Abstract: In recent years, microblog has become an important platform for Internet public opinion disseminationand public opinion mining. In order to analyze the impact of epidemic events on Netizens' emotions, we should do a good job in prevention and control publicity and public opinion guidance scientifically and efficiently.Therefore, we integrate different deep learning methods to conduct emotional analysis of microblog comments on the COVID-19 outbreak at the end of 2020. A hybrid model based on RNN(Recursive Neural Network) and LSTM (Long Short-Term Memory) and using the FastText word vector representation in the embedding layer is proposed to reduce the noise data in the word vectors and thus obtain high-quality word vectors with semantically
rich and less noise. Training on Weibo corpora and compared with Bayesian and Support Vector Machine, RNN,LSTM multiple methods, the results show that the accuracy of the emotion analysis model proposed in this paper reaches 98. 71% , which proves that the model can effectively improve the accuracy of emotion analysis.

Key words: emotional analysis;  , weibo corpus; , FastText word vector； long and short memory network