Citation link: http://dx.doi.org/10.25819/ubsi/9933
DC FieldValueLanguage
crisitem.author.orcid0000-0001-9018-9959-
crisitem.author.orcid0000-0002-1061-8686-
crisitem.author.orcid0000-0001-7870-6044-
dc.contributor.authorBurggräf, Peter-
dc.contributor.authorWeißer, Tim-
dc.contributor.authorSaßmannshausen, Till Moritz-
dc.contributor.authorOhrndorf, Dennis-
dc.contributor.authorWagner, Johannes-
dc.date.accessioned2021-06-16T15:07:49Z-
dc.date.available2021-06-16T15:07:49Z-
dc.date.issued2020de
dc.descriptionFinanziert aus dem Open-Access-Publikationsfonds der Universität Siegen für Zeitschriftenartikelde
dc.description.abstractWithin 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.en
dc.identifier.doihttp://dx.doi.org/10.25819/ubsi/9933-
dc.identifier.urihttps://dspace.ub.uni-siegen.de/handle/ubsi/1921-
dc.identifier.urnurn:nbn:de:hbz:467-19213-
dc.language.isoende
dc.sourceMethodsX ; Volume 7, 100831. - https://doi.org/10.1016/j.mex.2020.100831de
dc.subject.ddc620 Ingenieurwissenschaften und zugeordnete Tätigkeitende
dc.subject.otherSystematische Literaturrecherchede
dc.subject.otherNatürliche Sprachverarbeitungde
dc.subject.otherSystematic literature reviewen
dc.subject.otherNatural language processingen
dc.subject.swbLiteraturrecherche [vorwiegend nach inhaltlichen Kriterien]de
dc.subject.swbSprachverarbeitung [Verwendung des gesprochenen Wortes zur Dateneingabe und -ausgabe]de
dc.titleA clustering approach for topic filtering within systematic literature reviewsen
dc.typeArticlede
item.fulltextWith Fulltext-
ubsi.publication.affiliationFakultät IV - Naturwissenschaftlich-Technische Fakultätde
ubsi.source.authorElsevierde
ubsi.source.doi10.1016/j.mex.2020.100831-
ubsi.source.issn2215-0161-
ubsi.source.issued2020de
ubsi.source.issuenumber7de
ubsi.source.linkhttps://www.elsevier.com/de-dede
ubsi.source.pages10de
ubsi.source.placeAmsterdamde
ubsi.source.publisherElsevierde
ubsi.source.titleMethodsXde
ubsi.source.volume2020de
ubsi.subject.ghbsWASde
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