Representation of texts as complex networks: a mesoscopic approach

Abstract

Statistical techniques that analyze texts, referred to as text analytics, have departed from the use of simple word count statistics towards a new paradigm. Text mining now hinges on a more sophisticated set of methods, including the representations in terms of complex networks. While well-established word-adjacency (co-occurrence) methods successfully grasp syntactical features of written texts, they are unable to represent important aspects of textual data, such as its topical structure, i.e. the sequence of subjects developing at a mesoscopic level along the text. Such aspects are often overlooked by current methodologies. In order to grasp the mesoscopic characteristics of semantical content in written texts, we devised a network model which is able to analyze documents in a multi-scale fashion. In the proposed model, a limited amount of adjacent paragraphs are represented as nodes, which are connected whenever they share a minimum semantical content. To illustrate the capabilities of our model, we present, as a case example, a qualitative analysis of "Alice's Adventures in Wonderland". We show that the mesoscopic structure of a document, modeled as a network, reveals many semantic traits of texts. Such an approach paves the way to a myriad of semantic-based applications. In addition, our approach is illustrated in a machine learning context, in which texts are classified among real texts and randomized instances.

References in corpus (13)

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• Network properties of written human language
• Edge vulnerability in neural and metabolic networks
• A complex network approach to stylometry
• Authorship Attribution through Function Word Adjacency Networks
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• Word sense disambiguation via high order of learning in complex networks
• Authorship recognition via fluctuation analysis of network topology and word intermittency
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• Modeling the average shortest path length in growth of word-adjacency networks
• Topic segmentation via community detection in complex networks

Cited by in corpus (6)

• The Data Science of Hollywood: Using Emotional Arcs of Movies to Drive Business Model Innovation in Entertainment Industries
• Paragraph-based complex networks: application to document classification and authenticity verification
• Semantic flow in language networks
• Derivative of a hypergraph as a tool for linguistic pattern analysis
• Text characterization based on recurrence networks
• An Image Analysis Approach to the Calligraphy of Books