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Arabic Text Classification: A Review

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  • Adel Hamdan Mohammad

Abstract

Text classification is an important topic. The number of electronic documents available on line is massive. Text classification aims to classify documents into a set of predefined categories. Number of researches conducted on English dataset is great in comparison with number of researches done using Arabic dataset. This research could be considered as reference for most researchers who deal with Arabic dataset. This research used the most well-known algorithms used in text classification with Arabic dataset. Besides that, dataset used in this research is large enough in comparison with most dataset for Arabic language used in other researches. In addition, this research used different selections and weighting methods for documents. I expect that all researchers who would write researches using Arabic dataset will find this work helpful. Algorithms used in this research are naïve Bayesian, support vector machines, artificial neural networks, k- nearest neighbors, C4.5 decision tree and rocchio classifier.

Suggested Citation

  • Adel Hamdan Mohammad, 2019. "Arabic Text Classification: A Review," Modern Applied Science, Canadian Center of Science and Education, vol. 13(5), pages 1-88, May.
    • Handle: RePEc:ibn:masjnl:v:13:y:2019:i:5:p:88
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    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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