Text Classification Using Deep Learning Methods

Text classification is a fundamental task in multiple practical scenarios of natural language processing (NLP). During the last few decades, many text classification methods based on deep learning (DL) models have been proposed and adopted in various fields. This article provides an overview description of the mainstream deep learning approaches that are applied in text classification in recent years, including recurrent neural networks (RNN), convolutional neural networks (CNN), the attention mechanism, transformer based pre-trained models, and graph neural networks (GNN). Moreover, in order to give more intuitive comparison, the most popular datasets that are used to evaluate the above models, as well as their respective performance, are also analyzed. In the end, the potential research directions in the future are discussed.