Text into numbers: Can marketers benefit from unstructured data?

Barry Keating

Applied Marketing Analytics: The Peer-Reviewed Journal, 2016, vol. 2, issue 2, 111-120

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.

Keywords: text mining; target leakage; dimension reduction; natural language processing; k Nearest Neighbor (search for similar items in EconPapers)
JEL-codes: M3 (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:aza:ama000:y:2016:v:2:i:2:p:111-120

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