Sentiment Analysis of the COVID-19 Epidemic Based on Deep Learning

Abstract

Twitter text sentiment analysis has important applications in public sentiment monitoring. The results of sentiment analysis based on traditional machine learning models and sentiment dictionaries are often unsatisfactory. How to optimize the performance of public opinion sentiment analysis has become an important challenge in this field. This paper uses the BERT model based on deep learning to complete the language understanding task and compares the performance with the traditional practice. The results show that the BERT model achieves better performance, reaching more than 90%. The model was then used to perform three classifications to analyze Twitter comments during the COVID-19 outbreak, and overall positive sentiment and neutral sentiment dominated. In addition, we also conduct related analysis on word frequency, word cloud and time comparison, in order to achieve the purpose of comprehensively understanding the social-emotional state during the epidemic.