Detecting topic and sentiment dynamics due to COVID-19 pandemic using social media

Yin, Hui, Yang, Shuiqiao and Li, Jianxin 2021, Detecting topic and sentiment dynamics due to COVID-19 pandemic using social media, in ADMA 2020 : Proceedings of the 16th International Conference on Advanced Data Mining and Applications, Springer Nature, Cham, Switzerland, pp. 610-623, doi: 10.1007/978-3-030-65390-3_46.

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Title Detecting topic and sentiment dynamics due to COVID-19 pandemic using social media
Author(s) Yin, Hui
Yang, Shuiqiao
Li, JianxinORCID iD for Li, Jianxin orcid.org/0000-0002-9059-330X
Conference name ADMA 2020. Advanced Data Mining and Applications. International Conference (16th : 2020 : Foshan, China)
Conference location Foshan, China
Conference dates 12 - 14 Nov. 2020
Title of proceedings ADMA 2020 : Proceedings of the 16th International Conference on Advanced Data Mining and Applications
Editor(s) Yang, Xiaochun
Wang, Chang-Dong
Islam, Md. Saiful
Zhang, Zheng
Publication date 2021
Series Lecture Notes in Computer Science Book Series(LNCS)
Start page 610
End page 623
Total pages 14
Publisher Springer Nature
Place of publication Cham, Switzerland
Keyword(s) COVID-19
topic tracking
sentiment analysis
Twitter
CORE2020 B
Summary The outbreak of the novel Coronavirus Disease (COVID-19) has greatly influenced people’s daily lives across the globe. Emergent measures and policies (e.g., lockdown, social distancing) have been taken by governments to combat this highly infectious disease. However, people’s mental health is also at risk due to the long-time strict social isolation rules. Hence, monitoring people’s mental health across various events and topics will be extremely necessary for policy makers to make the appropriate decisions. On the other hand, social media have been widely used as an outlet for people to publish and share their personal opinions and feelings. The large scale social media posts (e.g., tweets) provide an ideal data source to infer the mental health for people during this pandemic period. In this work, we propose a novel framework to analyze the topic and sentiment dynamics due to COVID-19 from the massive social media posts. Based on a collection of 13 million tweets related to COVID-19 over two weeks, we found that the positive sentiment shows higher ratio than the negative sentiment during the study period. When zooming into the topic-level analysis, we find that different aspects of COVID-19 have been constantly discussed and show comparable sentiment polarities. Some topics like “stay safe home” are dominated with positive sentiment. The others such as “people death” are consistently showing negative sentiment. Overall, the proposed framework shows insightful findings based on the analysis of the topic-level sentiment dynamics.
ISBN 9783030653897
ISSN 0302-9743
1611-3349
Language eng
DOI 10.1007/978-3-030-65390-3_46
Indigenous content off
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2020, Springer Nature Switzerland
Persistent URL http://hdl.handle.net/10536/DRO/DU:30149183

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