TraSE: Towards Tackling Authorial Style from a Cognitive Science Perspective

TL;DR

This paper introduces TraSE, a novel cognitive-inspired feature representation for stylistic text analysis, achieving high accuracy in authorship attribution and capturing cognitive traits like age.

Contribution

The paper presents TraSE, a new feature representation that addresses topic influence and data requirements, improving stylistic analysis and authorship attribution.

Findings

90% attribution accuracy on 27,000+ authors

TraSE is robust across domains and data sizes

Qualitative analysis links TraSE to cognitive traits like age

Abstract

Stylistic analysis of text is a key task in research areas ranging from authorship attribution to forensic analysis and personality profiling. The existing approaches for stylistic analysis are plagued by issues like topic influence, lack of discriminability for large number of authors and the requirement for large amounts of diverse data. In this paper, the source of these issues are identified along with the necessity for a cognitive perspective on authorial style in addressing them. A novel feature representation, called Trajectory-based Style Estimation (TraSE), is introduced to support this purpose. Authorship attribution experiments with over 27,000 authors and 1.4 million samples in a cross-domain scenario resulted in 90% attribution accuracy suggesting that the feature representation is immune to such negative influences and an excellent candidate for stylistic analysis. Finally, a qualitative analysis is performed on TraSE using physical human characteristics, like age, to validate its claim on capturing cognitive traits.