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Text structuring methods based on complex network: a systematic review

Author

Listed:
  • 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)

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.

Suggested Citation

  • Samuel Zanferdini Oliva & Livia Oliveira-Ciabati & Denise Gazotto Dezembro & Mário Sérgio Adolfi Júnior & Maísa Carvalho Silva & Hugo Cesar Pessotti & Juliana Tarossi Pollettini, 2021. "Text structuring methods based on complex network: a systematic review," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1471-1493, February.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:2:d:10.1007_s11192-020-03785-y
    DOI: 10.1007/s11192-020-03785-y
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