Title: Research on online emotion of COVID-19 based on text sentiment analysis

Authors: Zhenyu Gu; Yao Lin; Yonghui Dai; Chenxiao Niu

Addresses: School of Statistics and Information, Shanghai University of International Business and Economics, Shanghai, 201620, China ' Management School, Shanghai University of International Business and Economics, Shanghai, 201620, China ' Management School, Shanghai University of International Business and Economics, Shanghai, 201620, China ' School of Management, Shandong University of Technology, Shandong Province, Zibo, 255012, China

Abstract: The growth of internet users and the convenience of internet communication provide a foundation for the formation of internet emotions. As the internet and real-life interactions become closer, the influence of internet emotions on society is increasing. Therefore, taking the spread of COVID-19 in Xinjiang in 2020 as an example, 43,111 related micro-blog texts were collected. After a series of operations such as Chinese word segmentation, POS tagging, data cleaning, text representation, feature extraction and so on, thematic extraction and text sentiment analysis were carried out to get people's comment themes, emotional tendencies and COVID-19's network emotional situation. The results show that the public will have a better understanding of the cause of COVID-19 disease and its infectiousness, preventive measures and cure as time goes on. The research of this paper can help the relevant government departments to perceive and guide the network emotional situation.

Keywords: COVID-19; text sentiment analysis; web sentiment; feature extraction; topic mining.

DOI: 10.1504/IJCSE.2022.124556

International Journal of Computational Science and Engineering, 2022 Vol.25 No.4, pp.460 - 466

Received: 09 Jun 2021
Accepted: 10 Aug 2021
Published online: 28 Jul 2022 *

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