COVID-19 Public Opinion and Emotion Monitoring System Based on Time Series Thermal New Word Mining
This work addresses the need for real-time public opinion and emotion tracking during epidemics, though it appears incremental as it builds on existing sentiment analysis and data mining techniques.
The authors tackled the problem of monitoring public emotions during the COVID-19 pandemic by developing a system based on time series thermal new word mining, which achieved better generalization ability and smaller sentiment discriminant error compared to the Jiagu deep learning model.
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.