A complex network approach to stylometry

TL;DR

This paper introduces a complex network approach to stylometry, demonstrating that combining topological properties with traditional text analysis enhances language classification performance.

Contribution

It presents a novel hybrid method that integrates complex network topology with statistical text analysis to improve stylometric classification accuracy.

Findings

Hybrid approach outperforms traditional methods

Topological properties complement textual features

Framework applicable to various textual analysis tasks

Abstract

Statistical methods have been widely employed to study the fundamental properties of language. In recent years, methods from complex and dynamical systems proved useful to create several language models. Despite the large amount of studies devoted to represent texts with physical models, only a limited number of studies have shown how the properties of the underlying physical systems can be employed to improve the performance of natural language processing tasks. In this paper, I address this problem by devising complex networks methods that are able to improve the performance of current statistical methods. Using a fuzzy classification strategy, I show that the topological properties extracted from texts complement the traditional textual description. In several cases, the performance obtained with hybrid approaches outperformed the results obtained when only traditional or networked methods were used. Because the proposed model is generic, the framework devised here could be straightforwardly used to study similar textual applications where the topology plays a pivotal role in the description of the interacting agents.