Research on Visual and Text Information Fusion Algorithm in Multimodal Sentiment Analysis

This paper aims to explore a new visual and text information fusion algorithm, which can effectively improve the accuracy and efficiency of sentiment analysis by combining the advantages of the trusted fine-grained alignment model and the Faster R-CNN algorithm. First, this paper proposes a visual object detection mechanism based on Faster R-CNN algorithm, which can accurately identify and locate key emotion expression elements in images. Then, the alignment model is used to associate the visual object with the corresponding text description, and the cross-modal information fusion is realized. This fusion not only considers the explicit emotional cues of visual information, but also digs deeply into the implicit emotional tendencies in the text, thus providing more comprehensive and detailed emotional analysis results. In order to verify the effectiveness of the proposed algorithm, a series of simulation experiments are designed and tested on several public data sets. The experimental results show that compared with the traditional single-modal analysis method, the fusion algorithm proposed in this paper significantly improves the performance of emotion classification tasks, especially when dealing with complex emotion expression and ambiguous text, showing stronger robustness and adaptability. The research results of this paper not only provide a new technical path in the field of multi-modal sentiment analysis, but also provide more reliable technical support for related application scenarios such as product review analysis and social media public opinion monitoring.