Classification of Arabic Poems: from the 5th to the 15th Century

Abstract

This paper describes a system for classification of Arabic poems according to the eras in which they were written. We used machine learning techniques where we applied a bunch of filters and classifiers. The best results were achieved by using the Multinomial Naïve Bayes (MNB) algorithm, with an accuracy equal to 70.21%, an F1-Score of 68.8% and a Kappa equal to 0.398, without filtering stop words. We observed that the stop words can have a positive impact on the accuracy but also a negative impact if it is used with word tokenizer preprocessing.