Authorship attribution of Morsi Gameel Aziz’s lyrics: A clustering-based stylometry approach

Abstract

Numerous studies have addressed the issue of the authorship of Morsi Gameel Aziz's lyrics. These studies have traditionally been based on chronological criteria for determining the real authors of disputed lyrics. To date, there is no agreement on the real authors of these disputed lyrics. This can mainly be attributed to both selectivity and the lack of empirical evidence in such studies, raising questions about the reliability of such approaches. With the advent of machine learning systems and data mining techniques, it is now possible to process thousands of texts using replicable methods. Thus, this study seeks to address the issue of the authorship of Morsi Gameel Aziz's lyrics making use of these advances by applying a clustering-based stylometry approach. The hypothesis is that lyrics grouped or clustered together are more likely to be written by the same poet. A corpus of 1,089 lyrics was built, including all known lyrics attributed to Aziz and the lyrics of the poets thought to be the real authors of the disputed lyrics. The lyrics were clustered using the Gibbs sampling Dirichlet multinomial mixture (GSDMM) technique and were assigned to four main classes, with the 12 disputed lyrics clustered within Aziz's class. Based on this, it is clear that the GSDMM model is effective and reliable in clustering short documents in Arabic. The results of the study show that machine learning systems and stylometric authorship techniques can be used in resolving many authorship questions that remain controversial and unanswered in Arabic literature.