# Russia-Ukraine war: Modeling and Clustering the Sentiments Trends of Various Countries

arXiv: https://arxiv.org/abs/2301.00604 · HTML: https://mlsanity.com/paper/2301.00604/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 2 Jan 2023.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Extracting Major Topics of COVID-19 Related Tweets](https://mlsanity.com/paper/2110.01876/) (2021)
- [Analyzing the Impact of COVID-19 on Economy from the Perspective of Users Reviews](https://mlsanity.com/paper/2110.02198/) (2021)
- [Extracting Feelings of People Regarding COVID-19 by Social Network Mining](https://mlsanity.com/paper/2110.06151/) (2021)
- [Investigating the Impact of COVID-19 on Education by Social Network Mining](https://mlsanity.com/paper/2203.06584/) (2022)

## Built on by (most cited first, 1 of 1)

- [Unveiling Global Narratives: A Multilingual Twitter Dataset of News Media on the Russo-Ukrainian Conflict](https://mlsanity.com/paper/2306.12886/) (2023)

## Abstract (arXiv)

With Twitter's growth and popularity, a huge number of views are shared by users on various topics, making this platform a valuable information source on various political, social, and economic issues. This paper investigates English tweets on the Russia-Ukraine war to analyze trends reflecting users' opinions and sentiments regarding the conflict. The tweets' positive and negative sentiments are analyzed using a BERT-based model, and the time series associated with the frequency of positive and negative tweets for various countries is calculated. Then, we propose a method based on the neighborhood average for modeling and clustering the time series of countries. The clustering results provide valuable insight into public opinion regarding this conflict. Among other things, we can mention the similar thoughts of users from the United States, Canada, the United Kingdom, and most Western European countries versus the shared views of Eastern European, Scandinavian, Asian, and South American nations toward the conflict.