WisdoM: Improving Multimodal Sentiment Analysis by Fusing Contextual World Knowledge

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. In this paper, we proposed a plug-in framework named WisdoM, designed to leverage contextual world knowledge induced from the large vision-language models (LVLMs) for enhanced multimodal sentiment analysis. WisdoM utilizes a LVLM to comprehensively analyze both images and corresponding sentences, simultaneously generating pertinent context. To reduce the noise in the context, we also introduce a training-free Contextual Fusion mechanism. Experimental results across diverse granularities of multimodal sentiment analysis tasks consistently demonstrate that our approach has substantial improvements (brings an average +1.89 F1 score among five advanced methods) over several state-of-the-art methods. Code will be released.