Efficient Methods for Sampling Responses from Large-Scale Qualitative Data

Surendra N. Singh (), Steve Hillmer () and Ze Wang ()
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Surendra N. Singh: School of Business, University of Kansas, Lawrence, Kansas 66045
Steve Hillmer: School of Business, University of Kansas, Lawrence, Kansas 66045
Ze Wang: College of Business Administration, University of Central Florida, Orlando, Florida 32816

Marketing Science, 2011, vol. 30, issue 3, 532-549

Abstract: The World Wide Web contains a vast corpus of consumer-generated content that holds invaluable insights for improving the product and service offerings of firms. Yet the typical method for extracting diagnostic information from online content--text mining--has limitations. As a starting point, we propose analyzing a sample of comments before initiating text mining. Using a combination of real data and simulations, we demonstrate that a sampling procedure that selects respondents whose comments contain a large amount of information is superior to the two most popular sampling methods--simple random sampling and stratified random sampling---in gaining insights from the data. In addition, we derive a method that determines the probability of observing diagnostic information repeated a specific number of times in the population, which will enable managers to base sample size decisions on the trade-off between obtaining additional diagnostic information and the added expense of a larger sample. We provide an illustration of one of the methods using a real data set from a website containing qualitative comments about staying at a hotel and demonstrate how sampling qualitative comments can be a useful first step in text mining.

Keywords: consumer-generated media; consumer-generated content; customer feedback on the Web; text mining; qualitative comments; large-scale qualitative data sets; sampling open-ended questions (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (4)

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