چکیده (انگلیسی):

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.