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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

A Closer Look at Arabic Text Classification

Author 1: Mohammad A R Abdeen
Author 2: Sami AlBouq
Author 3: Ahmed Elmahalawy
Author 4: Sara Shehata

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2019.0101189

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 11, 2019.

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Abstract: The world has witnessed an information explosion in the past two decades. Electronic devices are now available in many varieties such as PCs, Laptops, book readers, mobile devices and with relatively affordable prices. This and the ubiquitous use of software applications such as social media and cloud applications, and the increasing trend towards digitalization, the amount of information on the global cloud has surged to an unprecedented level. Therefore, a dire need exists in order to mine this massively large amount of data and produce meaningful information. Text Classification is one of the known and well established data mining techniques that has been used and reported in the literature. Text classification methods include statistical and machine learning algorithms such as Naive Baysian, Support Vector Machines and others have widely been used. Many works have been reported regarding text classification of various languages including English, Chinese, Russian, and many others. Arabic is the fifth most spoken language in the world. There has been many works in the literature for Arabic text classification. However, and to the best of our knowledge, there is no recent work that presents a good, critical and comprehensive survey of the Arabic text classification for the past two decades. The aim of this paper is to present a concise and yet comprehensive review of the Arabic text classification. We have covered over 50 research papers covering the past two decades (2000 - 2019). The main focus of this paper is to address the following issues: 1) The techniques reported in the literature including. 2) New Techniques. 3) Most claimed efficient technique. 4) Datasets used and which ones are most popular. 5) Which feature selection techniques are used? 6) Popular classes/categories used. 7) Effect of stemming techniques on classification results.

Keywords: Arabic text classification; support vector machines; k-NN; Naive Bayesian; decision trees; C4.5; maximum entropy; feature selection; Arabic dataset

Mohammad A R Abdeen, Sami AlBouq, Ahmed Elmahalawy and Sara Shehata, “A Closer Look at Arabic Text Classification” International Journal of Advanced Computer Science and Applications(IJACSA), 10(11), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0101189

@article{Abdeen2019,
title = {A Closer Look at Arabic Text Classification},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0101189},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0101189},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {11},
author = {Mohammad A R Abdeen and Sami AlBouq and Ahmed Elmahalawy and Sara Shehata}
}


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