title: Automated authorship attribution using advanced signal classification techniques.
abstract: In this paper, we develop two automated authorship attribution schemes, one based on Multiple Discriminant Analysis (MDA) and the other based on a Support Vector Machine (SVM). The classification features we exploit are based on word frequencies in the text. We adopt an approach of preprocessing each text by stripping it of all characters except a-z and space. This is in order to increase the portability of the software to different types of texts. We test the methodology on a corpus of undisputed English texts, and use leave-one-out cross validation to demonstrate classification accuracies in excess of 90%. We further test our methods on the Federalist Papers, which have a partly disputed authorship and a fair degree of scholarly consensus. And finally, we apply our methodology to the question of the authorship of the Letter to the Hebrews by comparing it against a number of original Greek texts of known authorship. These tests identify where some of the limitations lie, motivating a number of open questions for future work. An open source implementation of our methodology is freely available for use at https://github.com/matthewberryman/author-detection.
authors: School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, South Australia, Australia.
authors: Maryam
authors: M
authors: Ebrahimpour
authors: Tālis J
authors: TJ
authors: Putniņš
authors: Matthew J
authors: MJ
authors: Berryman
authors: Andrew
authors: A
authors: Allison
authors: Brian W-H
authors: BW
authors: Ng
authors: Derek
authors: D
authors: Abbott
mesh_terms: D001319:Authorship; D001331:Automation; D019992:Databases as Topic; D016002:Discriminant Analysis; D007802:Language; D016013:Likelihood Functions; D060388:Support Vector Machine; D013782:Textbooks as Topic