Arabic Text Categorization
Keywords:
text classification, categorization, naïve Bayes, Key Nearest Neighbor ( KNN), Arabic languageAbstract
In this paper, the researcher compared the performance of two classifiers for Arabic text classification. Naïve Bayes and Key Nearest Neighbor (KNN) were used to classify the documents. These documents which were not classified were preprocessed by removing stop words and punctuation marks from them. The word in each document was presented as a vector . These vectors were used in WEKA tool to give the results. The accuracy of two algorithms was compared using precision, recall, f-measure. The results showed that the accuracy Naïve Bayes algorithm was better than Key Nearest Neighbor( KNN) algorithm .
References
Kanaan ,G.G. 2006. Arabic Text Categorization Using kNN Algorithm, Proceedings of The 4th International Multiconference on Computer Science and Information Technology, Amman, Jordan, Vol 4.
Elhassan ,R,M. 2015. Arabic Text Classification Process, International Journal of Computer Science and Software Engineering, pp. 258-265
Errub ,A.A. 2014. Arabic text categorization algorithm using vector evaluation method, international journal of computer science & information technology , Vol 6.
Shalabi.R. 2015. Different classification algorithms based on Arabic text classification, Study International Journal of Advanced Computer Science and Applications, Vol. 6, No 2.
Alshammari.R. 2018. Arabic Text Categorization using Machine Learning Approaches, International Journal of Advanced Computer Science and Applications, Vol 9,No 3 .
Zhu,S.O. 2008. Text categorization via generalized discriminate analysis, Information Processing and Management ,an International Journal, Vol. 44, PP. 1684-1697.
Litoriya,R. 2012. Comparison the various clustering algorithms of weka tools, International Journal of Emerging Technology and Advanced Engineering .
