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Review of Text Classification Methods on Deep Learning

Hongping Wu1, Yuling Liu1, *, Jingwen Wang2

1College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China.
2 Department of Computer Science, Elizabethtown College, PA, 17022, USA.

* Corresponding Author: Yuling Liu. Email: email.

Computers, Materials & Continua 2020, 63(3), 1309-1321.


Text classification has always been an increasingly crucial topic in natural language processing. Traditional text classification methods based on machine learning have many disadvantages such as dimension explosion, data sparsity, limited generalization ability and so on. Based on deep learning text classification, this paper presents an extensive study on the text classification models including Convolutional Neural Network-Based (CNN-Based), Recurrent Neural Network-Based (RNN-based), Attention Mechanisms-Based and so on. Many studies have proved that text classification methods based on deep learning outperform the traditional methods when processing large-scale and complex datasets. The main reasons are text classification methods based on deep learning can avoid cumbersome feature extraction process and have higher prediction accuracy for a large set of unstructured data. In this paper, we also summarize the shortcomings of traditional text classification methods and introduce the text classification process based on deep learning including text preprocessing, distributed representation of text, text classification model construction based on deep learning and performance evaluation.


Cite This Article

H. Wu, Y. Liu and J. Wang, "Review of text classification methods on deep learning," Computers, Materials & Continua, vol. 63, no.3, pp. 1309–1321, 2020.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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