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Extending monitoring methods to textual data: a research agenda

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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.

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Authors and Affiliations

  1. College of Business, The University of Texas-Pan American, 1201 W. University Dr., Edinburg, TX, 78539, USA

    Triss Ashton

  2. College of Business, University of North Texas, 1155 Union Circle # 311160, Denton, TX, 76203-5017, USA

    Nicholas Evangelopoulos & Victor Prybutok

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  1. Triss Ashton
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  2. Nicholas Evangelopoulos
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  3. Victor Prybutok
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Correspondence to Triss Ashton.

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Ashton, T., Evangelopoulos, N. & Prybutok, V. Extending monitoring methods to textual data: a research agenda. Qual Quant 48, 2277–2294 (2014). https://doi.org/10.1007/s11135-013-9891-8

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