Sentiment Analysis in Smart Cities

With the emergence of Web 2.0, social networks represent essential information exchange platforms and tools. They are used for personal and professional purposes. Internet users express their sentiments and exchange ideas on various topical issues, including health; therefore, it is essential for professionals to consider the different opinions shared on social networks and analyze them mainly during a pandemic disease. The basic sentiment analysis methods are no longer effective given the huge amount of data present on the web, this is why machine learning algorithms become necessary, which provide decision-making systems that improve performance based on the processed data. In this paper, we focus on the analysis of sentiment in smart cities regarding the coronavirus vaccination. Our work involves determining the emotional mood of user speeches by classifying them into three categories: positive, neutral, and negative, to help authorities make decisions about the vaccination campaign.