Machine Learning for Authorship Attribution in Arabic Poetry

Abstract

This paper presented an authorship attribution in Arabic poetry using machine learning. Public features in poetry such as Characters, Poetry Sentence length; Word length, Rhyme, Meter and First word in the sentence are used as input data for text mining classification algorithms Naïve Bayes NB and Support Vector Machine SVM. The main problem: Can we automatically determine who poet wrote an unknown text, to solve this problem we use style markers to identify the author. The dataset of this work was divided into two groups: training dataset with known Poets and test dataset with unknown Poets. In this work, a group of 73 poets from completely different eras are used. The Experiment shows interesting results with classification precision of 98.63%.