Efficient Methods for Sampling Responses from Large-Scale Qualitative Data
Abbreviated Journal Title
Mark. Sci.
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; Business
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.
Journal Title
Marketing Science
Volume
30
Issue/Number
3
Publication Date
1-1-2011
Document Type
Article
Language
English
First Page
532
Last Page
549
WOS Identifier
ISSN
0732-2399
Recommended Citation
"Efficient Methods for Sampling Responses from Large-Scale Qualitative Data" (2011). Faculty Bibliography 2010s. 1928.
https://stars.library.ucf.edu/facultybib2010/1928
Comments
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