Network text analysis: a two-way classification approachCitation formats

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Network text analysis: a two-way classification approach. / Celardo, Livia; Everett, Martin.

In: International Journal of Information Management, 2019.

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Celardo, Livia ; Everett, Martin. / Network text analysis: a two-way classification approach. In: International Journal of Information Management. 2019.

Bibtex

@article{bf084dad119344af8138287d029c7d5d,
title = "Network text analysis: a two-way classification approach",
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. ",
author = "Livia Celardo and Martin Everett",
year = "2019",
doi = "10.1016/j.ijinfomgt.2019.09.005",
language = "English",
journal = "International Journal of Information Management",
issn = "0268-4012",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

T1 - Network text analysis: a two-way classification approach

AU - Celardo, Livia

AU - Everett, Martin

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

U2 - 10.1016/j.ijinfomgt.2019.09.005

DO - 10.1016/j.ijinfomgt.2019.09.005

M3 - Article

JO - International Journal of Information Management

JF - International Journal of Information Management

SN - 0268-4012

ER -