Text structuring methods based on complex network: a systematic review

Samuel Zanferdini Oliva (), Livia Oliveira-Ciabati, Denise Gazotto Dezembro, Mário Sérgio Adolfi Júnior, Maísa Carvalho Silva, Hugo Cesar Pessotti and Juliana Tarossi Pollettini
Additional contact information
Samuel Zanferdini Oliva: Kidopi Soluções em Informática Ltda
Livia Oliveira-Ciabati: Kidopi Soluções em Informática Ltda
Denise Gazotto Dezembro: Kidopi Soluções em Informática Ltda
Mário Sérgio Adolfi Júnior: Kidopi Soluções em Informática Ltda
Maísa Carvalho Silva: Kidopi Soluções em Informática Ltda
Hugo Cesar Pessotti: Kidopi Soluções em Informática Ltda
Juliana Tarossi Pollettini: Kidopi Soluções em Informática Ltda

Scientometrics, 2021, vol. 126, issue 2, No 25, 1493 pages

Abstract: Abstract Currently, there is a large amount of text being shared through the Internet. These texts are available in different forms—structured, unstructured and semi structured. There are different ways of analyzing texts, but domain experts usually divide this process in some steps such as pre-processing, feature extraction and a final step that could be classification, clustering, summarization, and keyword extraction, depending on the purpose over the text. For this processing, several approaches have been proposed in the literature based on variations of methods like artificial neural network and deep learning. In this paper, we conducted a systematic review of papers dealing with the use of complex networks approaches for the process of analyzing text. The main results showed that complex network topological properties, measures and modeling can capture and identify text structures concerning different purposes such as text analysis, classification, topic and keyword extraction, and summarization. We conclude that complex network topological properties provide promising strategies with respect of processing texts, considering their different aspects and structures.

Keywords: Complex network; Text network; Text analysis; Natural language processing; Network science (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s11192-020-03785-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:scient:v:126:y:2021:i:2:d:10.1007_s11192-020-03785-y

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11192

DOI: 10.1007/s11192-020-03785-y

Access Statistics for this article

Scientometrics is currently edited by Wolfgang Glänzel

More articles in Scientometrics from Springer, Akadémiai Kiadó
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().