Arabic text author identification using support vector machines

A model for Arabic text author identification is proposed. It classifies a set of Arabic text documents with unknown authorship by capturing the style of each author through features extracted from the text. The identification process is achieved through five phases which are: documents collection, dataset preparation, features extraction, features optimization and classification model building. The model relies on Support Vector Machines (SVM) and combines two feature types on two domains: Political Analysis Articles and Literature. The experiments show that the model is effective with classification accuracy that may reach 100%.