New stemming for arabic text classification using feature selection and decision trees

In this paper we conduct a comparative study between two stemming algorithms: khoja stemmer and our new stemmer for Arabic text classification (categorization), using Chisquare statistics as feature selection and focusing on decision tree classifier. Evaluation used a corpus that consists of 5070 documents independently classified into six categories: sport, entertainment, business, middle east, switch and world, on WEKA toolkit. The recall measure is used to compare the performance of these methods. Results show that text classification using our new stemmer outperforms classification using Khoja stemmer. Keywords—Arabic Text classification; Stemming; Decision tree; Chi-square;