# UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition

arXiv: https://arxiv.org/abs/2211.11256 · HTML: https://mlsanity.com/paper/2211.11256/

## Key facts

- Cited by 61 papers in the map (data through 14 Oct 2026).
- Builds on 53 of its 91 references that are in the map.
- First posted on arXiv 21 Nov 2022.

## Builds on (most cited first, 30 of 53)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [Multimodal Transformer for Unaligned Multimodal Language Sequences](https://mlsanity.com/paper/1906.00295/) (2019)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [Tensor Fusion Network for Multimodal Sentiment Analysis](https://mlsanity.com/paper/1707.07250/) (2017)
- [VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts](https://mlsanity.com/paper/2111.02358/) (2021)
- [Efficient Low-rank Multimodal Fusion with Modality-Specific Factors](https://mlsanity.com/paper/1806.00064/) (2018)
- [MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2005.03545/) (2020)
- [UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models](https://mlsanity.com/paper/2201.05966/) (2022)
- [DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation](https://mlsanity.com/paper/1908.11540/) (2019)
- [Learning Modality-Specific Representations with Self-Supervised Multi-Task Learning for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2102.04830/) (2021)
- [Learning Factorized Multimodal Representations](https://mlsanity.com/paper/1806.06176/) (2018)
- [A Unified Generative Framework for Various NER Subtasks](https://mlsanity.com/paper/2106.01223/) (2021)
- [COSMIC: COmmonSense knowledge for eMotion Identification in Conversations](https://mlsanity.com/paper/2010.02795/) (2020)
- [Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2109.00412/) (2021)
- [GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-Supervised Learning and Explicit Policy Injection](https://mlsanity.com/paper/2111.14592/) (2021)
- [MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversation](https://mlsanity.com/paper/2107.06779/) (2021)
- [Directed Acyclic Graph Network for Conversational Emotion Recognition](https://mlsanity.com/paper/2105.12907/) (2021)
- [A Unified Generative Framework for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2106.04300/) (2021)
- [Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors](https://mlsanity.com/paper/1811.09362/) (2018)
- [Learning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysis](https://mlsanity.com/paper/1911.05544/) (2019)
- [Discovering New Intents with Deep Aligned Clustering](https://mlsanity.com/paper/2012.08987/) (2020)
- [Deep Unknown Intent Detection with Margin Loss](https://mlsanity.com/paper/1906.00434/) (2019)

## Built on by (most cited first, 30 of 61)

- [MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct](https://mlsanity.com/paper/2409.05840/) (2024)
- [DialogueLLM: Context and Emotion Knowledge-Tuned Large Language Models for Emotion Recognition in Conversations](https://mlsanity.com/paper/2310.11374/) (2023)
- [Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspective](https://mlsanity.com/paper/2408.04638/) (2024)
- [Multimodal Learning Without Labeled Multimodal Data: Guarantees and Applications](https://mlsanity.com/paper/2306.04539/) (2023)
- [Modeling Multimodal Social Interactions: New Challenges and Baselines with Densely Aligned Representations](https://mlsanity.com/paper/2403.02090/) (2024)
- [Neuro-Inspired Information-Theoretic Hierarchical Perception for Multimodal Learning](https://mlsanity.com/paper/2404.09403/) (2024)
- [UniSA: Unified Generative Framework for Sentiment Analysis](https://mlsanity.com/paper/2309.01339/) (2023)
- [On Expert Estimation in Hierarchical Mixture of Experts: Beyond Softmax Gating Functions](https://mlsanity.com/paper/2410.02935/) (2024)
- [CHAMPAGNE: Learning Real-world Conversation from Large-Scale Web Videos](https://mlsanity.com/paper/2303.09713/) (2023)
- [CFN-ESA: A Cross-Modal Fusion Network with Emotion-Shift Awareness for Dialogue Emotion Recognition](https://mlsanity.com/paper/2307.15432/) (2023)
- [Speech-Text Dialog Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignment](https://mlsanity.com/paper/2305.11579/) (2023)
- [Revisiting Disentanglement and Fusion on Modality and Context in Conversational Multimodal Emotion Recognition](https://mlsanity.com/paper/2308.04502/) (2023)
- [PanoSent: A Panoptic Sextuple Extraction Benchmark for Multimodal Conversational Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2408.09481/) (2024)
- [Recent Advances in Multimodal Affective Computing: An NLP Perspective](https://mlsanity.com/paper/2409.07388/) (2024)
- [Unsupervised Dialogue Topic Segmentation with Topic-aware Utterance Representation](https://mlsanity.com/paper/2305.02747/) (2023)
- [BiosERC: Integrating Biography Speakers Supported by LLMs for ERC Tasks](https://mlsanity.com/paper/2407.04279/) (2024)
- [Can Large Language Models Help Multimodal Language Analysis? MMLA: A Comprehensive Benchmark](https://mlsanity.com/paper/2504.16427/) (2025)
- [Empathetic Response Generation via Emotion Cause Transition Graph](https://mlsanity.com/paper/2302.11787/) (2023)
- [VideoAdviser: Video Knowledge Distillation for Multimodal Transfer Learning](https://mlsanity.com/paper/2309.15494/) (2023)
- [Missing-by-Design: Certifiable Modality Deletion for Revocable Multimodal Sentiment Analysis](https://mlsanity.com/paper/2602.16144/) (2026)
- [Self-Explanation Prompting Improves Dialogue Understanding in Large Language Models](https://mlsanity.com/paper/2309.12940/) (2023)
- [Emotion Prediction Oriented method with Multiple Supervisions for Emotion-Cause Pair Extraction](https://mlsanity.com/paper/2302.12417/) (2023)
- [Triple Disentangled Representation Learning for Multimodal Affective Analysis](https://mlsanity.com/paper/2401.16119/) (2024)
- [End-to-end Semantic-centric Video-based Multimodal Affective Computing](https://mlsanity.com/paper/2408.07694/) (2024)
- [RCLMuFN: Relational Context Learning and Multiplex Fusion Network for Multimodal Sarcasm Detection](https://mlsanity.com/paper/2412.13008/) (2024)
- [CyIN: Cyclic Informative Latent Space for Bridging Complete and Incomplete Multimodal Learning](https://mlsanity.com/paper/2602.04920/) (2026)
- [SeqAug: Sequential Feature Resampling as a modality agnostic augmentation method](https://mlsanity.com/paper/2305.01954/) (2023)
- [Hierarchical Audio-Visual Information Fusion with Multi-label Joint Decoding for MER 2023](https://mlsanity.com/paper/2309.07925/) (2023)
- [EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding](https://mlsanity.com/paper/2412.08049/) (2024)
- [Meta-Learn Unimodal Signals with Weak Supervision for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2408.16029/) (2024)

## Abstract (arXiv)

Multimodal sentiment analysis (MSA) and emotion recognition in conversation (ERC) are key research topics for computers to understand human behaviors. From a psychological perspective, emotions are the expression of affect or feelings during a short period, while sentiments are formed and held for a longer period. However, most existing works study sentiment and emotion separately and do not fully exploit the complementary knowledge behind the two. In this paper, we propose a multimodal sentiment knowledge-sharing framework (UniMSE) that unifies MSA and ERC tasks from features, labels, and models. We perform modality fusion at the syntactic and semantic levels and introduce contrastive learning between modalities and samples to better capture the difference and consistency between sentiments and emotions. Experiments on four public benchmark datasets, MOSI, MOSEI, MELD, and IEMOCAP, demonstrate the effectiveness of the proposed method and achieve consistent improvements compared with state-of-the-art methods.