Sentiment Analysis of Russia-Ukraine Conflict Tweets Using RoBERTa




Sentiment analysis, Russia-Ukraine conflict, Twitter, DistilRoBERTa


[Objective] The moment Russia officially invaded Ukraine, the world experienced a period of tension and uncertainty. As a social release valve digital communication, channels increased their number of users and activity, generating a large amount of data. Twitter, in particular, being one of the most popular channels for sharing information and opinions, exploded with activities related to this historical moment. And as with many other social events, such as COVID-19, this social network became one of the main sources of information, opinion, and knowledge. This paper analyzes sentiments in tweets related to the armed conflict between Russia and Ukraine. [Methodology] The analyzed dataset contains tweets from January 1, 2022, through March 3, 2022, and was collected using event-related hashtags. In total, 603,552 tweets in English and 1,664 in Russian were analyzed. To perform emotion classification, DistilRoBERTa variant and the pre-trained XLM-RoBERTa-Base model were used, respectively. English tweets were classified into seven emotions: anger, disgust, fear, joy, neutral, sadness, and surprise. Russian tweets were classified into positive negative, and neutral polarities. [Results] The results showed that most English tweets convey fear and anger as predominant feelings, reaching 32.08% and 15.18% of the total tweets analyzed, respectively. Regarding tweets in Russian, the majority presented negative polarity, with 86.83% of the total. Some of the most recurrent phrases in the analysis allude to support for Ukraine and call for a halt to the war. Likewise, phrases of concern for the crisis, weapons, and fatalities are recurrent. [Conclusion] As expected, most people were concerned about the armed conflict and upset and angry about its consequences. Future works could use more tweets to improve the analysis and increase the time range to be studied. The analysis could also be segmented to study the sentiments of tweets according to different groupings and compare them with other societies, for instance, tweets could be segmented by country and analyzed accordingly.


Ashman, M., & Cruthers, A. (2021). Tables, charts, and graphs . Advanced Professional Communication, eCampus Pressbooks.

Dougherty, J., & Ilyankou, I. (2021). Hands-On Data Visualization. O'Reilly Media, Inc.

Ekman, P. (1992). An argument for basic emotions. Cognition & emotion, 6, 169-200.

Hartmann, J. (2022). Emotion English DistilRoBERTa-base.

Imran, A., Daudpota, S., Kastrati, Z., & Batra, R. (2020). Cross-cultural polarity and emotion detection using sentiment analysis and deep learning on covid-19 related tweets. IEEE Access, 8, 181074-181090.

Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31, 685-695.

Jianqiang, Z., & Xiaolin, G. (2017). Comparison Research on Text Pre-processing Methods on Twitter Sentiment Analysis. IEEE Access, 2870-2879.

Karin, E. (2019). Primate Ecology and Behavior. Retrieved from

Khun, N., & Thant, H. (2019). Visualization of Twitter Sentiment during the Period of US Banned Huawei. International Conference on Advanced Information Technologies, ICAIT 2019 (pp. 274-279). Institute of Electrical and Electronics Engineers Inc.

Kret, M., Prochazkova, E., Sterck, E., & Clay, Z. (2020). Emotional expressions in human and non-human great apes. Neuroscience & Biobehavioral Reviews, 115.

Li, Q. (2020). Overview of Data Visualization. Embodying Data, 17-47. doi:10.1007/978-981-15-5069-0_2

Mbah, R., & Wasum, D. (2022). Russian-Ukraine 2022 War: A Review of the Economic Impact of Russian-Ukraine Crisis on the USA, UK, Canada, and Europe. Advances in Social Sciences Research Journal, 9, 144-153.

Mota, F., & Cilento, I. (2020). Competence for internet use: Integrating knowledge, skills, and attitudes. Computers and Education Open, 1, 115.

Neogi, A., Garg, K., Mishra, R., & Dwivedi, Y. (2021). Sentiment analysis and classification of Indian farmers’ protest using twitter data. International Journal of Information Management Data Insights, 1(2), 100019.

Pota, M., Ventura, M., Fujita, H., & Esposito, M. (2021). Multilingual evaluation of pre-processing for BERT-based sentiment analysis of tweets. Expert Systems with Applications, 115119.

Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. ArXiv.

Signe, P. (2000). Primate Faces and Facial Expressions. Social Research, 67, 245-271.

Smetanin, S., & Komarov, M. (2019). Sentiment Analysis of Product Reviews in Russian using Convolutional Neural Networks. 2019 IEEE 21st Conference on Business Informatics (CBI) (pp. 482-486). IEEE.

Smetanin, S., & Komarov, M. (2021). Deep transfer learning baselines for sentiment analysis in Russian. Information Processing & Management, 102484.

Tomkins, S. (1962). Affect imagery consciousness: Volume I: The positive affects. Springer publishing company.

Zhang, C., & Lu, Y. (2021). Study on artificial intelligence: The state of the art and future prospects. Journal of Industrial Information Integration, 23.



How to Cite

Sentiment Analysis of Russia-Ukraine Conflict Tweets Using RoBERTa. (2023). Uniciencia, 37(1), 1-11.



Original scientific papers (evaluated by academic peers)

How to Cite

Sentiment Analysis of Russia-Ukraine Conflict Tweets Using RoBERTa. (2023). Uniciencia, 37(1), 1-11.

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