An experimental study for Arabic text classification techniques

Several algorithms have been implemented to resolve the problem of text categorization. Most of the work in this area geared for English text, whereas few researches have been conducted on Arabic text. However, the nature of Arabic text is different than English text; pre-processing of Arabic text are more challenging. In this paper an experimental study was conducted on three techniques for Arabic text classification; these techniques, Discriminative Multinominal Naive Bayes (DMNB), Naïve Bayesian (NB) and IBK Algorithms, The paper aimed to assess the accuracy for each classifier and to determine 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.