A Graph-based Approach to Text Genre Analysis

Abstract

Genre characterization can be achieved by a variety of methods that employ lexical, syntactic, and presentation features of text to highlight key domain differences and stylistic preferences. However, these traditional methods cannot uncover some important macro-structural features that are embedded in text. Representation of text as a word graph can enable effective frameworks for analysis and identification of key topological features that characterize genres of text. In this study, we investigated graph features such as clustering coefficients, centralization, diameter, and average path lengths for eight text genres. The findings indicated key patterns that vary from a genre to another according to the stylistic differences in text. Furthermore, evidence of subgenres was found through some graph features such as number of connected components and node heterogeneity.