Analysis of COVID-19 Rebound Based on Natural Language Processing

The novel coronavirus epidemic hasn't finished in the world. The number of people who come down with this disease keeps increasing. Besides, people make some comments on the website, which may be related to the epidemic situation. This work will analyze this relationship and make some suggestions to governments. First, the crawler is used to get the data of public emotion. NLP is the main method in this work, which can classify the words we get based on an emotion dictionary. In the end, we do some further analysis and give the warning line of confirmed cases by regression, whose value is around 30 people.