A Study on Emotional Analysis Focusing on Onomatopoeia Used on SNS for the COVID-19
Pages 109 - 116
Abstract
COVID-19 has dramatically changed the social situation in Japan. Along with the change in the real society, COVID-19 also changes the usage of social media. This study reports on findings from an analysis of onomatopoeia appears in posts on social media regarding COVID-19 to see how it has affected people's emotion. We analyzed the frequency of appearance of onomatopoeias expressing specific emotions according to the time variation, the relation between major events such as the declaration of state of emergency, and changes of co-occurrence words for the onomatopoeias. As a result of analysis, we found that the frequencies and degree of variation of onomatopoeias that belong to the same emotion group are complexly associated. The analysis results on co-occurrence words and frequency shift by events suggest that the cause of the change in emotion was different even for the onomatopoeia expressing the same emotion. The long-term emotional changes marked the peak in June 2020 during the second COVID-19 outbreak in Japan, rather than the first outbreak occurred in April 2020. At this time, as the number of infected people increased, the frequency of the use of the onomatopoeias also tended to increase. From the first case of COVID-19 in Japan (Jan 2019) to the second outbreak (Jun 2020), “anger “and “fear” were dominant emotions then they change to “peace of mind” during the second peak to the third outbreak (Nov 2020), and finally become “disgust”.
References
[1]
Dodds, P. S., Harris, K. D., Kloumann, I. M., Bliss, C. A., and Danforth, C. M. 2011. Temporal patterns of happiness and information in a global social network: Hedonometrics and Twitter. PLoS ONE, Vol. 6, No. 12, e26752. https://doi.org/10.1371/journal.pone.0026752
[2]
Golder, S. A. and Macy, M. W. 2011. Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science, Vol. 333, No. 6051, pp. 1878–1881. https://doi.org/10.1126/science.1202775
[3]
Asako Miura, Fujio Toriumi, Masashi Komori, Naohiro Matsumura, Kai Hiraishi. 2016. Relationship between emotion and diffusion of disaster information on social media: Case study on 2011 Tohoku Earthquake. Transactions of the Japanese Society for Artificial Intelligence. Vol. 31, No. 1, pp. NFC–A1–9 (in Japanese). https://doi.org/10.1527/tjsai.NFC-EC1
[4]
Cynthia Mei Chew, Gunther Eysenbach. 2010. Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak. PLoS One, 5(11), pp.1-13. https://doi.org/10.1371/journal.pone.0014118
[5]
Seltzer, E.K.; Jean, N.S.; Kramer-Golinkoff, E.; Asch, D.A.; Merchant, R.M. 2015. The content of social media's shared images about Ebola: A retrospective study. Public Health, 129, 1273–1277. https://doi.org/10.1016/j.puhe.2015.07.025
[6]
Fu K.-W., Liang H., Saroha N., Tse Z. T. H., Ip P., Fung I. C.-H. 2016. How people react to Zika virus outbreaks on Twitter? A computational content analysis. American Journal of Infection Control, Volume 44, pp.1700–1702. https://doi.org/10.1016/j.ajic.2016.04.253
[7]
Fujio Toriumi, Takeshi Sakaki, Mitsuo Yoshida. 2020. Social Emotions Under the Spread of COVID-19 Using Social Media. Transactions of the Japanese Society for Artificial Intelligence, Vol. 35, No. 4, pp. F-K45_1-7 (in Japanese). https://doi.org/10.1527/tjsai.F-K45
[8]
Ptaszynski, M., Dybala, P., Shi, W., Rzepka, R., and Araki, K. 2009. A system for affect analysis of utterances in Japanese supported with web mining. Journal of Japan Society for Fuzzy Theory and Intelligent Informatics, Vol. 21, No. 2, pp. 194–213. https://doi.org/10.3156/jsoft.21.194
[9]
Ptaszynski, M., Dybala, P., Rzepka, R., Araki, K., and Masui, F. 2017. ML-Ask: Open source affect analysis software for textual input in Japanese. Journal of Open Research Software, Vol. 5, No. 1, 16. https://doi.org/10.5334/jors.149
[10]
Akira Nakamura. 1993. Emotional Expression Dictionary. Tokyodo Shuppan Co (in Japanese).
[11]
Kei Ishiguro. 2008. What onomatopoeia is. Kokubungaku, Vol. 53, No. 14, pp.24–32 (in Japanese).
[12]
Kakei T, Tamori I. 1993. Onomatopia—A paradise of onomatopoeia, Keiso Shobo, vol 9 (in Japanese).
[13]
Tamori, I., Schourup, L. 1999. Onomatope: Form and meaning. Kurosio Publishers (in Japanese).
[14]
Kudo, T. 2005. Mecab: Yet another part-of-speech and morphological analyzer. http://mecab.sourceforge.net/.
[15]
Masahiro Ono. 2007. Onomatopoeia Mimetics 4500: Japanese Onomatopoeia Dictionary. Shogakukan (in Japanese).
[16]
R. Agrawal, T. Imielinski, A. Swami. 1993. Mining association rules between sets of items in large databases. Proceedings of the ACM SIGMOD International Conference on Management of Data, Washington, D.C., pp. 207–216. https://doi.org/10.1145/170036.170072
Recommendations
Emotion Bubbles: Emotional Composition of Online Discourse Before and After the COVID-19 Outbreak
WWW '22: Proceedings of the ACM Web Conference 2022The COVID-19 pandemic has been the single most important global agenda in the past two years. In addition to its health and economic impacts, it has affected people’s psychological states, including a rise in depression and domestic violence. We traced ...
SNS users' opinion expression: Focusing on suppression effect in spiral of silence
Highlights- This study presents an extended research model that combines SOS and IM theories.
AbstractBased on the spiral of silence (SOS) and impression management (IM) theories, this study suggested perceived opinion support, fear of negative evaluation, and social network service (SNS) impression management as motivational factors ...
The bright and dark sides of social media use during COVID-19 lockdown: Contrasting social media effects through social liability vs. social support
AbstractThere exist ongoing discussions regarding whether, when, or why heightened reliance on social media becomes benefits or drawbacks, especially in times of crisis. Using the concepts of social liability, social support, and cognitive ...
Highlights- We examined when dependence on social media becomes promises or pitfalls.
- We ...
Comments
Information & Contributors
Information
Published In
December 2021
541 pages
Copyright © 2021 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 11 April 2022
Check for updates
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
WI-IAT '21
Sponsor:
WI-IAT '21: IEEE/WIC/ACM International Conference on Web Intelligence
December 14 - 17, 2021
VIC, Melbourne, Australia
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 76Total Downloads
- Downloads (Last 12 months)25
- Downloads (Last 6 weeks)1
Reflects downloads up to 09 Aug 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format