Research on text sentiment analysis in natural language processing

Text sentiment analysis in natural language processing is an important means of understanding and interpreting human emotions in texts. It plays an important role in various applications, such as customer feedback analysis and social media monitoring. The topic of this article is research on text sentiment analysis in natural language processing. Four text sentiment analysis methods are reviewed: the method based on sentiment dictionary, the method based on traditional machine learning, the method based on deep learning, and the method based on hybrid method. By citing the literature on four methods, the paper analyzing the definition, advantages, disadvantages, and performance of each method in detail. The research results show that text sentiment analysis is mainly the method based on deep learning and the hybrid method. The conclusion highlights the need for continuing research to address the limitation of existing methods and improve the accuracy and applicability of sentiment analysis in different scenarios.