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Open Access

ARTICLE

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

Jinjin Liu1,2,3,*, Changchang Fan1,2, Qiulu Guo1,2, Yihao Xu1,2, Penghui Ma1,2
1 School of Computer Science, ZhongYuan University of Technology, Zhengzhou, China
2 Henan International Joint Laboratory of Artificial Intelligence Interpretability Reasoning and Application, Zhengzhou, China
3 Henan Engineering Technology Research Center of Archives Data Analysis and Security, Zhengzhou, China
* Corresponding Author: Jinjin Liu. Email: email
(This article belongs to the Special Issue: Deep Learning for Emotion Recognition)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.083112

Received 29 March 2026; Accepted 04 June 2026; Published online 07 July 2026

Abstract

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.

Keywords

Multimodal sentiment analysis; incomplete multimodal learning; text-centric modeling; cross-modal alignment

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