The Effect of Stemming on Arabic Text Classification: An Empirical Study

Abdullah Wahbeh, Mohammed Al-Kabi, Qasem Al-Radaideh, Emad Al-Shawakfa and Izzat Alsmadi
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Abdullah Wahbeh: Dakota State University, USA
Mohammed Al-Kabi: Yarmouk University, Jordan
Qasem Al-Radaideh: Yarmouk University, Jordan
Emad Al-Shawakfa: Yarmouk University, Jordan
Izzat Alsmadi: Yarmouk University, Jordan

International Journal of Information Retrieval Research (IJIRR), 2011, vol. 1, issue 3, 54-70

Abstract: The information world is rich of documents in different formats or applications, such as databases, digital libraries, and the Web. Text classification is used for aiding search functionality offered by search engines and information retrieval systems to deal with the large number of documents on the web. Many research papers, conducted within the field of text classification, were applied to English, Dutch, Chinese, and other languages, whereas fewer were applied to Arabic language. This paper addresses the issue of automatic classification or classification of Arabic text documents. It applies text classification to Arabic language text documents using stemming as part of the preprocessing steps. Results have showed that applying text classification without using stemming; the support vector machine (SVM) classifier has achieved the highest classification accuracy using the two test modes with 87.79% and 88.54%. On the other hand, stemming has negatively affected the accuracy, where the SVM accuracy using the two test modes dropped down to 84.49% and 86.35%.

Date: 2011
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