Review of Multimodal Sentiment Analysis Techniques

By integrating multimodal data such as text, image, audio and video, the multimodal emotion analysis technology has significantly improved the accuracy and robustness of emotion recognition, and has become a research hotspot in the field of artificial intelligence. This paper summarizes the research background, main research content and application of multimodal emotion analysis, and discusses the future development trend. This paper emphasizes the importance of emotion analysis in understanding user intention, improving user experience and optimizing decision support system, and points out the limitations of traditional unimodal emotion analysis, and leads to the necessity of multimodal emotion analysis. It systematically reviews the key technologies and methods of multimodal emotion analysis, including data fusion strategies, deep learning models, attention mechanisms, and cross-modal association learning, and analyzes the challenges in current research, such as data set imbalance, inter-modal inconsistency, and real-time requirements. This paper provides a comprehensive research idea for subsequent researchers, including constructing high-quality datasets, optimizing algorithms, expanding application scenarios, strengthening interdisciplinary cooperation, and emphasizing ethics and privacy protection, to promote the further development of multimodal sentiment analysis technology in the field of artificial intelligence. With the improvement of computing power and the innovation of algorithms, multimodal emotion analysis technology will be even more important in the field of artificial intelligence in the future.