Farshid Danesh - Information Management Department, Regional Information Center for Science and Technology (RICeST), Shiraz, Iran
Forough Rahimi - Information Management Department, Regional Information Center for Science and Technology (RICeST), Shiraz, Iran

Background and Aim: The results from the analysis of COVID-۱۹ literature by employing text-mining techniques are of particular importance for researchers, policymakers, and planners of medical sciences at the national and international levels, avoiding parallel research and waste of time and budget. The paper explore emerging topics and the trend of scientific words at the national and international levels in the subject area of COVID-۱۹. Materials and Methods: This applied research was conducted by employing the text-mining and its related algorithms and classifying texts. The population consists of all COVID-۱۹ articles indexed in PubMed Central® (PMC). The number of records retrieved was ۱۶۰,۸۶۲ items until June ۱۰, ۲۰۲۱. Among these, ۳۱۴۳ national and ۱۵۷,۷۱۹ international COVID-۱۹ articles. Python and its related libraries were applied. The most significant words were also identified and reported based on TF-IDF weighting. Emerging topics were identified according to the weighted average growth. Results: "COVID", "infect", and "cell" were among the most important words used in international COVID-۱۹ articles. In addition, the most important words in the national COVID-۱۹ articles were "patient", "SARS-Cov", and "COVID". Conclusion: Among the most important conclusions that can be inferred from the trend of word change used in the COVID-۱۹ literature is that the most significant words in international literature differ significantly from those in national literature, as international research focuses on COVID-۱۹ and the infections caused by it. In contrast, national research focuses on COVID-۱۹ and patients. Another significant result is the annual word-changing national and international literature.

Covid-۱۹, Text Mining, TF-IDF, Classification, Clustering, Emerging Topics, Python, کووید-۱۹, متن کاوی, فراوانی وزنی تی اف-آی دی اف, طبقه بندی, خوشه بندی, موضوعات نوپدید, پایتون