Authorship classification techniques: Bridging textual domains and languages

Abstract

Authorship classification analyzes an author's prior work to identify their writing style, a unique trait of each language and individual author. This research aims to conduct a thorough comparative analysis of various methods for classifying authorship. The study leverages two corpora: AAALitCorpus of Albanian literary texts and CCAT10 of English columns. We evaluate model-generated features across different configurations. The richness of the features and the breadth of the analysis provide a significant understanding of the problem, setting a new standard for comprehensive linguistic investigations across multiple languages. The study indicates that machine learning algorithms accurately discern authorial writing styles, highlighting the complexities of classifying authorship in a cross-linguistic context.