A Review of Methods and Applications of Sentiment Analysis Based on Deep Learning Models

Sentiment analysis refers to the information processing of people’s views, evaluations, or attitudes towards certain substances or their properties to extract sentiments and opinions. With the development of deep learning technology and the innovation of neural network structure, the focus of sentiment analysis methods has gradually shifted from traditional machine learning models to deep learning models and has gradually become popular, making sentiment analysis gradually applied to the market economy, politics and different fields, such as science, health or history. It can promote the development of society and generate new research directions. This review is divided into three parts: (1) Introduce the definition of sentiment analysis and the pre-processing of sentiment analysis. (2) Explain and discuss the models and methods of sentiment analysis and evaluate the ability of the model. (3) Summarize the advantages and disadvantages of deep learning models and propose sentiment a new direction for analysis.