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

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%. DOI: 10.4018/ijirr.2011070104 International Journal of Information Retrieval Research, 1(3), 54-70, July-September 2011 55 Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Mesleh et al., 2007; Rahman et al., 2003; Zubi, 2009; Al-Harbi et al., 2008). Several researches applied text classification and its techniques to English and other European languages. On the other hand, few researchers have addressed the issue of Arabic text classification. Text preprocessing and preparation; especially for Arabic, is a crucial task in several applications including; information retrieval, text mining, and natural language processing where the processing tasks include different stages such as: stop word removal and stemming. Stemming tries to reduce a word to its stem (Al-Shammari et al., 2008), stemming process uses word morphological analysis in order to get the word’s stems (Sembok et al., 2011). Stemming is very important technique that usually used in information retrieval and data mining as well as many other NLP applications. Stemming is important for some natural languages and unimportant in others. As reported by Sembok et al. (2011) and Al-Shammari (2008), stemming has the following benefits: • Stemming helps in reducing the size of the