COVID-19 Sentiment Analysis applying BERT

ABSTRACT

The growth of social data on the internet has accelerated during the last two decades. As a result, researchers can access data and information for various academic and commercial purposes. The novel coronavirus disease (COVID-19) is a current pandemic that has sparked widespread concern worldwide. Spreading misleading information on social media platforms like Twitter, on the other hand, is exacerbating the disease's concern. This research aims to examine tweets and develop a model that can detect public sentiment from social media posts;consequently, necessary precautions can be taken to preserve adequate validity of information for the general public. We believe that various social media platforms have a significant impact on creating public awareness about the disease's importance and encouraging preventive measures among community members. For this study, we applied the Bidirectional Encoder Representations from Transformers (BERT) model, a new deep-learning technique for text analysis and performance with exceptional multi-class accuracy. We also compared it with six shallow machine learning models. © 2022 IEEE.