Multimodal sentiment analysis (MSA) aims to infer sentiment by jointly modeling textual, acoustic, and visual information. Although existing multimodal fusion methods achieve strong performance when all modalities are available, their reliability often decreases when one or more modality streams are absent or unevenly informative. To address this problem, this paper proposes RoME: Robust multimodal expert learning for sentiment analysis with missing modalities, a two-stage robust multimodal embedding framework for sentiment analysis under incomplete modality conditions. In the first stage, RoME refines unimodal representations using modality-specific experts with soft routing, preserving modality-dependent structure under partial observations. In the second stage, RoME constructs adaptive multimodal representations through a Cross-Modal Interaction Transformer for inter-modality modeling, a Modality Reliability Re-Weighting mechanism to regulate modality contributions, a Residual Multimodal Corrector to compensate for fusion errors, and a Cross-Modal Denoising Autoencoder that provides reconstruction-driven regularization. Extensive experiments on the CMU-MOSI and CMU-MOSEI benchmarks demonstrate that RoME achieves strong performance across full-modality, partial-modality, and single-modality settings. Ablation studies, modality contribution analysis, and cross-validation results further confirm the effectiveness and stability of the proposed framework under controlled missing-modality conditions. Our code is available at https://github.com/atifmallo/RoME .
Hussain et al. (Fri,) studied this question.