Research on web text sentiment analysis and application based on deep learning

In recent years, with the rapid development of the Internet, a large amount of user-generated content has been generated on social media, forums and various online platforms. How to efficiently and accurately analyze the sentiment orientation of these texts has become an important research direction in the field of natural language processing. Traditional sentiment analysis methods rely on manually constructed features, which are difficult to deal with complex semantic expressions. However, deep learning methods have made breakthroughs in the field of web text sentiment analysis by virtue of their powerful automatic feature extraction capabilities. This paper reviews the network text sentiment analysis method based on deep learning, including cyclic convolution neural network training models of neural network, the attention mechanism and application, and discusses its social media analysis, product evaluation, public opinion monitoring, the application of intelligent customer service and bot, etc. The results of this paper show that the deep learning method in accuracy and efficiency of sentiment analysis is superior to the traditional method, especially in dealing with long text and complicated emotional expression. At the same time, with the introduction of pre-trained models, the effect of sentiment analysis is further improved, which provides more accurate technical support for various practical applications. Finally, this paper proposes that although deep learning methods have made significant progress in the field of sentiment analysis, there are still some challenges in data quality, model interpretability, and cross-language applications.