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

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 o...

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Main Authors: Claus Boye Asmussen, Charles Møller
Format: Article
Language:English
Published: SpringerOpen 2019-10-01
Series:Journal of Big Data
Subjects:
Online Access:http://link.springer.com/article/10.1186/s40537-019-0255-7
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spelling doaj-a726506ef2b34ec78715dc443253020e2020-11-25T02:46:40ZengSpringerOpenJournal of Big Data2196-11152019-10-016111810.1186/s40537-019-0255-7Smart literature review: a practical topic modelling approach to exploratory literature reviewClaus Boye Asmussen0Charles Møller1Department of Materials and Production, Center for Industrial Production, Aalborg UniversityDepartment of Materials and Production, Center for Industrial Production, Aalborg UniversityAbstract 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.http://link.springer.com/article/10.1186/s40537-019-0255-7Supply chain managementLatent Dirichlet AllocationTopic modellingAutomatic literature review
collection DOAJ
language English
format Article
sources DOAJ
author Claus Boye Asmussen
Charles Møller
spellingShingle Claus Boye Asmussen
Charles Møller
Smart literature review: a practical topic modelling approach to exploratory literature review
Journal of Big Data
Supply chain management
Latent Dirichlet Allocation
Topic modelling
Automatic literature review
author_facet Claus Boye Asmussen
Charles Møller
author_sort Claus Boye Asmussen
title Smart literature review: a practical topic modelling approach to exploratory literature review
title_short Smart literature review: a practical topic modelling approach to exploratory literature review
title_full Smart literature review: a practical topic modelling approach to exploratory literature review
title_fullStr Smart literature review: a practical topic modelling approach to exploratory literature review
title_full_unstemmed Smart literature review: a practical topic modelling approach to exploratory literature review
title_sort smart literature review: a practical topic modelling approach to exploratory literature review
publisher SpringerOpen
series Journal of Big Data
issn 2196-1115
publishDate 2019-10-01
description 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.
topic Supply chain management
Latent Dirichlet Allocation
Topic modelling
Automatic literature review
url http://link.springer.com/article/10.1186/s40537-019-0255-7
work_keys_str_mv AT clausboyeasmussen smartliteraturereviewapracticaltopicmodellingapproachtoexploratoryliteraturereview
AT charlesmøller smartliteraturereviewapracticaltopicmodellingapproachtoexploratoryliteraturereview
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