298 Int. J. Computer Applications in Technology, Vol. 60, No. 4, 2019 Copyright © 2019 Inderscience Enterprises Ltd. A comparison of text classification methods using different stemming techniques Mariem Bounabi* Computer Sciences, Imaging and Numerical Analysis Laboratory (LIIAN), USMBA University Fes, Fez City, Morocco Email: mariem.bounabi@usmba.ac.ma *Corresponding author Karim El Moutaouakil Hoceima National School of Applied Sciences (ENSAH), Mohammed First University, Al-Hoceima, Morocco Email: karimmoutaouakil@yahoo.fr Khalid Satori Computer sciences, Imaging and Numerical Analysis Laboratory (LIIAN), USMBA University Fes, Fez City, Morocco Email: khalidsatori@gmail.com Abstract: In the retrieval of information, two factors have an important impact on the performance of systems: the extract features and the matching process. In this work, we compare three well-known stemming techniques: Lovins stemmer, iterated Lovins and snowball stemmer. Concerning the classification phase, we compare, experimentally, six methods: BNET, NBMU, CNB, RF, SLogicF, and SVM. Basing on this comparison, we propose a new retrieval system by calling the voting method, as a matching tool, to improve the performance of the classical systems. In this paper, we use the TF-IDF algorithm to extract features. The envisaged systems are tested on two databases: BBCNEWS and BBCSPORT. The systems based on Lovins stemmers and on the voting technique give the best results. In fact, for the first databases, the best accuracy observed is for the system Lovins + Vote with a recognition rate of 97%. Concerning the second database, the system snowball +Vote gives us 99% as recognition rate. Keywords: NBMU; SVM; RF; NB; SLogiF; CNB; voting technique; classification; stemmer; term-weighting. Reference to this paper should be made as follows: Bounabi, M., El Moutaouakil, K. and Satori, K. (2019) ‘A comparison of text classification methods using different stemming techniques’, Int. J. Computer Applications in Technology, Vol. 60, No. 4, pp.298–306. Biographical notes: Mariem Bounabi received the Master degree from Computer Science Department at Faculty of Sciences of Fes (FSDM), Morocco, in 2015. She is currently pursuing her PhD in the same department. Her main research interests include machine learning and retrieval information. Karim El Moutaouakil received the PhD degree from the Faculty of sciences and Technologies in Fez, Morocco in 2011. He is currently an Assistant Professor of Computer Science at the National School of Applied Sciences in Al-Hoceima, Morocco. His research interests include artificial intelligence, machine learning and pattern recognition. Khalid Satori received the PhD degree from the National Institute for the Applied Sciences INSA at Lyon in 1993. He is currently a Full Professor of Computer Science at USMBA University in Morocco. His is the director of the LIIAN Laboratory. His research interests include real-time rendering, image based rendering, virtual reality, biomedical signal, camera self-calibration and 3D reconstruction and pattern recognition.