Text Mining Approaches to Analyze Public Sentiment Changes Regarding COVID-19 Vaccines on Social Media in Korea
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References listed on IDEAS
- Haklae Kim, 2019. "Analysis of standard vocabulary use of the open government data: the case of the public data portal of Korea," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(3), pages 1611-1622, May.
- Xuehua Han & Juanle Wang & Min Zhang & Xiaojie Wang, 2020. "Using Social Media to Mine and Analyze Public Opinion Related to COVID-19 in China," IJERPH, MDPI, vol. 17(8), pages 1-22, April.
- Philip Ball, 2020. "Anti-vaccine movement could undermine efforts to end coronavirus pandemic, researchers warn," Nature, Nature, vol. 581(7808), pages 251-251, May.
- Martin Reisenbichler & Thomas Reutterer, 2019. "Topic modeling in marketing: recent advances and research opportunities," Journal of Business Economics, Springer, vol. 89(3), pages 327-356, April.
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- Tao Shu & Zhiyi Wang & Huading Jia & Wenjin Zhao & Jixian Zhou & Tao Peng, 2022. "Consumers’ Opinions towards Public Health Effects of Online Games: An Empirical Study Based on Social Media Comments in China," IJERPH, MDPI, vol. 19(19), pages 1-19, October.
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Keywords
COVID-19 vaccines; topic modeling; sentiment analysis; Korea;All these keywords.
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