Multimodal sentiment analysis (MSA) seeks to understand user-generated videos by combining signals from language, audio, and vision. A new paper on arXiv argues that many existing methods make a fundamental mistake: they treat these modalities as symmetric, even though language typically carries the most semantic weight.
The authors propose a framework that explicitly anchors the model in text, using modality experts that are guided by an uncertainty-aware routing mechanism. This is intended to let the model rely more on language when it is informative, while still leveraging other modalities when they add clarity.
The paper is available as arXiv:2610.27571. As an abstract, it does not yet provide experimental details, so the claimed benefits remain to be evaluated. The core idea, however, challenges a common assumption in the field and suggests a direction for more robust multimodal sentiment systems.