Background: Systematic reviews increasingly seek to address issues beyond the relative effectiveness of interventions. This can be challenging when the concepts to be searched for are poorly indexed or difficult to describe. Using two case studies, one focusing on the ethics and acceptability of an intervention and the other focusing on economic evaluations, an approach to enhancing search strategy development is outlined.
Objectives: To use text mining principles to examine metadata and full-text papers to discover terms and concepts not discovered in standard search strategy development.
Methods: Using known relevant papers, metadata from bibliographic databases and full-text papers alongside freely available word cloud generators, both MeSH and free-text terms were examined to highlight undiscovered descriptors for use in a systematic review search strategy. Initially text was taken from the bibliographic database metadata (MeSH and keywords), title and abstract, and full-text papers. These sets of text were then examined separately to determine the most useful texts to explore through word clouds which highlight frequency of terms in their display.
Results: The use of word clouds to discover ‘hidden’ MeSH and keywords for search strategies and filters works well. In both case studies its use added to the final search strategies in both projects. The inherent simplicity of word clouds makes this an accessible method of discovery, however some types of text yielded better results than others, due to the limitations of word cloud software.
Conclusions: This method is easily accessible to anyone wishing to develop search strategies for hard-to-define or unfamiliar topics as it does not require large volumes of texts nor expertise in programming. This method of discovery can be utilised alongside standard approaches to provide a richer set of terms with minimal additional effort.