Abstract
Throughout the Covid-19 pandemic, social media has been flooded with dozens of important details that could illuminate the world. Twitter is a good community based on Natural Language Processing (NLP) strategies. In this project, our aim is to reflect the situation in the USA during the Covid-19 epidemic. Epidemics have become more prevalent in the region. Some states had large opposition in the past or were delayed in the event of standards, for example. Countries differed in the timeline for the establishment and enforcement of home occupation policies. Sentiment analysis is done via TextBlob, determining the magnitude of the tweets in the spectrum of good and bad. TextBlob is quite popular Python library used for the task of data processing. It has the feature of a simple API to share common language processing functions such as marking in speech, coding, sentiment analysis, separation, translation, etc. Marked details from these tweets form a visible timeline of global sentiment Covid-19. This opens the way for us to look at the public reaction to events caused by the epidemic.
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