An intelligent system for author attribution based on a hybrid feature set

Abstract

Authorship analysis is a long explored area in the computational research. Recently, there has been growing interest in developing intelligent systems that are capable of authorship identification. Inspired by recent works, we address the problem of author attribution of Arabic text. This area, in specific, has not been targeted in the literature except for few studies. However, it is a challenging problem as there are linguistic complexities associated with the Arabic language including elongation and inflection challenges. For this purpose, we propose a novel hybrid feature set consisting of: lexical, syntactic, structural and content-specific features for 456 instances belonging to seven different Arabic authors. For validation, we run extensive experiments with different intelligent classifiers and show the strength of the proposed feature set. Our results show that the proposed feature set has proved successful with a classification performance accuracy of 88% with the hold-out test and 82% with the cross-validation test.