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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Representation of texts as complex networks: a mesoscopic approach

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Author(s):
de Arruda, Henrique Ferraz [1] ; Silva, Filipi Nascimento [2] ; Marinho, Vanessa Queiroz [1] ; Amancio, Diego Raphael [1] ; Costa, Luciano da Fontoura [2]
Total Authors: 5
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
Total Affiliations: 2
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