Around the world in 60 days: an exploratory study of impact of COVID-19 on online global news sentiment
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DOI: 10.1007/s42001-020-00088-3
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- Wataru Souma & Irena Vodenska & Hideaki Aoyama, 2019. "Enhanced news sentiment analysis using deep learning methods," Journal of Computational Social Science, Springer, vol. 2(1), pages 33-46, January.
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Cited by:
- Ryuichi Saito & Shinichiro Haruyama, 2023. "Estimating time-series changes in social sentiment @Twitter in U.S. metropolises during the COVID-19 pandemic," Journal of Computational Social Science, Springer, vol. 6(1), pages 359-388, April.
- Dylong, Patrick & Koenings, Fabian, 2023. "Framing of economic news and policy support during a pandemic: Evidence from a survey experiment," European Journal of Political Economy, Elsevier, vol. 76(C).
- Emilio Ferrara & Stefano Cresci & Luca Luceri, 2020. "Misinformation, manipulation, and abuse on social media in the era of COVID-19," Journal of Computational Social Science, Springer, vol. 3(2), pages 271-277, November.
- Waseem Ahmad & Bang Wang & Philecia Martin & Minghua Xu & Han Xu, 2023. "Enhanced sentiment analysis regarding COVID-19 news from global channels," Journal of Computational Social Science, Springer, vol. 6(1), pages 19-57, April.
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Keywords
COVID-19; News sentiment analysis; Unsupervised opinion mining; News negativity; Correlation; News agenda;All these keywords.
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