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Text into numbers: Can marketers benefit from unstructured data?

Author

Listed:
  • Keating, Barry P.

    (University of Notre Dame, USA)

Abstract

Since much of the data marketers encounter is in the form of text, using predictive analytics techniques requires that the text be in some manner transformed into data that can be effectively used by standard data mining techniques. How exactly does this ‘transformation’ take place? Once transformed, how are the resulting data used in an analytics algorithm? This paper seeks to answer these two questions and to present an example of the process described. In addition, an important and common error that is often encountered in text mining is explained.

Suggested Citation

  • Keating, Barry P., 2016. "Text into numbers: Can marketers benefit from unstructured data?," Applied Marketing Analytics: The Peer-Reviewed Journal, Henry Stewart Publications, vol. 2(2), pages 111-120, June.
    • Handle: RePEc:aza:ama000:y:2016:v:2:i:2:p:111-120
    as

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    More about this item

    Keywords

    text mining; target leakage; dimension reduction; natural language processing; k Nearest Neighbor;
    All these keywords.

    JEL classification:

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

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