Text Categorization or Classification (TC) is concerned with placing text documents in their proper category according to
their contents. Due to the various applications of TC and the large volume of text documents uploaded on the Internet
daily, the need for such an automated method stems from the difficulty and tedium of doing such a process manually.
The usefulness of TC is manifested in different fields and needs. For instance, the ability of automatically classifying an
article or an email into its right class (Arts, Economics, Politics, Sports, etc.) would be very appreciated by individual
users as well as companies. This paper is concerned with TC of Arabic articles. It contains a comparison of the five best
known algorithms for TC. It also studies the effects of utilizing different Arabic stemmers (light and root-based stemmers)
on the effectiveness of these classifiers. Furthermore, a comparison between different data mining software tools (Weka
and RapidMiner) is presented. The results illustrate the strong accuracy provided by the SVM classifier, especially when
used with the light10 stemmer. This outcome can be used in future as a baseline to compare with other unexplored
classifiers and Arabic stemmers.