Artificial Intelligence Based Analysis of Positive and Negative Tweets Towards COVID-19 Vaccines

Today, the whole world is facing a biggest challenge in the form of coronavirus. The spread of COVID has caused health concerns worldwide. Considering this, there is an increase in the global efforts for the development of the COVID. The widespread provision of the vaccine is the major requirement in achieving the immunity against coronavirus. For this purpose, the public sentiments towards the vaccine campaign must be analysed. With the help of social media services, people are freely sharing their feelings and sentiments through posts, reviews or tweets. In this research, we have used advanced artificial intelligence methods for analysing the public sentiments towards vaccine campaigns. For this purpose, we used twitter data freely available on the Kaggle website and performed basic preprocessing steps. We used natural language processing (NLP) techniques such as TextBlob() and word cloud in order to find the polarity of the tweets to categorize them in seven different classes and find the most frequent keywords respectively. We used BERT model for sentimental analysis to understand the people’s mental state by studying their opinion and behaviour towards vaccines. Hence, the artificial intelligence based social network analysis must be considered for performing and analyzing the public sentiments towards any trending topic, pandemic or any other worldwide or local issue. Such methods can help to develop the trust of people towards vaccine campaigns timely and help to provide the proper administration of vaccines at large scale.