Lefebvre, C., Glanville, J., Wieland, L.Prospective comparison of search strategies for systematic reviews: An objective approach yielded higher sensitivity than a conceptual one. Hausner E, Guddat C, Hermanns T, Lampert U, Waffenschmidt S.Use of text mining tools in the development of search strategies – comparison of different approaches. Hausner E, Knelangen M, Waffenschmidt S.Do simple text mining tools have anything to offer Embase users? 2016 [Available from.
Text mining in search strategy development Suggested references Programming tools such as the tm package in R or quanteda allow for much more flexibility than some of the tools covered here, but they are also much more difficult to use if one is not accustomed to programming. Some of the tools listed allow for customization of these procedures, while some are preconfigured. Preprocessing includes data cleaning and normalization techniques such as: Text mining, like data science in general, also involves a great deal of preprocessing, which tools may or may not handle. Decisions about cutoffs for high frequency terms, for example, and calculations to establish high frequencies require somewhat large sets of relevant references (which can be derived based on the included studies of relevant systematic reviews, for example) as well as a population set of random records against which one can test whether a term is high frequency across documents in general (for example, words that are high-frequency due to common check tags such as 'human') or in the relevant documents only. Using text mining techniques to increase the objectivity of search strategies requires a more sophisticated use of tools that librarians or other searchers may or may not be prepared to implement. Developing objectively derived search strategies.Searching and screening within an integrated system.Assisting in the translation of search strategies from one database and/or platform to another.Improving the sensitivity of searches (i.e., the proportion of relevant studies retrieved by the search over the total number of relevant studies in the database) by identifying additional search terms (validating this requires the development of a gold standard/quasi-gold standard/reference set, which is often used when developing search filters or hedges).Improving the precision of searches (i.e., the proportion of retrieved records that are relevant), for example, by identifying more precise phrasal terms instead of using single-word terms in a search.Stansfield, O'Mara-Eves, and Thomas (2017) report five ways in which text mining tools can assist in search strategy development:
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