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 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.

Automated summary

This article investigates different NLP models and their applications in marketing, highlighting the advantages and disadvantages of these models and the conditions under which they are appropriate. The latest neural autoencoder NLP models are introduced, and an empirical comparison of these models and statistical NLP models is provided. The insights from the comparison are discussed, and guidelines for researchers are offered.