A Review of Multimodal Sentiment Analysis: Modal Fusion and Representation
Hanxue Fu, Huimin Lu · 2024
With the continuous advancement of multimedia technology, various modalities of data such as video, text, and audio are becoming increasingly abundant. Analyzing the unified emotional expression behind this diverse set of modalities has become a crucial research area. The rapid development of deep learning technology has been applied extensively across various domains. This paper delves into various deep learning-based multimodal sentiment analysis methods, focusing on two aspects: the fusion and representation of different modalities of data. Additionally, it outlines commonly used datasets, identifies potential challenges in the field, and provides a comprehensive review. This review is significant for deepening the understanding of multimodal sentiment analysis, summarizing previous work, and inspiring future research initiatives.