Abstract
Authorship Analysis (AA) is a natural language processing field that examines the previous works of writers to identify the author of a text based on its features. Studies in authorship analysis include authorship identification, authorship profiling, and authorship verification. Due to its relevance, to many applications in this field attention has been paid. It is widely used in the attribution of historical literature. Other applications include legal linguistics, criminal law, forensic investigations, and computer forensics. This paper aims to provide an overview of the work done and the techniques applied in the authorship analysis domain. The examination of recent developments in this field is the principal focus. Many different criteria can be used to define a writer’s style. This paper investigates stylometric features in different author-related tasks, including lexical, syntactic, semantic, structural, and content-specific ones. A lot of classification methods have been applied to authorship analysis tasks. We examine many research studies that use different machine learning and deep learning techniques. As a means of pointing the direction for future studies, we present the most relevant methods recently proposed. The reviewed studies include documents of different types and different languages. In summary, due to the fact that each natural language has its own set of features, there is no standard technique generically applicable for solving the AA problem.