Overview of Deep Learning Methods for Sentiment Analysis

Abstract

With the development and improvement of Web 2.0, user-driven Internet products are rapidly increasing, generating huge amounts of data, especially text data. Sentiment analysis can extract and analyse sentiment tendencies from text data, and is one of the most valuable research directions in natural language processing. In recent years, deep learning has been widely applied to sentiment analysis tasks, using advanced model architectures to achieve better results than ever before. In this paper, we review various deep learning methods and their extensions for sentiment analysis tasks in recent years synthetically, especially the Transformer models and Pre-trained models. In addition, the advantages and disadvantages of these models as well as the limitations of their use are illustrated. Finally, the paper summarized the main challenges of the current sentiment analysis tasks and possible future research directions.