TY  - JOUR
AB  - 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.
AU  - Burggräf, Peter
AU  - Weißer, Tim
AU  - Saßmannshausen, Till Moritz
AU  - Ohrndorf, Dennis
AU  - Wagner, Johannes
DA  - 2020
DO  - 10.25819/ubsi/9933
KW  - Literaturrecherche [vorwiegend nach inhaltlichen Kriterien]
KW  - Systematische Literaturrecherche
KW  - Natürliche Sprachverarbeitung
KW  - Systematic literature review
KW  - Natural language processing
LA  - eng
PY  - 2020
TI  - A clustering approach for topic filtering within systematic literature reviews
UR  - https://nbn-resolving.org/urn:nbn:de:hbz:467-19213
Y2  - 2024-12-26T20:06:38
ER  -