Representation of texts as complex networks: a mesoscopic approach

Author(s)

de Arruda, Henrique Ferraz ^[1] ; Silva, Filipi Nascimento ^[2] ; Marinho, Vanessa Queiroz ^[1] ; Amancio, Diego Raphael ^[1] ; Costa, Luciano da Fontoura ^[2]

Affiliation

^[1] Univ Sao Paulo, Inst Math & Comp Sci, Ave Trabalhador Sancarlense 400, BR-13566590 Sao Carlos, SP - Brazil

^[2] Univ Sao Paulo, Sao Carlos Inst Phys, Sao Ave Trabalhador Sancarlense 400, BR-13566590 Sao Carlos, SP - Brazil

• Document type: Journal article
• Source: JOURNAL OF COMPLEX NETWORKS; v. 6, n. 1, p. 125-144, FEB 2018.
• Web of Science Citations: 3

Abstract

Statistical techniques that analyse 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, that is 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 analyse 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, modelled 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. (AU)

• FAPESP's process: 15/05676-8 - Development of new models for authorship recognition using complex networks
• Grantee: Vanessa Queiroz Marinho
• Support type: Scholarships in Brazil - Master
• FAPESP's process: 15/08003-4 - Complex network approach to e-Science and dynamic datasets
• Grantee: Filipi Nascimento Silva
• Support type: Scholarships in Brazil - Post-Doctorate
• FAPESP's process: 14/20830-0 - Using complex networks to recognize patterns in written texts
• Grantee: Diego Raphael Amancio
• Support type: Regular Research Grants
• FAPESP's process: 11/50761-2 - Models and methods of e-Science for life and agricultural sciences
• Grantee: Roberto Marcondes Cesar Junior
• Support type: Research Projects - Thematic Grants
• FAPESP's process: 16/19069-9 - Using semantical information to classify texts modelled as complex networks
• Grantee: Diego Raphael Amancio
• Support type: Regular Research Grants