A clustering approach for topic filtering within systematic literature reviews
ABSTRACT: 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 methodologi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2020-01-01
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| Series: | MethodsX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016120300510 |
| _version_ | 1818950673244356608 |
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| author | Tim Weißer Till Saßmannshausen Dennis Ohrndorf Peter Burggräf Johannes Wagner |
| author_facet | Tim Weißer Till Saßmannshausen Dennis Ohrndorf Peter Burggräf Johannes Wagner |
| author_sort | Tim Weißer |
| collection | DOAJ |
| description | ABSTRACT: 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. |
| first_indexed | 2024-12-20T09:22:19Z |
| format | Article |
| id | doaj.art-aae3b51f15834dd985ae4d9a9833ce26 |
| institution | Directory Open Access Journal |
| issn | 2215-0161 |
| language | English |
| last_indexed | 2024-12-20T09:22:19Z |
| publishDate | 2020-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj.art-aae3b51f15834dd985ae4d9a9833ce262022-12-21T19:45:16ZengElsevierMethodsX2215-01612020-01-017100831A clustering approach for topic filtering within systematic literature reviewsTim Weißer0Till Saßmannshausen1Dennis Ohrndorf2Peter Burggräf3Johannes Wagner4Corresponding author:.; Chair for International Production Engineering and Management, University of SiegenChair for International Production Engineering and Management, University of SiegenChair for International Production Engineering and Management, University of SiegenChair for International Production Engineering and Management, University of SiegenChair for International Production Engineering and Management, University of SiegenABSTRACT: 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.http://www.sciencedirect.com/science/article/pii/S2215016120300510Systematic literature reviewLiterature filteringClustering |
| spellingShingle | Tim Weißer Till Saßmannshausen Dennis Ohrndorf Peter Burggräf Johannes Wagner A clustering approach for topic filtering within systematic literature reviews MethodsX Systematic literature review Literature filtering Clustering |
| title | A clustering approach for topic filtering within systematic literature reviews |
| title_full | A clustering approach for topic filtering within systematic literature reviews |
| title_fullStr | A clustering approach for topic filtering within systematic literature reviews |
| title_full_unstemmed | A clustering approach for topic filtering within systematic literature reviews |
| title_short | A clustering approach for topic filtering within systematic literature reviews |
| title_sort | clustering approach for topic filtering within systematic literature reviews |
| topic | Systematic literature review Literature filtering Clustering |
| url | http://www.sciencedirect.com/science/article/pii/S2215016120300510 |
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