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Text mining for search term development in systematic reviewing: A discussion of some methods and challenges

Stansfield, C; O'Mara-Eves, A; Thomas, J; (2017) Text mining for search term development in systematic reviewing: A discussion of some methods and challenges. Reseach Synthesis Methods , 8 (3) pp. 355-365. 10.1002/jrsm.1250. Green open access

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Abstract

Using text mining to aid the development of database search strings for topics described by diverse terminology has potential benefits for systematic reviews; however, methods and tools for accomplishing this are poorly covered in the research methods literature. We briefly review the literature on applications of text mining for search term development for systematic reviewing. We found that the tools can be used in 5 overarching ways: improving the precision of searches; identifying search terms to improve search sensitivity; aiding the translation of search strategies across databases; searching and screening within an integrated system; and developing objectively derived search strategies. Using a case study and selected examples, we then reflect on the utility of certain technologies (term frequency-inverse document frequency and Termine, term frequency, and clustering) in improving the precision and sensitivity of searches. Challenges in using these tools are discussed. The utility of these tools is influenced by the different capabilities of the tools, the way the tools are used, and the text that is analysed. Increased awareness of how the tools perform facilitates the further development of methods for their use in systematic reviews.

Type: Article
Title: Text mining for search term development in systematic reviewing: A discussion of some methods and challenges
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1002/jrsm.1250
Publisher version: http://doi.org/10.1002/jrsm.1250
Language: English
Additional information: Copyright © 2017 John Wiley & Sons, Ltd. This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Clustering, information retrieval, systematic search, text mining
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Education
UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education
UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education > IOE - Social Research Institute
URI: https://discovery-pp.ucl.ac.uk/id/eprint/1562394
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