Analyzing Iranian opinions toward COVID-19 vaccination

OBJECTIVES: The aim of this study was to assess Iranian tweets in order to: (1) analyze Iranian views toward COVID-19-vaccination; (2) compare Iranian views toward homegrown and imported COVID-19-vaccines; (3) present an effective model for sentiment analysis tasks regarding critical issues such as COVID-19-vaccination. DESIGN AND METHODS: Persian tweets mentioning homegrown and imported vaccines were retrieved between April 1 and and September 30, 2021. The sentiments of retrieved tweets were identified using a deep-learning sentiment-analysis model. A sarcasm detection model, based on a random forest classifier, was used to identify sarcastic tweets and thus minimize misclassification. Finally, Iranian views toward COVID-19 vaccination were investigated. RESULTS: Subtle differences were found in the number of positive sentiments toward homegrown and imported vaccines, with the latter having dominant positive polarity. Negative sentiments regarding homegrown and imported vaccines increased in some months. No significant differences were observed between the percentages of overall positive and negative opinions toward vaccination. CONCLUSION: It is worrisome that negative sentiments toward homegrown and imported vaccines increased in some months in Iran. Health organizations can focus on Twitter in order to promote positive messaging toward COVID-19 vaccination. Sarcasm detection enabled the identification of tweets that ironically stated positive sentiments toward vaccination, thus improving the accuracy of the sentiment analysis results. Our sentiment analysis-sarcasm detection model is a reliable tool for mitigating classification problems.