Public Sentiment Assessment of Coronavirus-Specific Tweets using a Transformer-based BERT Classifier

Abstract

Worldwide, the (COVID-19) pandemic had also affected people's daily routines. In general also during lockdown periods, people around the world use social media to express their thoughts and feelings about the epidemic which has interrupted their daily lives. There has been a huge spike in tweets about coronavirus on Twitter in a short period of time, including both positive and negative messages. As a result of the wide range of content in the tweets, the researchers have turned to sentiment analysis in order to gauge how the general public feels about COVID-19. According to the findings of this study, the best way to examine COVID-19 is to look at how people use Twitter to share their thoughts and opinions. Sentiment categorization can be accomplished by utilising a variety of feature sets as well as classifiers in combination with the suggested approach. Tweets collected from people with COVID-19 perceptions can be used to better understand and manage the epidemic. Positive, negative, as well as neutral emotion classifications are being used to classify tweets. In this study, Tweets containing specific information about the Coronavirus epidemic are used as sentiment analysis packages. Bidirectional Encoder Representations from Transformers (BERT) are used to identify sentiment categories, whereas the TF-IDF (term frequency-inverse document frequency) prototype is used to summarise the topics of postings. Trend analysis and qualitative methods are being used to identify negative sentiment traits. In general, when it comes to sentiment classification, the fine-tuned BERT is very accurate. In addition, the COVID-19-related post features of TF-IDF themes are accurately conveyed. Coronavirus tweet sentiments are analysed using a BERT and TF-IDF hybrid classifier. Single-sentence classification is transformed into pair-sentence classification, which solves BERT's performance issue in text classification problems. Our evaluation measures (accuracy= 0.70; precision= 0.67; recall= 0.64; and F1-score= 0.65) are used to evaluate the effectiveness of the classifier.