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This article introduces a novel approach, PSA-MF, for multimodal sentiment analysis. The core idea is to align personality and sentiment information at multiple levels of fusion. This suggests a focus on improving the accuracy and robustness of sentiment analysis by considering both the content and the underlying personality traits of the source. The use of 'multi-level fusion' indicates a sophisticated architecture likely involving different stages of data processing and integration.
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