Research Interests:
Research Interests:
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... more
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.
(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.