The COVID-19 pandemic has affected the economic sector, especially the audit task that requires the physical intervention of the auditor. The aim of this paper is to study the effect of COVID-19 on audit opinion in the MENA region through a novel text mining approach. The collected data included 83 bank reports from 377 branches in 14 MENA countries. The text mining approach was employed using Python software via corpus creation, tokenization, stop words removal, stemming, and feature selection. Afterwards, a univariate analysis was performed to delineate the variables that are significantly associated with COVID-19, followed by a linear regression model quantifying the relationship of the variables. The results of the text mining process led to the creation of a dictionary composed of 8000 words. After the text mining steps, 10 variables were obtained. The univariate analysis showed that 3 out of 10 extracted variables were associated with COVID-19 and a linear regression equation was accordingly generated. Our research revealed that, in the MENA region, the COVID-19 pandemic led to an increase in the audit workload and risk assessment, yielding an overall unfavorable audit opinion. Finally, the authors used similar techniques to the research of Wei, Li, Zhu, and Li (2019) and Boskou, Kirkos, and Spathis (2018).