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Sentiment analysis is rapidly advancing by utilizing various data modalities(e.g., text, image). However, most previous works relied on superficialinformation, 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 sentimentanalysis. In this paper, we proposed a plug-in framework named WisdoM, designedto leverage contextual world knowledge induced from the large vision-languagemodels (LVLMs) for enhanced multimodal sentiment analysis. WisdoM utilizes aLVLM to comprehensively analyze both images and corresponding sentences,simultaneously generating pertinent context. To reduce the noise in thecontext, we also introduce a training-free Contextual Fusion mechanism.Experimental results across diverse granularities of multimodal sentimentanalysis tasks consistently demonstrate that our approach has substantialimprovements (brings an average +1.89 F1 score among five advanced methods)over several state-of-the-art methods. Code will be released.

WisdoM: Improving Multimodal Sentiment Analysis by Fusing Contextual World Knowledge