TY - JOUR
T1 - Smart literature review
T2 - a practical topic modelling approach to exploratory literature review
AU - Asmussen, Claus Boye
AU - Møller, Charles
PY - 2019/10/19
Y1 - 2019/10/19
N2 - 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.
AB - 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.
KW - Supply Chain Managment
KW - Latent Dirichlet Allocation
KW - Topic modelling
KW - Automatic literature review
UR - http://www.scopus.com/inward/record.url?scp=85073674481&partnerID=8YFLogxK
U2 - 10.1186/s40537-019-0255-7
DO - 10.1186/s40537-019-0255-7
M3 - Journal article
AN - SCOPUS:85073674481
SN - 2196-1115
VL - 6
SP - 1
EP - 18
JO - Journal of Big Data
JF - Journal of Big Data
IS - 1
M1 - 93
ER -