Abstract
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.
Highlights
Manual exploratory literature reviews are soon to be outdated
Manual exploratory literature reviews should be a thing of the past, as technology and development of machine learning methods have matured
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
Summary
Manual exploratory literature reviews are soon to be outdated It is a time-consuming process, with limited processing power, resulting in a low number of papers analysed. The use of coding sheets has especially been used in social science, where trained humans have created impressive data collections, such as the Policy Agendas Project and the Congressional Bills Project in American politics [30]. These methods, have a high upfront cost of time, requiring a prior understanding where papers are grouped by categories based on pre-existing knowledge.
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