Topic modeling in marketing: recent advances and research opportunities

Martin Reisenbichler () and Thomas Reutterer ()
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Martin Reisenbichler: Institute for Service Marketing and Tourism, Vienna University of Economics and Business
Thomas Reutterer: Institute for Service Marketing and Tourism, Vienna University of Economics and Business

Journal of Business Economics, 2019, vol. 89, issue 3, No 4, 327-356

Abstract: Abstract Using a probabilistic approach for exploring latent patterns in high-dimensional co-occurrence data, topic models offer researchers a flexible and open framework for soft-clustering large data sets. In recent years, there has been a growing interest among marketing scholars and practitioners to adopt topic models in various marketing application domains. However, to this date, there is no comprehensive overview of this rapidly evolving field. By analyzing a set of 61 published papers along with conceptual contributions, we systematically review this highly heterogeneous area of research. In doing so, we characterize extant contributions employing topic models in marketing along the dimensions data structures and retrieval of input data, implementation and extensions of basic topic models, and model performance evaluation. Our findings confirm that there is considerable progress done in various marketing sub-areas. However, there is still scope for promising future research, in particular with respect to integrating multiple, dynamic data sources, including time-varying covariates and the combination of exploratory topic models with powerful predictive marketing models.

Keywords: LDA; Machine learning; Marketing research; Topic modeling (search for similar items in EconPapers)
JEL-codes: C00 M30 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (17)

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DOI: 10.1007/s11573-018-0915-7

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