Compared to traditional sentiment analysis, which only considers text,
multimodal sentiment analysis needs to consider emotional signals from
multimodal sources simultaneously and is therefore more consistent with the way
how humans process sentiment in real-world scenarios. It involves processing
emotional information from various sources such as natural language, images,
videos, audio, physiological signals, etc. However, although other modalities
also contain diverse emotional cues, natural language usually contains richer
contextual information and therefore always occupies a crucial position in
multimodal sentiment analysis. The emergence of ChatGPT has opened up immense
potential for applying large language models (LLMs) to text-centric multimodal
tasks. However, it is still unclear how existing LLMs can adapt better to
text-centric multimodal sentiment analysis tasks. This survey aims to (1)
present a comprehensive review of recent research in text-centric multimodal
sentiment analysis tasks, (2) examine the potential of LLMs for text-centric
multimodal sentiment analysis, outlining their approaches, advantages, and
limitations, (3) summarize the application scenarios of LLM-based multimodal
sentiment analysis technology, and (4) explore the challenges and potential
research directions for multimodal sentiment analysis in the future.

与传统情感分析相比，多模态情感分析需要同时考虑来自多模态源的情感信号，它更符合人类在现实场景中处理情感的方式。本文综述了近期文本为中心的多模态情感分析任务中的研究，审视了大型语言模型在文本为中心的多模态情感分析中的潜力、方法、优势和局限性，总结了基于大型语言模型的多模态情感分析技术的应用场景，并探讨了未来多模态情感分析的挑战和潜在研究方向。