Detecting Topic and Sentiment Dynamics Due to COVID-19 Pandemic Using   Social Media

Yin, H; Yang, S; Li, J

Detecting Topic and Sentiment Dynamics Due to COVID-19 Pandemic Using Social Media

Yin, H Yang, S

Li, J

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Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: Advanced Data Mining and Applications, 2020, 12447, pp. 610-623
Issue Date:: 2020

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Field	Value	Language
dc.contributor.author	Yin, H
dc.contributor.author	Yang, S https://orcid.org/0000-0002-6772-6805
dc.contributor.author	Li, J
dc.date	2020-11-12
dc.date.accessioned	2021-02-15T20:29:39Z
dc.date.available	2021-02-15T20:29:39Z
dc.date.issued	2020
dc.identifier.citation	Advanced Data Mining and Applications, 2020, 12447, pp. 610-623
dc.identifier.isbn	978-3-030-65389-7
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/146117
dc.description.abstract	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.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Advanced Data Mining and Applications
dc.relation.ispartof	International Conference on Advanced Data Mining and Applications
dc.relation.ispartofseries	Lecture Notes in Computer Science
dc.relation.isbasedon	10.1007/978-3-030-65390-3_46
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Detecting Topic and Sentiment Dynamics Due to COVID-19 Pandemic Using Social Media
dc.type	Conference Proceeding
utslib.citation.volume	12447
utslib.location.activity	Foshan, China
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-02-15T20:29:08Z
pubs.finish-date	2020-11-14
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.start-date	2020-11-12
pubs.volume	12447
dc.location	Switzerland

Abstract:

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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/146117