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Automatic Kurdish Text Classification Using KDC 4007 Dataset

Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT,volume 6)


Due to the large volume of text documents uploaded on the Internet daily. The quantity of Kurdish documents which can be obtained via the web increases drastically with each passing day. Considering news appearances, specifically, documents identified with categories, for example, health, politics, and sport appear to be in the wrong category or archives might be positioned in a nonspecific category called others. This paper is concerned with text classification of Kurdish text documents to placing articles or an email into its right class per their contents. Even though there are considerable numbers of studies directed on text classification in other languages, and the quantity of studies conducted in Kurdish is extremely restricted because of the absence of openness, and convenience of datasets. In this paper, a new dataset named KDC-4007 that can be widely used in the studies of text classification about Kurdish news and articles is created. KDC-4007 dataset its file formats are compatible with well-known text mining tools. Comparisons of three best-known algorithms (such as Support Vector Machine (SVM), Naïve Bays (NB) and Decision Tree (DT) classifiers) for text classification and TF × IDF feature weighting method are evaluated on KDC-4007. The paper also studies the effects of utilizing Kurdish stemmer on the effectiveness of these classifiers. The experimental results indicate that the good accuracy value 91.03% is provided by the SVM classifier, especially when the stemming and TF × IDF feature weighting are involved in the preprocessing phase. KDC-4007 datasets are available publicly and the outcome of this study can be further used in future as a baseline for evaluations with other classifiers by other researchers.


  • Support Vector Machine
  • Text Classification
  • Term Frequency
  • Text Document
  • Decision Tree Classifier

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  1. Hotho, A., Nurnberger, A., Paaß, G.: A brief survey of text mining. LDV Forum-GLDV J. Comput. Linguist. Lang. Technol. 20, 19–62 (2005)

    Google Scholar 

  2. Tan, A.: Text mining: the state of the art and the challenges concept-based. In: Proceedings of the PAKDD 1999 Workshop on Knowledge Discovery from Advanced Databases, pp. 65–70 (1999)

    Google Scholar 

  3. Chen, K.C.: Text Mining e-complaints data from e-auction store. J. Bus. Econ. Res. 7(5), 15–24 (2009)

    Google Scholar 

  4. Mohammed, F.S., Zakaria, L., Omar, N., Albared, M.Y.: Automatic kurdish sorani text categorization using N-gram based model. In: 2012 International Conference on Computer & Information Science (ICCIS), 12 Jun 2012, vol. 1, pp. 392–395. IEEE (2012)

    Google Scholar 

  5. Wahbeh, A., Al-Kabi, M., Al-Radaideh, Q., Al-Shawakfa, E., Alsmadi, I.: The effect of stemming on arabic text classification: an empirical study. Int. J. Inf. Retrieval Res. 1(3), 54–70 (2011)

    CrossRef  Google Scholar 

  6. Mohammad, A.H., Alwada’n, T., Al-Momani, O.: Arabic text categorization using support vector machine, Naïve Bayes and neural network. GSTF J. Comput. (JoC) 5(1), 108–115 (2016)

    CrossRef  Google Scholar 

  7. Mohsen, A.M., Hassan, H.A., Idrees, A.M.: Documents emotions classification model based on tf-idf weighting measure. World Acad. Sci. Eng. Technol. Int. J. Comput. Electric. Automat. Control Inf. Eng. 3(1), 1795 (2016)

    Google Scholar 

  8. Hmeidi, I., Al-Ayyoub, M., Abdulla, N.A., Almodawar, A.A., Abooraig, R., Mahyoub, N.A.: Automatic Arabic text categorization: a comprehensive comparative study. J. Inf. Sci. 41(1), 114–124 (2015)

    CrossRef  Google Scholar 

  9. Rish, I.: An empirical study of the naive Bayes classifier. In: IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, 4 August 2001, vol. 3, no. 22, pp. 41–46. IBM, New York (2001)

    Google Scholar 

  10. Sharma, R., Gulati, N.: Improving the accuracy and reducing the redundancy in data mining. Int. J. Eng. Sci., 45–75 (2016)

    Google Scholar 

  11. Last, M., Markov, A., Kandel, A.: Multi-lingual detection of web terrorist content. In: Chen, H. (ed.) WISI. LNCS, pp. 16–30. Springer (2006)

    Google Scholar 

  12. Kotsiantis, S.B., Zaharakis, I., Pintelas, P.: Supervised machine learning: a review of classification techniques, vol. 31, pp. 249–268 (2007)

    Google Scholar 

  13. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    MATH  Google Scholar 

  14. Burges, C.J.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2(2), 121–167 (1998)

    CrossRef  Google Scholar 

  15. Esmaili, K.S., Eliassi, D., Salavati, S., Aliabadi, P., Mohammadi, A., Yosefi, S., Hakimi, S.: Building a test collection for Sorani Kurdish. In: Proceedings of the 10th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA 2013), Ifrane, Morocco, 27–30 May 2013. IEEE, New York (2013)

    Google Scholar 

  16. Hassani, H., Medjedovic, D.: Automatic kurdish dialects identification. Comput. Sci. Inf. Technol., 61 (2016)

    Google Scholar 

  17. Mustafa, A.M., Rashid, T.A.: Kurdish stemmer pre-processing steps for improving information retrieval. J. Inf. Sci., 1–14 (2017). doi: 10.1177/0165551510000000,,

  18. Szymański, J.: Comparative analysis of text representation methods using classification. Cybern. Syst. 45(2), 180–199 (2014)

    Google Scholar 

  19. Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975)

    CrossRef  MATH  Google Scholar 

  20. Patra, A., Singh, D.: A survey report on text classification with different term weighing methods and comparison between classification algorithms. Int. J. Comput. Appl. 75(7) (2013)

    Google Scholar 

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Correspondence to Tarik A. Rashid .

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Rashid, T.A., Mustafa, A.M., Saeed, A.M. (2018). Automatic Kurdish Text Classification Using KDC 4007 Dataset. In: Barolli, L., Zhang, M., Wang, X. (eds) Advances in Internetworking, Data & Web Technologies. EIDWT 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 6. Springer, Cham.

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