The Impact of Oath Writing Style on Stylometric Features and Machine Learning Classifiers
- 1 Universiti Putra Malaysia, Malaysia
- 2 Universiti Tun Hussein Onn Malaysia, Malaysia
Copyright: © 2020 Ahmad Alqurneh and Aida Mustapha. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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.
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- Feature Selection
- Classifiers Performance
- Oath Styles