How to conduct efficient and objective literature reviews using natural language processing: A step‐by‐step guide for marketing researchers

Abstract

AbstractLiterature reviews are crucial for attaining a full understanding of the key topics and latest trends in research and instrumental in identifying important research gaps. Unfortunately, conducting literature reviews can be time‐consuming, and the outcomes are frequently subjective. Hence, to address such limitations, we detail an alternative, recent approach to conducting literature reviews. In this research, we outline the steps involved in conducting a literature review via natural language processing. Specifically, we illustrate how to (1) select relevant papers using term frequency‐inverse document frequency and (2) perform topic modeling analysis through latent Dirichlet allocation to identify key research topics. This study and the associated ready‐to‐use Python code provide researchers, including those in consumer behavior, with detailed guidance on how to use natural language processing in their literature reviews.