An overview and empirical comparison of natural language processing (NLP) models and an introduction to and empirical application of autoencoder models in marketing

With artificial intelligence permeating conversations and marketing interactions through digital technologies and media, machine learning models, in particular, natural language processing (NLP) models, have surged in popularity for analyzing unstructured data in marketing. Yet, we do not fully unde...

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Veröffentlicht in:Journal of the Academy of Marketing Science Jg. 50; H. 6; S. 1324 - 1350
Hauptverfasser: Shankar, Venkatesh, Parsana, Sohil
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.11.2022
Springer
Springer Nature B.V
Schlagworte:
Business and Management
Comparative analysis
Computational linguistics
Language processing
Machine learning
Marketing
Methodological Paper
Methods
Natural language interfaces
Natural language processing
Social Sciences
United States
Topic modeling
Text mining
Text analysis
Autoencoder
Machine learning
Natural language processing (NLP)
Artificial intelligence
Unstructured data
Marketing
ISSN:0092-0703, 1552-7824
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Zusammenfassung:With artificial intelligence permeating conversations and marketing interactions through digital technologies and media, machine learning models, in particular, natural language processing (NLP) models, have surged in popularity for analyzing unstructured data in marketing. Yet, we do not fully understand which NLP models are appropriate for which marketing applications and what insights can be best derived from them. We review different NLP models and their applications in marketing. We layout the advantages and disadvantages of these models and highlight the conditions under which different models are appropriate in the marketing context. We introduce the latest neural autoencoder NLP models, demonstrate these models to analyze new product announcements and news articles, and provide an empirical comparison of the different autoencoder models along with the statistical NLP models. We discuss the insights from the comparison and offer guidelines for researchers. We outline future extensions of NLP models in marketing.
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ISSN:0092-0703
1552-7824
DOI:10.1007/s11747-022-00840-3