# Multimodal Sentiment Analysis: A Survey

arXiv: https://arxiv.org/abs/2305.07611 · HTML: https://mlsanity.com/paper/2305.07611/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 80 references that are in the map.
- First posted on arXiv 12 May 2023.

## Builds on (most cited first, 16 of 16)

- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2005.03545/) (2020)
- [MOSI: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videos](https://mlsanity.com/paper/1606.06259/) (2016)
- [Learning Modality-Specific Representations with Self-Supervised Multi-Task Learning for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2102.04830/) (2021)
- [Found in Translation: Learning Robust Joint Representations by Cyclic Translations Between Modalities](https://mlsanity.com/paper/1812.07809/) (2018)
- [Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors](https://mlsanity.com/paper/1811.09362/) (2018)
- [Multimodal Language Analysis with Recurrent Multistage Fusion](https://mlsanity.com/paper/1808.03920/) (2018)
- [Jointly Fine-Tuning "BERT-like" Self Supervised Models to Improve Multimodal Speech Emotion Recognition](https://mlsanity.com/paper/2008.06682/) (2020)
- [Select-Additive Learning: Improving Generalization in Multimodal Sentiment Analysis](https://mlsanity.com/paper/1609.05244/) (2016)
- [The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvements](https://mlsanity.com/paper/2101.06053/) (2021)
- [Vision Transformers in Medical Computer Vision -- A Contemplative Retrospection](https://mlsanity.com/paper/2203.15269/) (2022)
- [Hybrid Multimodal Feature Extraction, Mining and Fusion for Sentiment Analysis](https://mlsanity.com/paper/2208.03051/) (2022)
- [Complementary Fusion of Multi-Features and Multi-Modalities in Sentiment Analysis](https://mlsanity.com/paper/1904.08138/) (2019)
- [Multimodal Emotion Classification](https://mlsanity.com/paper/1903.12520/) (2019)
- [An AutoML-based Approach to Multimodal Image Sentiment Analysis](https://mlsanity.com/paper/2102.08092/) (2021)

## Built on by (most cited first, 12 of 12)

- [LR-FPN: Enhancing Remote Sensing Object Detection with Location Refined Feature Pyramid Network](https://mlsanity.com/paper/2404.01614/) (2024)
- [Shared and Private Information Learning in Multimodal Sentiment Analysis with Deep Modal Alignment and Self-supervised Multi-Task Learning](https://mlsanity.com/paper/2305.08473/) (2023)
- [Part-Attention Based Model Make Occluded Person Re-Identification Stronger](https://mlsanity.com/paper/2404.03443/) (2024)
- [DRIVE: Dependable Robust Interpretable Visionary Ensemble Framework in Autonomous Driving](https://mlsanity.com/paper/2409.10330/) (2024)
- [Towards Explainable Fusion and Balanced Learning in Multimodal Sentiment Analysis](https://mlsanity.com/paper/2504.12151/) (2025)
- [PEPL: Precision-Enhanced Pseudo-Labeling for Fine-Grained Image Classification in Semi-Supervised Learning](https://mlsanity.com/paper/2409.03192/) (2024)
- [AdaFSNet: Time Series Classification Based on Convolutional Network with a Adaptive and Effective Kernel Size Configuration](https://mlsanity.com/paper/2404.18246/) (2024)
- [Improving the Classification Effect of Clinical Images of Diseases for Multi-Source Privacy Protection](https://mlsanity.com/paper/2408.13038/) (2024)
- [S2RC-GCN: A Spatial-Spectral Reliable Contrastive Graph Convolutional Network for Complex Land Cover Classification Using Hyperspectral Images](https://mlsanity.com/paper/2404.00964/) (2024)
- [Differential Attention for Multimodal Crisis Event Analysis](https://mlsanity.com/paper/2507.05165/) (2025)
- [Multi-Modal Sentiment Analysis with Dynamic Attention Fusion](https://mlsanity.com/paper/2509.22729/) (2025)
- [Unveiling Public Opinion: A Study of Sentiment Analysis Using LSTM and Traditional Models](https://mlsanity.com/paper/2607.07772/) (2026)

## Abstract (arXiv)

Multimodal sentiment analysis has become an important research area in the field of artificial intelligence. With the latest advances in deep learning, this technology has reached new heights. It has great potential for both application and research, making it a popular research topic. This review provides an overview of the definition, background, and development of multimodal sentiment analysis. It also covers recent datasets and advanced models, emphasizing the challenges and future prospects of this technology. Finally, it looks ahead to future research directions. It should be noted that this review provides constructive suggestions for promising research directions and building better performing multimodal sentiment analysis models, which can help researchers in this field.