# Enriching Multimodal Sentiment Analysis through Textual Emotional Descriptions of Visual-Audio Content

arXiv: https://arxiv.org/abs/2412.10460 · HTML: https://mlsanity.com/paper/2412.10460/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 41 references that are in the map.
- First posted on arXiv 12 Dec 2024.

## Builds on (most cited first, 1 of 1)

- [MOSI: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videos](https://mlsanity.com/paper/1606.06259/) (2016)

## Built on by (most cited first, 25 of 25)

- [Tri-Subspaces Disentanglement for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2602.19585/) (2026)
- [CLCR: Cross-Level Semantic Collaborative Representation for Multimodal Learning](https://mlsanity.com/paper/2602.19605/) (2026)
- [Temporal-Spatial Decouple before Act: Disentangled Representation Learning for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2601.13659/) (2026)
- [Group Cognition Learning: Making Everything Better Through Governed Two-Stage Agents Collaboration](https://mlsanity.com/paper/2605.00370/) (2026)
- [Disentangling Bias by Modeling Intra- and Inter-modal Causal Attention for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2508.04999/) (2025)
- [Towards Minimal Causal Representations for Human Multimodal Language Understanding](https://mlsanity.com/paper/2509.21805/) (2025)
- [CaReFlow: Cyclic Adaptive Rectified Flow for Multimodal Fusion](https://mlsanity.com/paper/2602.19140/) (2026)
- [Uncertainty-Aware Collaborative System of Large and Small Models for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2509.04459/) (2025)
- [Decoupled Hierarchical Distillation for Multimodal Emotion Recognition](https://mlsanity.com/paper/2602.04260/) (2026)
- [Addressing Missing and Noisy Modalities in One Solution: Unified Modality-Quality Framework for Low-quality Multimodal Data](https://mlsanity.com/paper/2603.02695/) (2026)
- [Progressive Representation Learning for Multimodal Sentiment Analysis with Incomplete Modalities](https://mlsanity.com/paper/2603.09111/) (2026)
- [Enhance-then-Balance Modality Collaboration for Robust Multimodal Sentiment Analysis](https://mlsanity.com/paper/2604.12518/) (2026)
- [Active Multimodal Distillation for Few-shot Action Recognition](https://mlsanity.com/paper/2506.13322/) (2025)
- [QiNN-QJ: A Quantum-inspired Neural Network with Quantum Jump for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2510.27091/) (2025)
- [Text-Routed Sparse Mixture-of-Experts Model with Explanation and Temporal Alignment for Multi-Modal Sentiment Analysis](https://mlsanity.com/paper/2512.22741/) (2025)
- [GRCF: Two-Stage Groupwise Ranking and Calibration Framework for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2601.09606/) (2026)
- [GRAMformer: Any-Order Modality Interactions via Volumetric Multimodal Cross-Attention](https://mlsanity.com/paper/2606.06249/) (2026)
- [Audio Sentiment Analysis via Distillation and Cross-Modal Integration of Generated Multilingual Transcripts](https://mlsanity.com/paper/2607.06611/) (2026)
- [Segregate, Refine, Integrate: Decomposing Multimodal Fusion for Sentiment Analysis](https://mlsanity.com/paper/2607.12686/) (2026)
- [ProFocus: Interpreting Affective Experience in Artistic Images with Progressive Visual Focusing](https://mlsanity.com/paper/2608.13974/) (2026)
- [Multi-Granularity Sentiment Integration for LLM-Based Multimodal Sentiment Analysis](https://mlsanity.com/paper/2608.16201/) (2026)
- [Vision-Guided Text Prompt Tuning for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2609.06497/) (2026)
- [BiCFlow-MER: Orchestrating Discriminative and Generative Multimodal Emotion Recognition via Conditional Transport](https://mlsanity.com/paper/2609.27615/) (2026)
- [SemMSA: Latent Semantic-Aided Robust Multimodal Sentiment Analysis with Incomplete Data](https://mlsanity.com/paper/2609.30238/) (2026)
- [Reliability-aware Cross-sample Enhancement for Robust Multimodal Sentiment Analysis](https://mlsanity.com/paper/2609.30470/) (2026)

## Abstract (arXiv)

Multimodal Sentiment Analysis (MSA) stands as a critical research frontier, seeking to comprehensively unravel human emotions by amalgamating text, audio, and visual data. Yet, discerning subtle emotional nuances within audio and video expressions poses a formidable challenge, particularly when emotional polarities across various segments appear similar. In this paper, our objective is to spotlight emotion-relevant attributes of audio and visual modalities to facilitate multimodal fusion in the context of nuanced emotional shifts in visual-audio scenarios. To this end, we introduce DEVA, a progressive fusion framework founded on textual sentiment descriptions aimed at accentuating emotional features of visual-audio content. DEVA employs an Emotional Description Generator (EDG) to transmute raw audio and visual data into textualized sentiment descriptions, thereby amplifying their emotional characteristics. These descriptions are then integrated with the source data to yield richer, enhanced features. Furthermore, DEVA incorporates the Text-guided Progressive Fusion Module (TPF), leveraging varying levels of text as a core modality guide. This module progressively fuses visual-audio minor modalities to alleviate disparities between text and visual-audio modalities. Experimental results on widely used sentiment analysis benchmark datasets, including MOSI, MOSEI, and CH-SIMS, underscore significant enhancements compared to state-of-the-art models. Moreover, fine-grained emotion experiments corroborate the robust sensitivity of DEVA to subtle emotional variations.