Probing the topological properties of complex networks modeling short written texts
This work addresses the problem of analyzing short texts for stylistic analysis, enabling applications like authorship recognition in limited text samples, though it is incremental as it builds on existing word adjacency models.
The study investigated whether important topological patterns exist in small pieces of texts by analyzing subtexts from 50 novels, finding that most topological measurements are stable for short subtexts and that authorship recognition with short texts can match or outperform full texts, with SVM classification based on short texts surpassing that with entire books.
In recent years, graph theory has been widely employed to probe several language properties. More specifically, the so-called word adjacency model has been proven useful for tackling several practical problems, especially those relying on textual stylistic analysis. The most common approach to treat texts as networks has simply considered either large pieces of texts or entire books. This approach has certainly worked well -- many informative discoveries have been made this way -- but it raises an uncomfortable question: could there be important topological patterns in small pieces of texts? To address this problem, the topological properties of subtexts sampled from entire books was probed. Statistical analyzes performed on a dataset comprising 50 novels revealed that most of the traditional topological measurements are stable for short subtexts. When the performance of the authorship recognition task was analyzed, it was found that a proper sampling yields a discriminability similar to the one found with full texts. Surprisingly, the support vector machine classification based on the characterization of short texts outperformed the one performed with entire books. These findings suggest that a local topological analysis of large documents might improve its global characterization. Most importantly, it was verified, as a proof of principle, that short texts can be analyzed with the methods and concepts of complex networks. As a consequence, the techniques described here can be extended in a straightforward fashion to analyze texts as time-varying complex networks.