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Extending monitoring methods to textual data: a research agenda

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  • Triss Ashton
  • Nicholas Evangelopoulos
  • Victor Prybutok

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. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Triss Ashton & Nicholas Evangelopoulos & Victor Prybutok, 2014. "Extending monitoring methods to textual data: a research agenda," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(4), pages 2277-2294, July.
    • Handle: RePEc:spr:qualqt:v:48:y:2014:i:4:p:2277-2294
    DOI: 10.1007/s11135-013-9891-8
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    Cited by:

    1. Jaroslav Ráček & Jan Ministr, 2014. "Tools for Automatic Recognition of Persons and their Relationships in Unstructured Data [Nástroje pro automatické rozpoznávání entit a jejich vztahů v nestrukturovaných textech]," Acta Informatica Pragensia, Prague University of Economics and Business, vol. 2014(3), pages 280-287.
    2. Zavala, Araceli & Ramirez-Marquez, Jose Emmanuel, 2019. "Visual analytics for identifying product disruptions and effects via social media," International Journal of Production Economics, Elsevier, vol. 208(C), pages 544-559.

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