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Article: COVID-19 Sensing: Negative Sentiment Analysis on Social Media in China via BERT Model

TitleCOVID-19 Sensing: Negative Sentiment Analysis on Social Media in China via BERT Model
Authors
KeywordsCOVID-19 sensing
Public health
Sentiment classification
Social media in China
Issue Date2020
PublisherInstitute of Electrical and Electronics Engineers (IEEE): OAJ. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6287639
Citation
IEEE Access, 2020, v. 8, p. 138162-138169 How to Cite?
AbstractCoronavirus disease 2019 (COVID-19) poses massive challenges for the world. Public sentiment analysis during the outbreak provides insightful information in making appropriate public health responses. On Sina Weibo, a popular Chinese social media, posts with negative sentiment are valuable in analyzing public concerns. 999,978 randomly selected COVID-19 related Weibo posts from 1 January 2020 to 18 February 2020 are analyzed. Specifically, the unsupervised BERT (Bidirectional Encoder Representations from Transformers) model is adopted to classify sentiment categories (positive, neutral, and negative) and TF-IDF (term frequency-inverse document frequency) model is used to summarize the topics of posts. Trend analysis and thematic analysis are conducted to identify characteristics of negative sentiment. In general, the fine-tuned BERT conducts sentiment classification with considerable accuracy. Besides, topics extracted by TF-IDF precisely convey characteristics of posts regarding COVID-19. As a result, we observed that people concern four aspects regarding COVID-19, the virus Origin (Gamey Food, 3.08%; Bat, 2.70%; Conspiracy Theory, 1.43%), Symptom (Fever, 2.13%; Cough, 1.19%), Production Activity (Go to Work, 1.94%; Resume Work, 1.12%; School New Semester Beginning, 1.06%) and Public Health Control (Temperature Taking, 1.39%; Coronavirus Cover-up, 1.26%; City Shutdown, 1.09%). Results from Weibo posts provide constructive instructions on public health responses, that transparent information sharing and scientific guidance might help alleviate public concerns.
Persistent Identifierhttp://hdl.handle.net/10722/289926
ISSN
2019 Impact Factor: 3.745
2015 SCImago Journal Rankings: 0.947
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, T-
dc.contributor.authorLu, K-
dc.contributor.authorChow, KP-
dc.contributor.authorZhu, Q-
dc.date.accessioned2020-10-22T08:19:26Z-
dc.date.available2020-10-22T08:19:26Z-
dc.date.issued2020-
dc.identifier.citationIEEE Access, 2020, v. 8, p. 138162-138169-
dc.identifier.issn2169-3536-
dc.identifier.urihttp://hdl.handle.net/10722/289926-
dc.description.abstractCoronavirus disease 2019 (COVID-19) poses massive challenges for the world. Public sentiment analysis during the outbreak provides insightful information in making appropriate public health responses. On Sina Weibo, a popular Chinese social media, posts with negative sentiment are valuable in analyzing public concerns. 999,978 randomly selected COVID-19 related Weibo posts from 1 January 2020 to 18 February 2020 are analyzed. Specifically, the unsupervised BERT (Bidirectional Encoder Representations from Transformers) model is adopted to classify sentiment categories (positive, neutral, and negative) and TF-IDF (term frequency-inverse document frequency) model is used to summarize the topics of posts. Trend analysis and thematic analysis are conducted to identify characteristics of negative sentiment. In general, the fine-tuned BERT conducts sentiment classification with considerable accuracy. Besides, topics extracted by TF-IDF precisely convey characteristics of posts regarding COVID-19. As a result, we observed that people concern four aspects regarding COVID-19, the virus Origin (Gamey Food, 3.08%; Bat, 2.70%; Conspiracy Theory, 1.43%), Symptom (Fever, 2.13%; Cough, 1.19%), Production Activity (Go to Work, 1.94%; Resume Work, 1.12%; School New Semester Beginning, 1.06%) and Public Health Control (Temperature Taking, 1.39%; Coronavirus Cover-up, 1.26%; City Shutdown, 1.09%). Results from Weibo posts provide constructive instructions on public health responses, that transparent information sharing and scientific guidance might help alleviate public concerns.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE): OAJ. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6287639-
dc.relation.ispartofIEEE Access-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectCOVID-19 sensing-
dc.subjectPublic health-
dc.subjectSentiment classification-
dc.subjectSocial media in China-
dc.titleCOVID-19 Sensing: Negative Sentiment Analysis on Social Media in China via BERT Model-
dc.typeArticle-
dc.identifier.emailChow, KP: chow@cs.hku.hk-
dc.identifier.authorityChow, KP=rp00111-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ACCESS.2020.3012595-
dc.identifier.scopuseid_2-s2.0-85089567252-
dc.identifier.hkuros317151-
dc.identifier.hkuros314371-
dc.identifier.volume8-
dc.identifier.spage138162-
dc.identifier.epage138169-
dc.identifier.isiWOS:000558092200001-
dc.publisher.placeUnited States-
dc.identifier.issnl2169-3536-

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