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Article ; Online: Weibo Users' Emotion and Sentiment Orientation in Traditional Chinese Medicine (TCM) During the COVID-19 Pandemic.

Gao, Hao / Guo, Difan / Wu, Jing / Li, Lina

Disaster medicine and public health preparedness

2021  Volume 16, Issue 5, Page(s) 1835–1838

Abstract: ... this study used DLUT-Emotion ontology to analyze the sentiment orientation and emotions of selected data and ... of TCM in coronavirus disease 2019 (COVID-19) treatment.: Methods: We collected data referring to TCM ... have a relatively positive attitude to the TCM in the COVID-19 treatment in general. Results of text ...

Abstract Objective: This study aimed to explore Chinese people's attitudes to the official application of TCM in coronavirus disease 2019 (COVID-19) treatment.
Methods: We collected data referring to TCM on Weibo from 0:00 on January 24, 2020, to 23:59:59 on March 31, 2020 (Beijing time). In addition, this study used DLUT-Emotion ontology to analyze the sentiment orientation and emotions of selected data and then conducted a text analysis.
Results: According to DLUT-Emotion ontology, we examined 3 sentiment orientations of 215,565 valid Weibo posts. Among them, 25,025 posts were judged as positive emotions, accounting for approximately 12%; 22,362 were regarded as negative emotions, accounting for approximately 10%; and 168,178 were judged as neutral emotions, accounting for approximately 78%. Results indicate that the words judged as "Good" have the highest frequency, and words marked as "Happy" have increased over time. The word frequency of "Fear" and "Sadness" showed a significant downward trend.
Conclusion: Weibo users have a relatively positive attitude to the TCM in the COVID-19 treatment in general. Results of text analysis show that data with negative emotions is essentially an expression of public opinions to supporting TCM or not. Texts of "Fear" and "Sadness" do not reflect users' negative attitudes to TCM.
MeSH term(s) Humans ; Pandemics ; COVID-19/epidemiology ; SARS-CoV-2 ; Medicine, Chinese Traditional ; Social Media ; Emotions ; Attitude
Language English
Publishing date 2021-08-09
Publishing country United States
Document type Journal Article
ZDB-ID 2375268-3
ISSN 1938-744X ; 1935-7893
ISSN (online) 1938-744X
ISSN 1935-7893
DOI 10.1017/dmp.2021.259
Database MEDical Literature Analysis and Retrieval System OnLINE

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