Malcolm Corney
Alison Anderson
George Mohay

Abstract

We describe an investigation of authorship gender and language background cohort attribution mining from e-mail text documents. We used an extended set of predominantly topic content-free e-mail document features such as style markers, structural characteristics and gender-preferential language features together with a Support Vector Machine learning algorithm. Experiments using a corpus of e-mail documents generated by a large number of authors of both genders gave promising results for both author gender and language background cohort categorisation.