Authorship attribution in Arabic poetry using NB, SVM, SMO

We study in this paper an authorship attribution in Arabic poetry using text mining classification. Several features 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, Support Vector Machine SVM, and Sequential Minimal Optimization SMO. The data set of experiment was divided into two groups: training dataset with known Poets and test dataset with unknown Poets. In the experiment, a group of 54 poets from completely different eras are used. The Experiment shows interesting results with classification precision of 98.96%.