# Robust-MSA: Understanding the Impact of Modality Noise on Multimodal Sentiment Analysis

arXiv: https://arxiv.org/abs/2211.13484 · HTML: https://mlsanity.com/paper/2211.13484/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2022.

## Built on by (most cited first, 5 of 5)

- [Recent Advances in Multimodal Affective Computing: An NLP Perspective](https://mlsanity.com/paper/2409.07388/) (2024)
- [TRAP: Targeted Redirecting of Agentic Preferences](https://mlsanity.com/paper/2505.23518/) (2025)
- [Addressing Missing and Noisy Modalities in One Solution: Unified Modality-Quality Framework for Low-quality Multimodal Data](https://mlsanity.com/paper/2603.02695/) (2026)
- [Controlling Decision Drift in Multimodal Sentiment Analysis with Missing Modalities](https://mlsanity.com/paper/2605.16889/) (2026)
- [Reliability-aware Cross-sample Enhancement for Robust Multimodal Sentiment Analysis](https://mlsanity.com/paper/2609.30470/) (2026)

## Abstract (arXiv)

Improving model robustness against potential modality noise, as an essential step for adapting multimodal models to real-world applications, has received increasing attention among researchers. For Multimodal Sentiment Analysis (MSA), there is also a debate on whether multimodal models are more effective against noisy features than unimodal ones. Stressing on intuitive illustration and in-depth analysis of these concerns, we present Robust-MSA, an interactive platform that visualizes the impact of modality noise as well as simple defence methods to help researchers know better about how their models perform with imperfect real-world data.