@Article{cmc.2020.010172,
AUTHOR = {Hongping Wu, Yuling Liu, Jingwen Wang},
TITLE = {Review of Text Classification Methods on Deep Learning},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {63},
YEAR = {2020},
NUMBER = {3},
PAGES = {1309--1321},
URL = {http://www.techscience.com/cmc/v63n3/38877},
ISSN = {1546-2226},
ABSTRACT = {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.},
DOI = {10.32604/cmc.2020.010172}
}