Utilizing distilBert transformer model for sentiment classification of COVID-19's Persian open-text responses
This work addresses sentiment analysis for public health monitoring in Iran during the COVID-19 pandemic, but it is incremental as it applies an existing method to new data.
The authors tackled sentiment classification of Persian open-text responses about COVID-19 using a distilBert transformer model, achieving an accuracy of 0.824, precision of 0.824, recall of 0.798, and F1 score of 0.804.
The COVID-19 pandemic has caused drastic alternations in human life in all aspects. The government's laws in this regard affected the lifestyle of all people. Due to this fact studying the sentiment of individuals is essential to be aware of the future impacts of the coming pandemics. To contribute to this aim, we proposed an NLP (Natural Language Processing) model to analyze open-text answers in a survey in Persian and detect positive and negative feelings of the people in Iran. In this study, a distilBert transformer model was applied to take on this task. We deployed three approaches to perform the comparison, and our best model could gain accuracy: 0.824, Precision: 0.824, Recall: 0.798, and F1 score: 0.804.