Integrated Sentiment and Emotion Analysis of the Ukraine-Russia Conflict Using Machine Learning and Transformer Models

Mohammad Hossein Amirhosseini, Nabeela Berardinelli, Kunal Gaikwad, Christian Eze Iwuchukwu, Mahmud Ahmed

2025

Abstract

The Russia-Ukraine war has been a significant international conflict, generating a wide range of public sentiments. With escalating geopolitical tensions, determining whether public discourse supports or condemns the invasion has become increasingly important. This study investigates public attitudes through large-scale sentiment analysis of 1,426,310 tweets collected during the early phase of the conflict. Sentiment classification was performed using machine learning models, including XGBoost, Random Forest, Naïve Bayes, Support Vector Machine, and a Feedforward Deep Learning model, combined with Count Vectorizer and TF-IDF. The deep learning model with Count Vectorizer achieved the highest accuracy at 89.58%, outperforming all others. To go beyond polarity classification, emotion prediction was also conducted using a lexicon-based method (NRC Emotion Lexicon) and a transformer-based model (DistilRoBERTa), both trained to classify tweets into eight emotions: joy, trust, surprise, fear, anger, sadness, disgust, and anticipation. A comparative evaluation showed that the transformer model significantly outperformed the lexicon-based model across all metrics, including accuracy, precision, recall, F1 score, and Hamming loss. Fear and anger emerged as the most dominant emotions, highlighting widespread public anxiety and distress. This analysis provides a nuanced understanding of online discourse during conflict and offers insights for researchers, policymakers, and communicators responding to global crises.

Paper Citation

in Harvard Style

Amirhosseini M., Berardinelli N., Gaikwad K., Iwuchukwu C. and Ahmed M. (2025). Integrated Sentiment and Emotion Analysis of the Ukraine-Russia Conflict Using Machine Learning and Transformer Models. In Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-758-0, SciTePress, pages 191-202. DOI: 10.5220/0013645500003967

in Bibtex Style

@conference{data25,
author={Mohammad Amirhosseini and Nabeela Berardinelli and Kunal Gaikwad and Christian Iwuchukwu and Mahmud Ahmed},
title={Integrated Sentiment and Emotion Analysis of the Ukraine-Russia Conflict Using Machine Learning and Transformer Models},
booktitle={Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2025},
pages={191-202},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013645500003967},
isbn={978-989-758-758-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - Integrated Sentiment and Emotion Analysis of the Ukraine-Russia Conflict Using Machine Learning and Transformer Models
SN - 978-989-758-758-0
AU - Amirhosseini M.
AU - Berardinelli N.
AU - Gaikwad K.
AU - Iwuchukwu C.
AU - Ahmed M.
PY - 2025
SP - 191
EP - 202
DO - 10.5220/0013645500003967
PB - SciTePress