Sentiment Analysis Using Improved CT-BERT_CONVLayer Fusion Model for COVID-19 Vaccine Recommendation

Abstract COVID-19 has significantly impacted individuals, communities, and countries worldwide. These effects include health impacts, economics impacts, social impacts, educational, political and environmental impacts. The COVID-19 vaccine development was crucial for disease control and monitoring, yet the threat still looms large. Vaccine recommender systems can help the health practitioners in combating COVID-19 by providing the information and guidance on the benefits and risks of COVID-19 vaccines to individuals based on their preferences and medical history. In this paper, we have proposed sentiment analysis based recommender system for COVID-19 vaccines. We used Twitter data of 10,000 tweets about COVID-19 vaccines and applied pre-processing steps. We propose an ensemble of random forest with CT-BERT_CONVLayerFusion model, a novel algorithm, for classifying the tweets into seven different categories of sentiments. We also performed aspect-based review categorization which works on the queries given by a user. We compared the results of sentiment classification with the state-of-the-art with metrics including accuracy, recall, precision, and F1-score, and found out that our proposed approach outperformed all other state-of-the-art model by achieving maximum accuracy, recall, precision and F1-measure. Hence, such advanced methods can help somehow to fight COVID-19 as well as reducing the vaccine hesitancy by suggesting proper vaccines to patients based on the their specific concerns and questions.