Unsupervised Topic Detection in document collections: an application in marketing and business journals

Decker R, Scholz S (2007)
International Journal of Business Intelligence and Data Mining 2(3): 347-364.

Journal Article | Published | English

No fulltext has been uploaded

Abstract
The rapid increase of publications in marketing and related areas increasingly hampers the realisation of a general idea of what is ‘hot’ in the respective fields of interest. Topic Detection (TD), based on unsupervised text clustering, is a promising approach to tackle this problem. We introduce a new methodology that facilitates the determination of the number of topics discussed in a given text collection. By applying this approach to a text corpus which includes 12 international marketing and business journals we identify hot spots in marketing science. The approach may help both scientists and practitioners to systematically discover topics in digital information environments, as provided by the internet for instance.
Publishing Year
ISSN
eISSN
PUB-ID

Cite this

Decker R, Scholz S. Unsupervised Topic Detection in document collections: an application in marketing and business journals. International Journal of Business Intelligence and Data Mining. 2007;2(3):347-364.
Decker, R., & Scholz, S. (2007). Unsupervised Topic Detection in document collections: an application in marketing and business journals. International Journal of Business Intelligence and Data Mining, 2(3), 347-364.
Decker, R., and Scholz, S. (2007). Unsupervised Topic Detection in document collections: an application in marketing and business journals. International Journal of Business Intelligence and Data Mining 2, 347-364.
Decker, R., & Scholz, S., 2007. Unsupervised Topic Detection in document collections: an application in marketing and business journals. International Journal of Business Intelligence and Data Mining, 2(3), p 347-364.
R. Decker and S. Scholz, “Unsupervised Topic Detection in document collections: an application in marketing and business journals”, International Journal of Business Intelligence and Data Mining, vol. 2, 2007, pp. 347-364.
Decker, R., Scholz, S.: Unsupervised Topic Detection in document collections: an application in marketing and business journals. International Journal of Business Intelligence and Data Mining. 2, 347-364 (2007).
Decker, Reinhold, and Scholz, Sören. “Unsupervised Topic Detection in document collections: an application in marketing and business journals”. International Journal of Business Intelligence and Data Mining 2.3 (2007): 347-364.
This data publication is cited in the following publications:
This publication cites the following data publications:

Export

0 Marked Publications

Open Data PUB

Search this title in

Google Scholar