Smart literature review: a practical topic modelling approach to exploratory literature review

Claus Boye Asmussen, Charles Møller

Research output: Contribution to journalJournal articleResearchpeer-review

67 Citations (Scopus)
191 Downloads (Pure)


Manual exploratory literature reviews should be a thing of the past, as technology and development of machine learning methods have matured. The learning curve for using machine learning methods is rapidly declining, enabling new possibilities for all researchers. A framework is presented on how to use topic modelling on a large collection of papers for an exploratory literature review and how that can be used for a full literature review. The aim of the paper is to enable the use of topic modelling for researchers by presenting a step-by-step framework on a case and sharing a code template. The framework consists of three steps; pre-processing, topic modelling, and post-processing, where the topic model Latent Dirichlet Allocation is used. The framework enables huge amounts of papers to be reviewed in a transparent, reliable, faster, and reproducible way.
Translated title of the contributionSmart Literatur Review: En praktisk topic model tilgang til eksplorative literature review
Original languageEnglish
Article number93
JournalJournal of Big Data
Issue number1
Pages (from-to)1-18
Number of pages18
Publication statusPublished - 19 Oct 2019

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