Analyzing unstructured text data: Using latent categorization to identify intellectual communities in information systems

Kai R. Larsen, David E. Monarchi, Dirk S. Hovorka, Christopher N. Bailey

Research output: Contribution to journalArticleResearchpeer-review

48 Citations (Scopus)

Abstract

The Information Systems field is structured by the research topics emphasized by communities of journals. The Latent Categorization Method categorized and automatically named IS research topics in 14,510 abstracts from 65 Information Systems journals. These topics were clustered into seven intellectual communities based on publication patterns. The technique develops categories from the data itself, it is replicable, is relatively insensitive to the size of the text units, and it avoids many of the problems that frequently accompany human categorization. As such LCM provides a new approach to analyzing a wide array of textual data.

Original languageEnglish
Pages (from-to)884-896
Number of pages13
JournalDecision Support Systems
Volume45
Issue number4
DOIs
Publication statusPublished - Nov 2008
Externally publishedYes

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Information Systems
Information systems
Research
Publications
Community-based

Cite this

Larsen, Kai R. ; Monarchi, David E. ; Hovorka, Dirk S. ; Bailey, Christopher N. / Analyzing unstructured text data : Using latent categorization to identify intellectual communities in information systems. In: Decision Support Systems. 2008 ; Vol. 45, No. 4. pp. 884-896.
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Analyzing unstructured text data : Using latent categorization to identify intellectual communities in information systems. / Larsen, Kai R.; Monarchi, David E.; Hovorka, Dirk S.; Bailey, Christopher N.

In: Decision Support Systems, Vol. 45, No. 4, 11.2008, p. 884-896.

Research output: Contribution to journalArticleResearchpeer-review

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