COVID-19 Literature Topic-Based Search via Hierarchical NMF
This provides a domain-specific tool for researchers to efficiently search COVID-19 literature, but it is incremental as it applies an existing method to new data.
The researchers tackled the problem of organizing COVID-19 scientific literature by applying hierarchical nonnegative matrix factorization to create a topic-based tree structure, resulting in the discovery of 8 major topics and 52 subtopics and the development of an interactive search tool.
A dataset of COVID-19-related scientific literature is compiled, combining the articles from several online libraries and selecting those with open access and full text available. Then, hierarchical nonnegative matrix factorization is used to organize literature related to the novel coronavirus into a tree structure that allows researchers to search for relevant literature based on detected topics. We discover eight major latent topics and 52 granular subtopics in the body of literature, related to vaccines, genetic structure and modeling of the disease and patient studies, as well as related diseases and virology. In order that our tool may help current researchers, an interactive website is created that organizes available literature using this hierarchical structure.