The Impact of Oath Writing Style on Stylometric Features and Machine Learning Classifiers

Abstract

Abstract:Computational stylometry is the field that studies the distinctive style of a written text using computational tasks. The first task is how to define quantifiable measures in a text and the second is to classify the text into a predefined category. This study propose a stylometric features selection approach evaluated by machine learning algorithms to find the finest of the features and to study the impact of the features selection on the classifiers performance in the domain of oath statement in the Quranic text. The results show that better classifiers performance is highly affected by the best feature selection which is associated to an explicit oath style.

An application of distributional semantics for the analysis of the Holy Quran

“…Among those, [1] proposes a stylometric model for the study of Quranic oath expressions using application-specific features. Also in [2] the authors focus on the domain of oath statement in the Quranic text, by introducing a stylometric features selection approach evaluated by machine learning algorithms, to find the finest of the features and to study the impact of the features selection on the classifiers performance. Furthermore, there are a few works investigating the authorship of the Quran in which the task of Authorship Attribution has been performed through author discrimination, a method consisting in checking whether two texts are written by the same author or not.…”