Abstract
Twitter is a social networking and microblogging website with approximately 200 million registered users, of which 100 million are active, million people are active on Twitter. Every day, about 250 million tweets are twitted. The recent Russia- Ukraine dispute drew widespread worldwide attention, and a crisis puts an emergency to the test. With the growth of technology, the usage of social media to exchange information and ideas has increased dramatically. In this research, we use real-time data from Twitter to analyses public sentiment on the Ukraine and Russia crises on a global scale. We want to get a reflection of public sentiment by analyzing the sentiments conveyed in tweets due to the widespread use of Twitter. It is critical to assess public opinion.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Mendoza L, Peinado L, Martinez DL, Campo-Arias A (2004) Prevalence of depressive symptoms in school boys and girls in Bucaramanga, Colombia. Revista Colombiana de PsiquiatrÃa 33:163–171
Choudhury M, Counts S, Horvitz E (2013) Social media as a measurement tool of depression in populations. In: Proceedings of the 5th Annual ACM web science conference websci2013, 47–56. https://doi.org/10.1145/2464464.2464480
Coppersmith G, Harman C, Dredze M (2014) Measuring post traumatic stress disorder in Twitter. In: Eighth international AAAI conference on weblogs and social media
Veientlena S, Prabu P (2018) Prevalence of hypertension and determination of its risk factors in Korangrapady, Udupi District, Coastal Karnataka, India. Prevalence 11(6)
Verma P, Dumka A, Ashok A, Dumka A, Bhardwaj A (2021) Covid-19: origin, detection and impact analysis using artificial intelligence computational techniques, 1st edn. CRC Press. https://doi.org/10.1201/9781003131410
Reece AG, Reagan AJ, Lix KL, Dodds PS, Danforth CM, Langer EJ (2017) Forecasting the onset and course of mental illness with Twitter data. Sci Rep 7(1):1–11
Haselmayer M, Jenny M (2017) Sentiment analysis of political communication: combining a dictionary approach with crowdcoding. Qual Quant 51(6):2623–2646
Dumka A, Verma P, Singh R, Bisht K, Anand D, Aljahdali M, Noya D, Obregón AS (2022) A novel deep learning based healthcare model for COVID-19 pandemic stress analysis. Comput Mat Contin 72(3):6029–6044. ISSN 1546-2226
Verma P, Dumka A, Bhardwaj A et al (2021) A statistical analysis of impact of COVID19 on the global economy and stock index returns. SN Comput Sci 2:27. https://doi.org/10.1007/s42979-020-00410
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chaudhari, V., Dumka, A., Rastogi, N., Ashok, A., Pant, B. (2023). Twitter Sentiment Analysis on Russia Ukraine War. In: Goar, V., Kuri, M., Kumar, R., Senjyu, T. (eds) Advances in Information Communication Technology and Computing. Lecture Notes in Networks and Systems, vol 628. Springer, Singapore. https://doi.org/10.1007/978-981-19-9888-1_49
Download citation
DOI: https://doi.org/10.1007/978-981-19-9888-1_49
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-9887-4
Online ISBN: 978-981-19-9888-1
eBook Packages: EngineeringEngineering (R0)