Topic modelling for open-ended survey responses

Song Chen, Chad Vidden, Nicole Nelson and Marco Vriens
Additional contact information
Chad Vidden: Associate Professor of Mathematics and Statistics, University of Wisconsin-La Crosse, USA
Marco Vriens: Chief Executive Officer, Kwantum, USA

Applied Marketing Analytics: The Peer-Reviewed Journal, 2018, vol. 4, issue 1, 53-62

Abstract: Due to the availability of massive amounts of text data, both from online (Twitter, Facebook, online forums, etc) and offline open-ended survey questions, text analytics is growing in marketing research and analytics. Most companies are now using open-ended survey questions to solicit customer opinions on any number of topics (eg ‘how can we improve our service?’). With large sample sizes, however, the task of collating this information manually is practically impossible. This paper describes an end-to-end process to extract insight from text survey data via topic modelling. A case study from a Fortune 500 firm is used to illustrate the process.

Keywords: text analysis; open-ended questions; topic modelling; latent Dirichlet allocation; natural language processing (search for similar items in EconPapers)
JEL-codes: M3 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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