Text mining draw more and more attention recently, it has been applied on different domains including web mining, and sentiment analysis. Text preprocessing is an important stage in text mining. The main problems in text mining are structuring text data, and the very high dimensionality of text data. Natural language processing and morphological tools can be employed to reduce the dimensionality of text data. In addition, term weighting schemes can be used to enhance text representation as feature vector. Researches in the field of Arabic text mining are still fairly limited. The work of this book presents and compares the impact of text preprocessing on Arabic text classification using popular text classification algorithms. Text preprocessing includes applying different term weighting schemes, and Arabic morphological analysis (stemming and light stemming). Text Classification algorithms are applied on 7 Arabic corpora. Results show that Light stemming with term pruning is best feature reduction technique; Support Vector Machines and Naïve Bayes variations outperform other algorithms; Weighting schemes impact the performance of distance based classifier.