  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.
