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Topic modelling for open-ended survey responses

Author

Listed:
  • Chen, Song
  • Vidden, Chad

    (Associate Professor of Mathematics and Statistics, University of Wisconsin-La Crosse, USA)

  • Nelson, Nicole
  • Vriens, Marco

    (Chief Executive Officer, Kwantum, USA)

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.

Suggested Citation

  • Chen, Song & Vidden, Chad & Nelson, Nicole & Vriens, Marco, 2018. "Topic modelling for open-ended survey responses," Applied Marketing Analytics: The Peer-Reviewed Journal, Henry Stewart Publications, vol. 4(1), pages 53-62, April.
    • Handle: RePEc:aza:ama000:y:2018:v:4:i:1:p:53-62
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    Cited by:

    1. Iago S. Muraro & Kjerstin Thorson & Patricia T. Huddleston, 2023. "Spurring and sustaining online consumer activism: the role of cause support and brand relationship in microlevel action frames," Journal of Brand Management, Palgrave Macmillan, vol. 30(5), pages 461-477, September.

    More about this item

    Keywords

    text analysis; open-ended questions; topic modelling; latent Dirichlet allocation; natural language processing;
    All these keywords.

    JEL classification:

    • M3 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising

    Statistics

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