@Article{cmc.2020.011316,
AUTHOR = {Yixian Zhang, Jieren Cheng, Yifan Yang, Haocheng Li, Xinyi Zheng, Xi Chen, Boyi Liu, Tenglong Ren, Naixue Xiong},
TITLE = {COVID-19 Public Opinion and Emotion Monitoring System  Based on Time Series Thermal New Word Mining},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {3},
PAGES = {1415--1434},
URL = {http://www.techscience.com/cmc/v64n3/39437},
ISSN = {1546-2226},
ABSTRACT = {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 are 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.},
DOI = {10.32604/cmc.2020.011316}
}