Pugliese, S., Giannetti, V. orcid.org/0000-0001-5703-789X and Banerjee, S. (2023) How to conduct efficient and objective literature reviews using natural language processing: A step-by-step guide for marketing researchers. Psychology and Marketing, 41 (2). pp. 427-441. ISSN 0742-6046
Abstract
Literature 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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Authors. Psychology & Marketing published by Wiley Periodicals LLC. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. |
Keywords: | literature reviews; marketing methods; marketing research; natural language processing; topic modeling |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Business (Leeds) > Marketing Division (LUBS) |
Depositing User: | Symplectic Publications |
Date Deposited: | 19 Oct 2023 10:35 |
Last Modified: | 26 Jan 2024 14:58 |
Status: | Published |
Publisher: | Wiley |
Identification Number: | 10.1002/mar.21931 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:204350 |