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Smart literature review: a practical topic modelling approach to exploratory literature review
Journal of Big Data ( IF 8.1 ) Pub Date : 2019-10-19 , DOI: 10.1186/s40537-019-0255-7
Claus Boye Asmussen , Charles Møller

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.

中文翻译:

智能文献复习:探索性文献复习的实用主题建模方法

随着机器学习方法的技术和发展的成熟,手工探索性文献综述应该已经成为过去。使用机器学习方法的学习曲线正在迅速下降,为所有研究人员提供了新的可能性。提出了一个框架,说明如何在大量论文上使用主题模型进行探索性文献综述以及如何将其用于完整文献综述。本文的目的是通过在案例上展示分步框架并共享代码模板,使研究人员可以使用主题建模。该框架包括三个步骤:预处理,主题建模和后处理,其中使用了主题模型Latent Dirichlet Allocation。该框架可以透明,可靠,更快,
更新日期:2019-10-19
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