Using Word N-Grams as Features in Arabic Text Classification

  • Abdulmohsen Al-ThubaityEmail author
  • Muneera Alhoshan
  • Itisam Hazzaa
Part of the Studies in Computational Intelligence book series (SCI, volume 569)


The feature type (FT) chosen for extraction from the text and presented to the classification algorithm (CAL) is one of the factors affecting text classification (TC) accuracy. Character N-grams, word roots, word stems, and single words have been used as features for Arabic TC (ATC). A survey of current literature shows that no prior studies have been conducted on the effect of using word N-grams (N consecutive words) on ATC accuracy. Consequently, we have conducted 576 experiments using four FTs (single words, 2-grams, 3-grams, and 4-grams), four feature selection methods (document frequency (DF), chi-squared, information gain, and Galavotti, Sebastiani, Simi) with four thresholds for numbers of features (50, 100, 150, and 200), three data representation schemas (Boolean, term frequency-inversed document frequency, and lookup table convolution), and three CALs (naive Bayes (NB), k-nearest neighbor (KNN), and support vector machine (SVM)). Our results show that the use of single words as a feature provides greater classification accuracy (CA) for ATC compared to N-grams. Moreover, CA decreases by 17% on average when the number of N-grams increases. The data also show that the SVM CAL provides greater CA than NB and KNN; however, the best CA for 2-grams, 3-grams, and 4-grams is achieved when the NB CAL is used with Boolean representation and the number of features is 200.


Arabic text classification feature extraction classification algorithms classification accuracy 


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  1. 1.
    Alarifi, A., Alghamdi, M., Zarour, M., Aloqail, B., Lraqibah, H., Alsadhan, K., Alkwai, L.: Estimating the Size of Arabic Indexed Web Content. Scientific Research and Essays 7(28), 2472–2483 (2012)Google Scholar
  2. 2.
    Mesleh, A.M.: Feature sub-set selection metrics for Arabic text classification. Pattern Recognition Letters 32(14), 1922–1929 (2011)CrossRefGoogle Scholar
  3. 3.
    Althubaity, A., Almuhareb, A., Alharbi, S., Al-Rajeh, A., Khorsheed, M.: KACST Arabic Text Classification Project: Overview and Preliminary Results. In: 9th IBMIA Conference on Information Management in Modern Organizations (2008)Google Scholar
  4. 4.
    Alwedyan, J., Hadi, W.M., Salam, M., Mansour, H.Y.: Categorize Arabic data sets using multi-class classification based on association rule approach. In: Proceedings of the 2011 International Conference on Intelligent Semantic Web-Services and Applications, vol. 18 (2011)Google Scholar
  5. 5.
    Khorsheed, M.S., Al-Thubaity, A.O.: Comparative evaluation of text classification techniques using a large diverse Arabic dataset. Language Resources and Evaluation 47(2), 513–538 (2013)CrossRefGoogle Scholar
  6. 6.
    Duwairi, R., Al-Refai, M.N., Khasawneh, N.: Feature reduction techniques for Arabic text categorization. Journal of the American Society for Information Science and Technology 60(11), 2347–2352 (2009)CrossRefGoogle Scholar
  7. 7.
    Noaman, H.M., Elmougy, S., Ghoneim, A., Hamza, T.: Naive Bayes classifier based Arabic document categorization. In: 7th International Conference on Informatics and Systems (INFOS 2010), pp. 1–5 (2010)Google Scholar
  8. 8.
    Harrag, F., El-Qawasmah, E., Al-Salman, A.M.S.: Comparing dimension reduction techniques for Arabic text classification using BPNN algorithm. In: First International Conference on Integrated Intelligent Computing (ICIIC 2010), pp. 6–11 (2010)Google Scholar
  9. 9.
    Al-Shammari, E.T.: Improving Arabic document categorization: Introducing local stem. In: 10th International Conference on Intelligent Systems Design and Applications (ISDA), pp. 385–390 (2010)Google Scholar
  10. 10.
    Sawalha, M., Atwell, E.S.: Comparative evaluation of Arabic language morphological analysers and stemmers. In: Proceedings of COLING 2008 22nd International Conference on Computational Linguistics (Poster Volume), pp. 107–110. Coling 2008 Organizing Committee (2008)Google Scholar
  11. 11.
    Sawaf, H., Zaplo, J., Ney, H.: Statistical classification methods for Arabic news articles. In: Proceedings of the ACL/EACL 2001 Workshop on Arabic Language Processing: Status and Prospects, Toulouse, France (2001)Google Scholar
  12. 12.
    Khreisat, L.: A machine learning approach for Arabic text classification using N-gram frequency statistics. Journal of Informetrics 3(1), 72–77 (2009)CrossRefGoogle Scholar
  13. 13.
    Al-Shalabi, R., Obeidat, R.: Improving KNN Arabic text classification with n-grams based document indexing. In: Proceedings of the Sixth International Conference on Informatics and Systems, Cairo, Egypt, pp. 108–112 (2008)Google Scholar
  14. 14.
    Güran, A., Akyokucs, S., Bayazit, N.G., Gürbüz, M.Z.: Turkish text categorization using N-gram words. In: Proceedings of the International Symposium on Innovations in Intelligent Systems and Applications (INISTA 2009), pp. 369–373 (2009)Google Scholar
  15. 15.
    Bina, B., Ahmadi, M., Rahgozar, M.: Farsi text classification using n-grams and KNN algorithm: A comparative study. In: Proceedings of the 4th International Conference on Data Mining (DMIN 2008), pp. 385–390 (2008)Google Scholar
  16. 16.
    Froud, H., Lachkar, A., Ouatik, S.A.: A comparative study of root-based and stem-based approaches for measuring the similarity between Arabic words for Arabic text mining applications. arXiv preprint arXiv:1212.3634 (2012)Google Scholar
  17. 17.
    Al-Harbi, S., Almuhareb, A., Al-Thubaity, A., Khorsheed, M., Al-Rajeh, A.: Automatic Arabic text classification. In: 9es Journées Internationales d’Analyse Statistique des Données Textuelles, JADT 2008, pp. 77–83 (2008)Google Scholar
  18. 18.
    Al-Saleem, S.: Associative classification to categorize Arabic data sets. International Journal of ACM JORDAN 1, 118–127 (2010)Google Scholar
  19. 19.
    Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34(1), 1–47 (2002)CrossRefGoogle Scholar
  20. 20.
    Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T.: YALE: Rapid prototyping for complex data mining tasks. In: Ungar, L., Craven, M., Gunopulos, D., Eliassi-Rad, T. (eds.) KDD 2006 Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 935–940. ACM, New York (2006)Google Scholar

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© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Abdulmohsen Al-Thubaity
    • 1
    Email author
  • Muneera Alhoshan
    • 1
  • Itisam Hazzaa
    • 2
  1. 1.Computer Research InstituteKing Abdulaziz City for Science and TechnologyRiyadhKSA
  2. 2.College of Computer and Information SciencesKing Saud UniversityRiyadhKSA

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