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

By Claus Boye Asmussen, Charles Møller. Journal of Big Data, 2019

384 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, General Social Sciences

View Paper: https://www.semanticscholar.org/paper/0c358732e3965e363024e072779b9d97617a8558