enhancement. Second, modality-specific encoders are constructed to capture complementary semantic, spatial, and utterance-level acoustic characteristics from textual, visual, and acoustic modalities, respectively. Third, an adaptive fusion framework is introduced to explicitly model cross-modal interactions, dynamically estimate the importance of different modality combinations, and further calibrate discriminative feature channels through channel attention. By jointly performing modality-level interaction learning and channel-wise feature refinement, the proposed framework effectively enhances multimodal representation capability for sentiment classification. Extensive experiments conducted on the CMU-MOSI and MELD benchmark datasets demonstrate that our framework consistently outperforms previous methods. In particular, the proposed model achieves 90.27% accuracy and 90.26% F1-score on CMU-MOSI, together with 66.57% accuracy and 66.21% F1-score on MELD. Additional ablation studies and qualitative analyses further validate the effectiveness of the proposed preprocessing strategy, modality-specific representation learning, and adaptive fusion mechanism.