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SCI."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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.<\/jats:p>","DOI":"10.1007\/s42979-024-03296-0","type":"journal-article","created":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T12:02:40Z","timestamp":1727956960000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Sentiment Analysis Using Improved CT-BERT_CONVLayer Fusion Model for COVID-19 Vaccine Recommendation"],"prefix":"10.1007","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3638-7369","authenticated-orcid":false,"given":"Areeba","family":"Umair","sequence":"first","affiliation":[]},{"given":"Elio","family":"Masciari","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"key":"3296_CR1","doi-asserted-by":"publisher","DOI":"10.31234\/osf.io\/6pv5c","author":"M Yesilada","year":"2021","unstructured":"Yesilada M, Lewandowsky S. 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