Journal Of Big Data· 2019Q1
Smart literature review: a practical topic modelling approach to exploratory literature review
- 447citations
- Q1SCImago
- 2019year
Short summary
A novel three-step framework (pre-processing, topic modelling with Latent Dirichlet Allocation, post-processing) enables researchers to conduct exploratory literature reviews on large paper collections transparently, reliably, faster, and reproducibly.
AI-generated from the title and abstract; the full text is not read.
Key points
- Presents a three-step framework for exploratory literature reviews using topic modelling.
- Utilizes Latent Dirichlet Allocation (LDA) as the core topic modelling technique.
- The framework aims for transparency, reliability, speed, and reproducibility in reviewing large paper collections.
- Includes a code template to assist researchers in implementing the approach.
AI-generated from the title and abstract; the full text is not read.
Abstract
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.
The authors' abstract, as published at the source. Journal Of Big Data, 2019 · DOI ↗
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Field: General Social Sciences
General Social SciencesSocial Sciences