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.
Zeitschriftenaufsatz
| Veröffentlicht | Englisch
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Einrichtung
Abstract / Bemerkung
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.
Stichworte
environmental scanning;
marketing science;
text mining;
topic detection;
unsupervised clustering.;
TD;
business intelligence
Erscheinungsjahr
2007
Zeitschriftentitel
International Journal of Business Intelligence and Data Mining
Band
2
Ausgabe
3
Seite(n)
347-364
ISSN
1743-8187
eISSN
1743-8195
Page URI
https://pub.uni-bielefeld.de/record/2495054
Zitieren
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. https://doi.org/10.1504/ijbidm.2007.015489
Decker, Reinhold, and Scholz, Sören. 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.
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