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A systematic review of text classification research based on deep learning models in Arabic language
Date
2020
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Abstract
Classifying or categorizing texts is the process by which documents are
classified into groups by subject, title, author, etc. This paper undertakes
a systematic review of the latest research in the field of the classification of
Arabic texts. Several machine learning techniques can be used for text
classification, but we have focused only on the recent trend of neural network
algorithms. In this paper, the concept of classifying texts and classification
processes are reviewed. Deep learning techniques in classification and its
type are discussed in this paper as well. Neural networks of various types,
namely, RNN, CNN, FFNN, and LSTM, are identified as the subject of
study. Through systematic study, 12 research papers related to the field of
the classification of Arabic texts using neural networks are obtained: for each
paper the methodology for each type of neural network and the accuracy
ration for each type is determined. The evaluation criteria used in
the algorithms of different neural network types and how they play a large
role in the highly accurate classification of Arabic texts are discussed.
Our results provide some findings regarding how deep learning models can
be used to improve text classification research in Arabic language.