COVID -19 Literature Topic-Based Search via Hierarchical NMF (2020)

Abstract: 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.

## introduction the appearance of the novel sars-cov-2 virus on the global scale has generated demand for rapid research into the virus and the disease it causes, covid-19. however, the literature about coronaviruses such as sars-cov-2 is vast and difficult to sift through. this paper describes an attempt to organize existing literature on coronaviruses, other pandemics, and early research on the current covid-19 outbreak in response to the call to action issued by the white house office of science and technology policy (science and policy, 2020) and posted on the semantic scholar @xcite and @xcite websites. the original dataset posted on that site is augmented by adding articles drawn from other databases in order to make the final interactive organizational structure more robust for researchers. our primary goal is to create a framework for a topic-based search of papers within this dataset that is helpful to those investigating the novel coronavirus, sars-cov-2, and the global covid-19 pandemic. in order to discover the latent topics present in the collection of scholarly articles, as well as to organize them into a hierarchical tree structure that allows for an interactive search, we use a modified hierarchical nonnegative matrix factorization (hnmf) approach. a website foot_0 that allows users to walk through the topic tree based on the top keywords associated with each topic is created using this hierarchical organization of the papers. ## data description the dataset used is compiled from 4 different databases that contain scholarly articles related to covid-19, various coronavirus diseases, other infectious diseases, and epidemiology (scholar, 2020; for disease control and prevention, 2020; national center for biotechnology @xcite . from each of these databases, only articles written in english that have a complete abstract and text body available are included. punctuation and words on the nltk english stopwords list @xcite are removed from the text body and abstract of each article. an initial application of nmf generated a topic consisting primarily of words that, upon further investigation, were found to be part of the copyright and publishing information present at the top of articles primarily drawn from the biorxiv database, and not the content of the articles themselves. therefore, we also remove the top 30 keywords of that topic from the corpus. see appendix 7.1 for the list of these removed keywords. finally, the articles are lemmatized and each word in the text body and abstract is represented by a td-idf embedding @xcite . after processing and cleaning, the final dataset contains 25,663 articles. most of these databases are regularly updated and one of the important future directions of this work will include developing a dynamic tree structure that pulls new articles from these databases weekly. ## hierarchical nmf for topic detection in a vector space model, a corpus can be represented by a d × n matrix x, where d is the size of the vocabulary, and n is the number of documents. the underlying assumptions in topic modeling @xcite are that a latent topic can be represented as a distribution over the words, and that every document is a mixture of topics, i.e. comprises a statistical distribution of topics that can be obtained by "adding up" all of the distributions of all the topics covered. in this section, we will introduce how to apply hierarchical nmf for topic detection and creation of the hierarchical tree structure. as a preliminary step, a brief introduction to using nmf for topic detection is given. ## discussion of results this section begins with a discussion and visualization of the hierarchical tree structure obtained using algorithm 1. then in sections 4.3 and 4.4 quantitative evidence is provided that the discovered topics are reasonable. in doing this, we seek to measure both the rationality of a given topic and the similarity between topics to evaluate whether the topics differ enough to be useful for a user. ## implementation in this section, we discuss the details of the implementation of hnmf and the construction of the algorithm 2: determine optimal number of topics input: integer q, corpus matrix x. determine a range for the potential topic number [k 1 , k 2 ] by plotting increment in variance explained by adding one more cluster to x; randomly select q + 1 seeds for initialization; for integer k in [k 1 , k 2 ] do generate topic sets {t j } q+1 @xmath0; generate s kj for @xmath1, 2, • • • , q where s kj is the cosine similarity matrix between topics in t j , t j+1 ; for s kj , @xmath2, 2, • • • , q do lss @xmath3; add @xmath4. ), max(s .b )) to lss k , where s ab is the (a, b)th entry of the matrix s kj ; end end return k * = arg max k (median(lss k )); hierarchical structure.