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

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