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

TL;DR

This paper presents a comprehensive COVID-19 public opinion monitoring system that utilizes time series thermal new word mining, sentiment analysis, and data visualization to track and analyze public emotions and opinions during the pandemic.

Contribution

It introduces a novel system combining new word discovery, sentiment analysis, and visualization specifically tailored for COVID-19 public opinion monitoring.

Findings

The system effectively identifies public emotion trends and hot topics.

The proposed sentiment model outperforms existing deep learning models in accuracy.

Visualization tools provide clear insights into public opinion dynamics.

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 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.