Open Access

ARTICLE

COVID-19 Public Opinion and Emotion Monitoring System Based on Time Series Thermal New Word Mining

Yixian Zhang¹, Jieren Cheng^{2, *}, Yifan Yang², Haocheng Li², Xinyi Zheng², Xi Chen², Boyi Liu³, Tenglong Ren⁴, Naixue Xiong⁵

1 School of Information and Communication Engineering, Hainan University, Haikou, 570100, China.
2 School of Computer Science and Cyberspace Security, Hainan University, Haikou, 570100, China.
3 University of Chinese Academy of Sciences, Shenzhen, 518000, China.
4 National University of Defense Technology, Changsha, 410000, China.
5 Department of Mathematics and Computer Science, Northeastern State University, Tahlequah, USA.

* Corresponding Author: Jieren Cheng. Email: .

(This article belongs to the Special Issue: Artificial Intelligence and Information Technologies for COVID-19)

Computers, Materials & Continua 2020, 64(3), 1415-1434. https://doi.org/10.32604/cmc.2020.011316

Received 30 April 2020; Accepted 23 May 2020; Issue published 30 June 2020

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.

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Citations