Topic modeling in marketing: recent advances and research opportunities View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-04

AUTHORS

Martin Reisenbichler, Thomas Reutterer

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. More... »

PAGES

327-356

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11573-018-0915-7

DOI

http://dx.doi.org/10.1007/s11573-018-0915-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1107038091


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Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11573-018-0915-7'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11573-018-0915-7'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11573-018-0915-7'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11573-018-0915-7'


 

This table displays all metadata directly associated to this object as RDF triples.

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