| dc.description.abstract | COVID-19 is continuing to threaten the public hygiene of countries around the world. An
efficiently way to predict the trend of COVID-19 epidemic will help researchers and policy maker make the right decision to reduce the mortality rate and confrimed case rate.At present,All research on COVID-19 epidemic prediction is based on technical data, However, With the development of social media,Using social media texts to predict is common in various fields.Therfore, This research is mainly discussed about using different sentiment analysis methods to generate daily sentiment scores from social media texts,and combine technical data
for epidemic prediction.
This research selects different sentiment analysis methods(dictionary method, API, and dynamic word embedding sentiment analysis method),and uses three different classifiers ,SVM、LSTM、Bi-GRU for epidemic prediction. At the end of the research, we found that the dynamic word embedding sentiment analysis method RoBERTa with the epidemic prediction classifier Bi-GRU can predict the trend of COVID-19 epidemic with best combination. In predicting the
number of confirmed cases, evaluation indicator precision is rasie to 75.89%. | en_US |