The Research and Preprocessing Results of The End-to-End Multimodal Sentiment Analysis System

Abstract

With the rapid development of artificial intelligence technology, the application of sentiment analysis in areas such as social media monitoring, customer service, and intelligent assistants is gradually increasing. Traditional sentiment analysis usually relies on unimodal analysis, which has certain limitations. Therefore, end-to-end multimodal sentiment analysis systems have emerged. This paper combines several typical multimodal sentiment analysis systems, with the Tension-Tension Experimental Engineering (TTEE) model for efficient computation and reasoning, and incorporates weighted vector fusion and cross-modal attention mechanisms to improve the accuracy of sentiment analysis. The system uses Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to extract high-level features from text, speech, and images, enabling deep fusion of multimodal data. Additionally, the system adopts the Multi-Task Multi-View Network (MTMVN) architecture for multi-task and multi-perspective learning, thereby enhancing the robustness and accuracy of the sentiment analysis model. Compared to traditional unimodal analysis methods, multimodal sentiment analysis systems demonstrate higher accuracy and stability across multiple sentiment analysis tasks.