# Text-oriented Modality Reinforcement Network for Multimodal Sentiment Analysis from Unaligned Multimodal Sequences

arXiv: https://arxiv.org/abs/2307.13205 · HTML: https://mlsanity.com/paper/2307.13205/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2023.

## Built on by (most cited first, 13 of 13)

- [Correlation-Decoupled Knowledge Distillation for Multimodal Sentiment Analysis with Incomplete Modalities](https://mlsanity.com/paper/2404.16456/) (2024)
- [Towards Multimodal Sentiment Analysis Debiasing via Bias Purification](https://mlsanity.com/paper/2403.05023/) (2024)
- [DLF: Disentangled-Language-Focused Multimodal Sentiment Analysis](https://mlsanity.com/paper/2412.12225/) (2024)
- [Robust Emotion Recognition in Context Debiasing](https://mlsanity.com/paper/2403.05963/) (2024)
- [It's Never Too Late: Fusing Acoustic Information into Large Language Models for Automatic Speech Recognition](https://mlsanity.com/paper/2402.05457/) (2024)
- [MISS: A Generative Pretraining and Finetuning Approach for Med-VQA](https://mlsanity.com/paper/2401.05163/) (2024)
- [Efficiency in Focus: LayerNorm as a Catalyst for Fine-tuning Medical Visual Language Pre-trained Models](https://mlsanity.com/paper/2404.16385/) (2024)
- [Towards Context-Aware Emotion Recognition Debiasing from a Causal Demystification Perspective via De-confounded Training](https://mlsanity.com/paper/2407.04963/) (2024)
- [Asynchronous Multimodal Video Sequence Fusion via Learning Modality-Exclusive and -Agnostic Representations](https://mlsanity.com/paper/2407.04955/) (2024)
- [Improving Multimodal Sentiment Analysis via Modality Optimization and Dynamic Primary Modality Selection](https://mlsanity.com/paper/2511.06328/) (2025)
- [TCAN: Text-oriented Cross Attention Network for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2404.04545/) (2024)
- [Skip and Skip: Segmenting Medical Images with Prompts](https://mlsanity.com/paper/2406.14958/) (2024)
- [Vision-Guided Text Prompt Tuning for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2609.06497/) (2026)

## Abstract (arXiv)

Multimodal Sentiment Analysis (MSA) aims to mine sentiment information from text, visual, and acoustic modalities. Previous works have focused on representation learning and feature fusion strategies. However, most of these efforts ignored the disparity in the semantic richness of different modalities and treated each modality in the same manner. That may lead to strong modalities being neglected and weak modalities being overvalued. Motivated by these observations, we propose a Text-oriented Modality Reinforcement Network (TMRN), which focuses on the dominance of the text modality in MSA. More specifically, we design a Text-Centered Cross-modal Attention (TCCA) module to make full interaction for text/acoustic and text/visual pairs, and a Text-Gated Self-Attention (TGSA) module to guide the self-reinforcement of the other two modalities. Furthermore, we present an adaptive fusion mechanism to decide the proportion of different modalities involved in the fusion process. Finally, we combine the feature matrices into vectors to get the final representation for the downstream tasks. Experimental results show that our TMRN outperforms the state-of-the-art methods on two MSA benchmarks.