Transferring Personality Knowledge to Multimodal Sentiment Analysis

Multimodal sentiment analysis systems have achieved remarkable success. However, significant challenges persist in tailoring sentiment analysis to individualized needs. Recognizing the pivotal role of personality traits in shaping emotional expression-where distinct personalities manifest emotions with varying styles and intensities-we introduce a novel enhancement to MSA. Our approach integrates personality traits into the sentiment recognition framework, refining its ability to discern nuanced emotional variances among individuals. This paper presents the TPK (Transferring Personality Knowledge) framework, is a pioneering solution designed to bolster the personalization and precision of sentiment analysis. The TPK frame-work is distinguished by its dual-pronged strategy: firstly, the seamless transfer of personality knowledge to enrich sentiment learning, thereby capturing personalized emotional signals; secondly, the synergistic fusion of multimodal data to amplify the depth of emotional expression. We substantiate the TPK framework's efficacy through rigorous experimentation on two renowned public datasets.