  Sentiment analysis is rapidly advancing by utilizing various data modalities
(e.g., text, image). However, most previous works relied on superficial
information, neglecting the incorporation of contextual world knowledge (e.g.,
background information derived from but beyond the given image and text pairs)
and thereby restricting their ability to achieve better multimodal sentiment
analysis (MSA). In this paper, we proposed a plug-in framework named WisdoM, to
leverage the contextual world knowledge induced from the large vision-language
models (LVLMs) for enhanced MSA. WisdoM utilizes LVLMs to comprehensively
analyze both images and corresponding texts, simultaneously generating
pertinent context. To reduce the noise in the context, we also introduce a
training-free contextual fusion mechanism. Experiments across diverse
granularities of MSA tasks consistently demonstrate that our approach has
substantial improvements (brings an average +1.96% F1 score among five advanced
methods) over several state-of-the-art methods.
