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.

中文翻译：

---

LitCovid 中的伊朗 COVID-19 出版物：文本挖掘和主题建模

COVID-19 对全世界人民的生命构成威胁。由于 COVID-19 的新特性和未知性质，最近进行了大量研究。为了增加和提高伊朗关于 COVID-19 出版物的增长率，本文旨在分析 LitCovid 中的这些出版物，以确定上述主题领域科学出版物的主题和内容结构以及主题建模。本文是通过使用分析方法和文本挖掘技术进行的应用研究。统计人口是伊朗研究人员在 LitCovid 上的所有出版物。使用潜在狄利克雷分配 (LDA) 和 Python 来分析数据并实现文本挖掘和主题建模算法。数据分析显示，LitCovid 8个专题组中伊朗出版物的比例如下：预防（39.57%）、治疗（18.99%）、诊断（18.99%）、预测（7.83%）、病例报告（6.52%） 、机制 (3.91%)、传输 (3.62%) 和一般 (0.58%)。结果表明，患者、大流行、爆发、病例、伊朗、模型、护理、健康、冠状病毒和疾病是伊朗研究人员在 LitCovid 的出版物中最重要的词。六个预防主题；治疗和病例报告和预测的四个主题；通过实现主题建模算法，获得了诊断、机制和传输三个主题。LitCovid 中的大多数伊朗出版物都与“大流行状况”主题相关，其中 22.47% 属于预防类别，与“环境”主题相关的出版物数量最少，传播类别占 11.11%。本研究表明对 LitCovid 中伊朗出版物的基本和战略问题有了更好的理解。结果表明，伊朗对 COVID-19 的许多研究主要是针对与预防、管理和控制相关的问题。这些发现为伊朗的 COVID-19 提供了结构化和基于研究的观点，以指导研究人员和决策者。