Linguistic data mining with complex networks: a stylometric-oriented approach

TL;DR

This paper explores using word-adjacency networks to identify individual language styles in literary texts, achieving over 90% accuracy in authorship attribution by analyzing network characteristics.

Contribution

It introduces a network-based approach for stylometric analysis, extending traditional lexical methods with complex network metrics for authorship attribution.

Findings

Over 90% accuracy in authorship attribution using network features

Weighted clustering coefficients and degrees are key indicators

Network analysis reveals language-specific stylistic differences

Abstract

By representing a text by a set of words and their co-occurrences, one obtains a word-adjacency network being a reduced representation of a given language sample. In this paper, the possibility of using network representation to extract information about individual language styles of literary texts is studied. By determining selected quantitative characteristics of the networks and applying machine learning algorithms, it is possible to distinguish between texts of different authors. Within the studied set of texts, English and Polish, a properly rescaled weighted clustering coefficients and weighted degrees of only a few nodes in the word-adjacency networks are sufficient to obtain the authorship attribution accuracy over 90%. A correspondence between the text authorship and the word-adjacency network structure can therefore be found. The network representation allows to distinguish individual language styles by comparing the way the authors use particular words and punctuation marks. The presented approach can be viewed as a generalization of the authorship attribution methods based on simple lexical features. Additionally, other network parameters are studied, both local and global ones, for both the unweighted and weighted networks. Their potential to capture the writing style diversity is discussed; some differences between languages are observed.