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Towards an Integrative Approach for Automated Literature Reviews Using Machine Learning

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Title:Towards an Integrative Approach for Automated Literature Reviews Using Machine Learning
Authors:Tauchert, Christoph
Bender, Marco
Mesbah, Neda
Buxmann, Peter
Keywords:Text Analytics
lda
literature review
text mining
Date Issued:07 Jan 2020
Abstract:Due to a huge amount of scientific publications which are mostly stored as unstructured data, complexity and workload of the fundamental process of literature reviews increase constantly. Based on previous literature, we develop an artifact that partially automates the literature review process from collecting articles up to their evaluation. This artifact uses a custom crawler, the word2vec algorithm, LDA topic modeling, rapid automatic keyword extraction, and agglomerative hierarchical clustering to enable the automatic acquisition, processing, and clustering of relevant literature and subsequent graphical presentation of the results using illustrations such as dendrograms. Moreover, the artifact provides information on which topics each cluster addresses and which keywords they contain. We evaluate our artifact based on an exemplary set of 308 publications. Our findings indicate that the developed artifact delivers better results than known previous approaches and can be a helpful tool to support researchers in conducting literature reviews.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/63834
ISBN:978-0-9981331-3-3
DOI:10.24251/HICSS.2020.095
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Text Analytics


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