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• W2976002893 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." @default.
• W2976002893 created "2019-10-03" @default.
• W2976002893 creator A5022929961 @default.
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• W2976002893 date "2020-04-01" @default.
• W2976002893 modified "2023-10-16" @default.
• W2976002893 title "Network text analysis: A two-way classification approach" @default.
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• W2976002893 doi "https://doi.org/10.1016/j.ijinfomgt.2019.09.005" @default.
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