Iranian COVID-19 Publications in LitCovid: Text Mining and Topic Modeling

• Published in: Scientific programming Vol. 2021; pp. 1 - 12
• Main Authors: Dastani, Meisam, Danesh, Farshid
• Format: Journal Article
• Language: English
• Published: New York Hindawi 2021; John Wiley & Sons, Inc
• Subjects: Algorithms; Citation indexes; Coronaviruses; COVID-19 vaccines; Data analysis; Data mining; Diagnosis; Dirichlet problem; Disease prevention; Disease transmission; Documents; Emergency medical care; Epidemics; Explicit knowledge; Forecasting; Mathematical models; Medical research; Modelling; Pandemics; Scientific papers; Scientists; Scientometrics; Severe acute respiratory syndrome coronavirus 2; Strategic planning; Viral diseases; Iran; United States > US; China; India
• ISSN: 1058-9244; 1875-919X; 1875-919X
• DOI: 10.1155/2021/3315695

COVID-19 is a threat to the lives of people all over the world. As a result of the new and unknown nature of COVID-19, much research has been conducted recently. In order to increase and enhance the growth rate of Iranian publications on COVID-19, this article aims to analyze these publications in LitCovid to identify the topical and content structure and topic modeling of scientific publications in the mentioned subject area. The present article is applied research performed by using an analytical approach as well as text mining techniques. The statistical population is all the publications of Iranian researchers in LitCovid. Latent Dirichlet Allocation (LDA) and Python were used to analyze the data and implement text mining and topic modeling algorithms. Data analysis shows that the percentage of Iranian publications in the eight topical groups in LitCovid is as follows: prevention (39.57%), treatment (18.99%), diagnosis (18.99%), forecasting (7.83%), case report (6.52%), mechanism (3.91%), transmission (3.62%), and general (0.58%). The results indicate that patient, pandemic, outbreak, case, Iranian, model, care, health, coronavirus, and disease are the most important words in the publications of Iranian researchers in LitCovid. Six topics for prevention; four topics for treatment and case report and forecasting; three topics for diagnosis, mechanism, and transmission in general have been obtained by implementing the topic modeling algorithm. Most of the Iranian publications in LitCovid are related to the topic “pandemic status,” with 22.47% in the prevention category, and the lowest number of publications is related to the topic “environment,” with 11.11% in the transmission category. The present study indicates a better understanding of essential and strategic issues of Iranian publications in LitCovid. The results reveal that many Iranian studies on COVID-19 were primarily on the issues related to prevention, management, and control. These findings provided a structured and research-based viewpoint of COVID-19 in Iran to guide researchers and policymakers.