Gender and Authorship Categorisation of Arabic Text from Twitter Using PPM

摘要

In this paper we present gender and authorship categorisationusing the Prediction by Partial Matching(PPM) compression scheme for text from Twitter written in Arabic. The PPMD variant of the compressionscheme with different orders was used to perform the categorisation. We also applied different machinelearning algorithms such as Multinational Na飗e Bayes (MNB), K-Nearest Neighbours (KNN), and animplementation of Support Vector Machine (LIBSVM), applying the same processing steps for all thealgorithms. PPMD shows significantly better accuracy in comparison to all the other machine learningalgorithms, with order 11 PPMD working best, achieving 90 % and 96% accuracy for gender andauthorship respectively.