  With the spread and development of new epidemics, it is of great reference
value to identify the changing trends of epidemics in public emotions. We
designed and implemented the COVID-19 public opinion monitoring system based on
time series thermal new word mining. A new word structure discovery scheme
based on the timing explosion of network topics and a Chinese sentiment
analysis method for the COVID-19 public opinion environment is proposed.
Establish a "Scrapy-Redis-Bloomfilter" distributed crawler framework to collect
data. The system can judge the positive and negative emotions of the reviewer
based on the comments, and can also reflect the depth of the seven emotions
such as Hopeful, Happy, and Depressed. Finally, we improved the sentiment
discriminant model of this system and compared the sentiment discriminant error
of COVID-19 related comments with the Jiagu deep learning model. The results
show that our model has better generalization ability and smaller discriminant
error. We designed a large data visualization screen, which can clearly show
the trend of public emotions, the proportion of various emotion categories,
keywords, hot topics, etc., and fully and intuitively reflect the development
of public opinion.
