Application of Multimodal Deep Learning in Sentiment Analysis for Recommendation Systems

Abstract

This paper proposes a sentiment analysis method for recommendation systems based on multimodal deep learning. In modern internet applications, the accuracy of recommendation systems and user satisfaction are crucial. Therefore, this study designs and implements an innovative multimodal deep learning model that integrates text, image, and user behavioral data for sentiment analysis tasks. Extensive experimental validation using multiple public datasets demonstrates that the proposed method not only significantly outperforms traditional approaches in accuracy but also makes substantial advancements in enhancing user satisfaction and recommendation effectiveness.