Analyzing Sentiments in Twitter Data on Russia-Ukraine Conflict Using Machine Learning Methods

As the conflict between Russia and Ukraine has deepened and more international countries have been engaged, it has become critical to ascertain if the universal message is one of support for or opposition to military activities. This goal is supported by research on how public opinion and foreign affairs are critically connected. As a result, researchers looked at how the global community felt about Russia's invasion of Ukraine. In total, 27,894 tweets with the hashtag #UkraineRussia that were published on the first day were examined. The findings indicate that some of the most often recurring words in the tweets were "war," "people," "world," and "people." Sadness was the most prominent emotion, and negative sentiments were higher than positive sentiments. It was discovered that nearly 89% of conflicts involved the word "world" when a term was supervised in context concordance analysis. This finding underlines the rising worry that the situation between Russia and Ukraine may worsen

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Published
2025-02-06
DOI
https://doi.org/10.1109/cictn64563.2025.10932647
Primary Topic
Information and Cyber Security
Type
article
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Analyzing Sentiments in Twitter Data on Russia-Ukraine Conflict Using Machine Learning Methods

Himanshu K. Patel, Meeta Chaudhry, Rakesh Kumar, Diksha Mishra et al.
Information and Cyber Security
article

Analyzing Sentiments in Twitter Data on Russia-Ukraine Conflict Using Machine Learning Methods

Himanshu K. Patel, Meeta Chaudhry, Rakesh Kumar, Diksha Mishra, Navin Prakash, Ashish
article en

Abstract

As the conflict between Russia and Ukraine has deepened and more international countries have been engaged, it has become critical to ascertain if the universal message is one of support for or opposition to military activities. This goal is supported by research on how public opinion and foreign affairs are critically connected. As a result, researchers looked at how the global community felt about Russia's invasion of Ukraine. In total, 27,894 tweets with the hashtag #UkraineRussia that were published on the first day were examined. The findings indicate that some of the most often recurring words in the tweets were "war," "people," "world," and "people." Sadness was the most prominent emotion, and negative sentiments were higher than positive sentiments. It was discovered that nearly 89% of conflicts involved the word "world" when a term was supervised in context concordance analysis. This finding underlines the rising worry that the situation between Russia and Ukraine may worsen

M.J.P. Rohilkhand University (IN), Noida International University (IN)
Openalex Percentile: Top 93%
Information and Cyber Security
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