Extending monitoring methods to textual data: a research agenda

Extending monitoring methods to textual data: a research agenda Textual data has become increasingly common in business analytic data sets. While concept-based text mining offers a method of extracting meaningful information from text data, methods for monitoring of customer perceptions of business processes and products that are discussed in customer-generated documents are not immediately available. We explore the results of two text-mining algorithms and review issues observed in the data that affect uploading the results onto a newly proposed methodological monitoring platform analogous to statistical process control charts. Finally, we discuss several topics for future research in text mining. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

Extending monitoring methods to textual data: a research agenda

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Publisher
Springer Netherlands
Copyright
Copyright © 2013 by Springer Science+Business Media Dordrecht
Subject
Social Sciences, general; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
D.O.I.
10.1007/s11135-013-9891-8
Publisher site
See Article on Publisher Site

Abstract

Textual data has become increasingly common in business analytic data sets. While concept-based text mining offers a method of extracting meaningful information from text data, methods for monitoring of customer perceptions of business processes and products that are discussed in customer-generated documents are not immediately available. We explore the results of two text-mining algorithms and review issues observed in the data that affect uploading the results onto a newly proposed methodological monitoring platform analogous to statistical process control charts. Finally, we discuss several topics for future research in text mining.

Journal

Quality & QuantitySpringer Journals

Published: Jul 6, 2013

References

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