TC-DSC: Text-Centric Hierarchical Dual-Stream Interaction for Incomplete Multimodal Sentiment Analysis

Incomplete multimodal sentiment analysis has attracted increasing research interest in recent years. Existing methods attempt to recover missing modalities through generative reconstruction and text-enhanced fusion, but these approaches may be limited in preserving sentiment-relevant information and fully leveraging complementary and hierarchical cross-modal interactions, particularly under noisy or incomplete conditions. To address these challenges, we propose TC-DSC, a text-centric hierarchical dual-stream interaction framework for incomplete multimodal sentiment analysis. Rather than reconstructing raw signals, TC-DSC performs semantic alignment and consistency modeling in the feature space through structured interactions between a text-centric stream and auxiliary audio-visual streams. A multi-scale enhanced encoder is designed to improve the robustness of non-text modalities under noisy conditions. Furthermore, a hierarchical proxy layer enables bidirectional interaction, with the text modality serving as a semantic anchor to guide cross-modal alignment. A semantic distillation strategy is also incorporated to facilitate knowledge transfer in the feature space under modality missing. Extensive experiments on MOSI, MOSEI, and SIMS demonstrate that TC-DSC achieves competitive performance and consistent improvements under both complete and incomplete settings.