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Network text analysis: A two-way classification approach

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  • Celardo, Livia
  • Everett, Martin G.

Abstract

Text clustering is a well-known method for information retrieval and numerous methods for classifying words, documents or both together have been proposed. Frequently, textual data are encoded using vector models so the corpus is transformed in to a matrix of terms by documents; using this representation text clustering generates groups of similar objects on the basis of the presence/absence of the words in the documents. An alternative way to work on texts is to represent them as a network where nodes are entities connected by the presence and distribution of the words in the documents. In this work, after summarising the state of the art of text clustering we will present a new network approach to textual data. We undertake text co-clustering using methods developed for social network analysis. Several experimental results will be presented to demonstrate the validity of the approach and the advantages of this technique compared to existing methods.

Suggested Citation

  • Celardo, Livia & Everett, Martin G., 2020. "Network text analysis: A two-way classification approach," International Journal of Information Management, Elsevier, vol. 51(C).
    • Handle: RePEc:eee:ininma:v:51:y:2020:i:c:s0268401218313914
    DOI: 10.1016/j.ijinfomgt.2019.09.005
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    Cited by:

    1. Xueli Li & Songtao Geng & Suyu Liu, 2022. "Social Network Analysis on Tourists’ Perceived Image of Tropical Forest Park: Implications for Niche Tourism," SAGE Open, , vol. 12(1), pages 21582440211, January.
    2. Juana Alonso-Cañadas & Laura Saraite-Sariene & Federico Galán-Valdivieso & María del Carmen Caba-Pérez, 2023. "Green Tweets or Not? The Sustainable Commitment of Higher Education Institutions," SAGE Open, , vol. 13(4), pages 21582440231, December.

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