基于深度学习的文本分类综述

随着互联网的不断发展，网络上的文本数据日益增多，如果能对这些数据进行有效分类，那么更有利于从中挖掘出有价值的信息，因此文本数据的管理和整合显得十分重要。文本分类是自然语言处理任务中的一项基础性工作，主要应用于舆情检测及新闻文本分类等领域，目的是对文本资源进行整理和归类。基于深度学习的文本分类，在对文本数据处理中，表现出较好的分类效果。本文对用于文本分类的深度学习算法进行详细阐述，按照深度学习的不同算法进行分类，并分析各种算法的特点，最后对深度学习算法在文本分类领域的未来研究方向进行总结。

A Survey of Text Classification Based on Deep Learning

With the continuous development of the Internet, there is an increasing number of text data on the Internet. If these data can be effectively classified, it is more conducive to mining valuable information. Therefore, the management and integration of text data is very important. Text classification is a basic task in natural language processing tasks. It is mainly used in the fields of public opinion detection and news text classification. The purpose is to sort and classify text resources. The text classification based on deep learning shows a good classification effect in the processing of text data. The article elaborates on the deep learning algorithms used for text classification, classifies according to different deep learning algorithms, and analyzes the characteristics of various algorithms, and finally summarizes the future research directions of deep learning algorithms in the field of text classification.