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Extending monitoring methods to textual data: a research agenda


  • Triss Ashton


  • Nicholas Evangelopoulos


  • Victor Prybutok



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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    References listed on IDEAS

    1. Grün, Bettina & Hornik, Kurt, 2011. "topicmodels: An R Package for Fitting Topic Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i13).
    2. Peter Swanborn, 1996. "A common base for quality control criteria in quantitative and qualitative research," Quality & Quantity: International Journal of Methodology, Springer, vol. 30(1), pages 19-35, February.
    3. Ding, Chris & Li, Tao & Peng, Wei, 2008. "On the equivalence between Non-negative Matrix Factorization and Probabilistic Latent Semantic Indexing," Computational Statistics & Data Analysis, Elsevier, vol. 52(8), pages 3913-3927, April.
    4. C. Poortman & K. Schildkamp, 2012. "Alternative quality standards in qualitative research?," Quality & Quantity: International Journal of Methodology, Springer, vol. 46(6), pages 1727-1751, October.
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    Cited by:

    1. Jaroslav Ráček & Jan Ministr, 2014. "Tools for Automatic Recognition of Persons and their Relationships in Unstructured Data," Acta Informatica Pragensia, University of Economics, Prague, vol. 2014(3), pages 280-287.


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