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An Experimental Study for the Effect of Stop Words Elimination for Arabic Text Classification Algorithms

Author

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  • Bassam Al-Shargabi

    (Isra University, Jordan)

  • Fekry Olayah

    (Isra University, Jordan)

  • Waseem AL Romimah

    (University of Science and Technology, Yemen)

Abstract

In this paper, an experimental study was conducted on three techniques for Arabic text classification. These techniques are Support Vector Machine (SVM) with Sequential Minimal Optimization (SMO), Naïve Bayesian (NB), and J48. The paper assesses the accuracy for each classifier and determines which classifier is more accurate for Arabic text classification based on stop words elimination. The accuracy for each classifier is measured by Percentage split method (holdout), and K-fold cross validation methods, along with the time needed to classify Arabic text. The results show that the SMO classifier achieves the highest accuracy and the lowest error rate, and shows that the time needed to build the SMO model is much lower compared to other classification techniques.

Suggested Citation

  • Bassam Al-Shargabi & Fekry Olayah & Waseem AL Romimah, 2011. "An Experimental Study for the Effect of Stop Words Elimination for Arabic Text Classification Algorithms," International Journal of Information Technology and Web Engineering (IJITWE), IGI Global, vol. 6(2), pages 68-75, April.
    • Handle: RePEc:igg:jitwe0:v:6:y:2011:i:2:p:68-75
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