Ensemble methods for instance-based Arabic language authorship attribution

Abstract

The Authorship Attribution (AA) is considered as a subfield of authorship analysis and it is an important problem as the range of anonymous information increased with fast-growing of internet usage worldwide. In other languages such as English, Spanish and Chinese, such issue is quite well studied. However, in the Arabic language, the AA problem has received less attention from the research community due to the complexity and nature of Arabic sentences. The paper presented an intensive review of previous studies for Arabic language. Based on that, this study has employed the Technique for Order Preferences by Similarity to Ideal Solution (TOPSIS) method to choose the base classifier of the ensemble methods. In terms of attribution features, hundreds of stylometric features and distinct words using several tools have been extracted. Then, AdaBoost and Bagging ensemble methods have been applied to Arabic enquires (Fatwa) dataset. The findings showed an improvement of the effectiveness of the authorship attribution task in the Arabic language.