Efficient Methods for Sampling Responses from Large-Scale Qualitative Data

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.

• 저널Marketing Science · 30(3) · 532–549
• 토픽Digital Marketing and Social Media · Sociology and Political Science
• DOI10.1287/mksc.1100.0632
• 저자Surendra N. Singh, Steve Hillmer, Ze Wang