Title: Performance of authorship attribution classifiers with short texts: application of religious Arabic fatwas
Authors: Mohammed Al-Sarem; Abdel-Hamid Emara; Ahmed Abdel Wahab
Addresses: Department of Information System, Taibah University, Madinah, Saudi Arabia; Department of Information Science, Saba'a Region University, Mareb, Yemen ' Computers and Systems Engineering Department, Faculty of Engineering, Al-Azhar University, Egypt; Department of Computer Science, Taibah University, Madinah, Saudi Arabia ' Computers and Systems Engineering Department, Faculty of Engineering, Al-Azhar University, Egypt
Abstract: Although authorship attribution is a well-known problem in authorship analysis domain, researches on Arabic contexts are still limited. In addition, examining the performance of the attribution methods on training set with short textual documents is also not considered well in other languages, such as English, Chinese, Spanish and Dutch. Therefore, this current work aims at examining the performance of attribution classifiers in the context of short Arabic textual documents. The experimental part of this work is conducted with well-known classifiers namely: decision tree C4.5 method, naive Bayes model, K-NN method, Markov model, SMO and Burrows Delta method. We experiment with various features combination. The results show that combining the word-based lexical features with the structural features yields the best accuracy. At this end, we use this combination as a baseline for further investigation. We also examine the effect of combining the n-gram features. The results indicate that some classifiers show an improvement while the others do not. In addition, the results show that the naive Bayes method gives the highest accuracy among all the attribution classifiers.
Keywords: authorship attribution; AA; stylomatric features; SF; attribution classifiers; JGAAP tool; Arabic language.
International Journal of Data Mining, Modelling and Management, 2020 Vol.12 No.3, pp.350 - 364
Received: 30 Oct 2018
Accepted: 27 Jan 2019
Published online: 23 Jul 2020 *