Analysis of Sentiments on Russia-Ukraine Conflict using Machine Learning Techniques

Nowadays, the internet has become an essential part of the current scenario. Due to the rapid expansion of the social network industry, people are using its platforms to share their opinions on current events. It is important to collect and evaluate consumer feedback on products, services, and other topics. It is of utmost importance to determine if an endorsement or criticism of war operations leads to the universal message as the crisis between Russia and Ukraine has worsened and more foreign powers are involved. The abundant writings on the critical connection between public opinion to international relations support the above-mentioned objective. This study focuses mainly on Twitter text data extracted by Twitter API during the Ukraine-Russia war timeline and is available on Kaggle. Sentimental analysis using various supervised machine learning techniques, including Random Forest, Xtreme Gradient Boosting, Support Vector Machine, and Logistic Regression. Also, the comparison of their performance based on various metrics, including the accuracy and precision of the data is presented. As a result, it investigates the opinions and reactions of the global world to Russia’s invasion of Ukraine.