Research and Application of Key Techniques of Multimodal Sentiment Analysis

Multimodal sentiment analysis is an important research direction in the field of natural language processing and artificial intelligence, aiming to improve the accuracy of emotion recognition by using multimodal data such as text, speech and image. Under the background of today's big data era, the technology of multi-modal sentiment analysis is becoming more and more important, and it is also widely used in all aspects of people's lives. This paper first introduces the basic concept and research background of multimodal sentiment analysis. Then, a series of key technologies such as feature fusion, feature extraction, sentiment analysis, data fusion and deep learning are discussed in the current situation. Next, it analyzes the typical applications of multimodal sentiment analysis in the fields of social media and human-computer interaction. At the same time, it also analyzes and evaluates the future development direction of multimodal sentiment analysis since the limitations of the current application of the technology, and provides certain ideas for the future development of multimodal sentiment analysis.