We pulled 18,000 comments on the top ten productivity apps. We looked closely at 5,000 of those comments and ran a sentiment analysis tool [designed to identify and extract subjective information] that showed us the requirements that will go into the next generation of each app.
Goul or Goul's project group used text mining to start the product development process. The use of this analytical tool to sift through gigabytes or terabytes of data reflects a lot of knowledge.
Text mining tool, which is similar to data mining, has become a necessary part of the analytical toolkit because of the increasing number of comment sections on blogs and electronically delivered customer satisfaction surveys. To make sense of these comments, a researcher grabs a text mining tool to generate market intelligence; specifically, from customers. The responsiveness, as reflected by Goul's money story, could be an improved or new market offering.