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

Authors:
Kai R. Larsen;David E. Monarchi;Dirk S. Hovorka;Christopher N. Bailey
Affiliations:
Leeds School of Business, University of Colorado, Boulder, 419 UCB, Boulder, CO 80309, United States;Leeds School of Business, University of Colorado, Boulder, 419 UCB, Boulder, CO 80309, United States;Leeds School of Business, University of Colorado, Boulder, 419 UCB, Boulder, CO 80309, United States;Leeds School of Business, University of Colorado, Boulder, 419 UCB, Boulder, CO 80309, United States
Venue:
Decision Support Systems
Year:
2008

Citing 29
Cited 2

The intellectual development of management information systems, 1972-1982: a co-citation analysis

Management Science
Mapping the intellectual structure of MIS, 1980-1985: a co-citation analysis

MIS Quarterly
An information systems keyword classification scheme

MIS Quarterly
Can the field of MIS be disciplined?

Communications of the ACM
Revisiting DSS implementation research: a meta-analysis of the literature and suggestions for researchers

MIS Quarterly
A comparative analysis of the topic areas of computer science, software engineering, and information systems

Journal of Systems and Software
Analyzing methodological rigor of MIS survey research from 1980–1989

Information and Management
A review of MIS research and disciplinary development

Journal of Management Information Systems
Using linear algebra for intelligent information retrieval

SIAM Review
The sciences of the artificial (3rd ed.)

The sciences of the artificial (3rd ed.)
Computing research programs in the U.S.

Communications of the ACM
Is North American IS research different from European IS research?

ACM SIGMIS Database
An analysis of research in information systems (1981-1997)

Information and Management
The new (1982) Computing Reviews classification system—final version

Communications of the ACM
The proposed new Computing Reviews classification scheme

Communications of the ACM
Is information system a science? an inquiry into the nature of the information systems discipline

ACM SIGMIS Database
On site: global perceptions of IS journals

Communications of the ACM
Forums for information systems scholars: III

Information and Management
Research Commentary. Academic Rewards for Teaching, Research, and Service: Data and Discourse

Information Systems Research
An analysis of research in computing disciplines

Communications of the ACM - Wireless sensor networks
Examining differences across journal rankings

Communications of the ACM - Medical image modeling
Editorial Notes

Information Systems Research
Research in Information Systems: An Empirical Study of Diversity in the Discipline and Its Journals

Journal of Management Information Systems
Improving customer complaint management by automatic email classification using linguistic style features as predictors

Decision Support Systems
A Latent Semantic Indexing-based approach to multilingual document clustering

Decision Support Systems
Technology and institutions: what can research on information technology and research on organizations learn from each other?

MIS Quarterly
The identity crisis within the is discipline: defining and communicating the discipline's core properties

MIS Quarterly
Design science in information systems research

MIS Quarterly
Reach and grasp

MIS Quarterly

Exploring determinants of voting for the "helpfulness" of online user reviews: A text mining approach

Decision Support Systems
The social life of categories: An empirical study of term categorization

Applied Ontology - Ontologies and Terminologies: Continuum or Dichotomy?

Quantified Score

Hi-index	0.00

Visualization

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.