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Dokument Type: Article
metadata.dc.title: A clustering approach for topic filtering within systematic literature reviews
Authors: Burggräf, Peter 
Weißer, Tim 
Saßmannshausen, Till Moritz 
Ohrndorf, Dennis 
Wagner, Johannes 
Institute: Fakultät IV - Naturwissenschaftlich-Technische Fakultät 
Free keywords: Systematische Literaturrecherche, Natürliche Sprachverarbeitung, Systematic literature review, Natural language processing
Dewey Decimal Classification: 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
GHBS-Clases: WAS
Issue Date: 2020
Publish Date: 2021
Source: MethodsX ; Volume 7, 100831. -
Within a systematic literature review (SLR), researchers are confronted with vast amounts of articles from scientific databases, which have to be manually evaluated regarding their relevance for a certain field of observation. The evaluation and filtering phase of prevalent SLR methodologies is therefore time consuming and hardly expressible to the intended audience. The proposed method applies natural language processing (NLP) on article meta data and a k-means clustering algorithm to automatically convert large article corpora into a distribution of focal topics. This allows efficient filtering as well as objectifying the process through the discussion of the clustering results. Beyond that, it allows to quickly identify scientific communities and therefore provides an iterative perspective for the so far linear SLR methodology.
• NLP and k-means clustering to filter large article corpora during systematic literature reviews.
• Automated clustering allows filtering very efficiently as well as effectively compared to manual selection.
• Presentation and discussion of the clustering results helps to objectify the nontransparent filtering step in systematic literature reviews.
Finanziert aus dem Open-Access-Publikationsfonds der Universität Siegen für Zeitschriftenartikel
URN: urn:nbn:de:hbz:467-19213
Appears in Collections:Geförderte Open-Access-Publikationen

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