A review of deep learning-based text sentiment analysis research

Abstract

The study of textual sentiments is a growing subfield of natural language processing. Research on deep learning-based text sentiment analysis approaches has received a lot of interest as machine learning technology has advanced. There are a variety of approaches to analyzing text for sentiment, but they may be broken down into three broad categories: those that rely on neural networks, those that introduce an attention mechanism, and those that rely on pre-trained models. In this paper, the author uses a literature review approach and CNKI as the search engine to examine previous studies on deep learning-based text sentiment analysis methods and models and to categorize the evolution of this field so as to aid in the development of similar studies in the future.