Identification of Literary Movements Using Complex Networks to Represent Texts

Abstract

The use of statistical methods to analyze large databases of text has been useful to unveil patterns of human behavior and establish historical links between cultures and languages. In this study, we identify literary movements by treating books published from 1590 to 1922 as complex networks, whose metrics were analyzed with multivariate techniques to generate six clusters of books. The latter correspond to time periods coinciding with relevant literary movements over the last 5 centuries. The most important factor contributing to the distinction between different literary styles was {the average shortest path length (particularly, the asymmetry of the distribution)}. Furthermore, over time there has been a trend toward larger average shortest path lengths, which is correlated with increased syntactic complexity, and a more uniform use of the words reflected in a smaller power-law coefficient for the distribution of word frequency. Changes in literary style were also found to be driven by opposition to earlier writing styles, as revealed by the analysis performed with geometrical concepts. The approaches adopted here are generic and may be extended to analyze a number of features of languages and cultures.

The Supplementary Information (SI) is available from http://iopscience.iop.org/1367-2630/14/4/043029/media/njp043029suppdata.pdf

References in corpus (4)

• Finding community structure in networks using the eigenvectors of matrices
• Parameter estimation for power-law distributions by maximum likelihood methods
• Network properties of written human language
• Correlations between structure and dynamics in complex networks

Cited by in corpus (6)

• Text authorship identified using the dynamics of word co-occurrence networks
• Probing the topological properties of complex networks modeling short written texts
• Concentric network symmetry grasps authors' styles in word adjacency networks
• Authorship recognition via fluctuation analysis of network topology and word intermittency
• Complex networks analysis of language complexity
• A perspective on the advancement of natural language processing tasks via topological analysis of complex networks