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Kim, Y. (2014) Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, 1746-1751. arXiv: 1408.5882 https://doi.org/10.3115/v1/D14-1181

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• TITLE: Review of Text Classification in Deep Learning

  AUTHORS: Qi Wang, Wenling Li, Zhezhi Jin

  KEYWORDS: Text Classification, Word-Embedding, Convolution Neural Network, Long and Short Term Memory Network

  JOURNAL NAME: Open Access Library Journal, Vol.8 No.3, March 25, 2021

  ABSTRACT: Text classification is an important research content in natural language processing. Compared with traditional manual processing, text classification based on deep learning improves both efficiency and accuracy. However, in the learning process, the content involved is very large and complex. In order to facilitate the research of more scholars, this paper summarizes the text classification of deep learning. The first part of this paper introduces the preprocessing of text classification. The second part introduces several feasible methods for deep learning text classification in detail. The third part introduces the test method of the model. The fourth part summarizes and analyzes the advantages and disadvantages of several methods to lay a foundation for further research.