Exploring Sentiments in the Russia-Ukraine Conflict: A Comparative Analysis of KNN, Decision Tree And Logistic Regression Machine Learning Classifiers.

The outbreak of conflict between Russia and Ukraine on February 23, 2022, was a huge shock, and it quickly became the dominant topic of conversation on social media. Our research is based on Twitter conversations about the conflict between Russia and Ukraine. This paper intends to monitor and analyze the Twitter conversation on the continuing war in Ukraine using natural language processing techniques such as word cloud analysis, sentiment analysis, and visualization. The major goal is to acquire insight into Twitter users' shifting thoughts and opinions on various parts of the conflict over time. The study's findings have important implications for politicians, journalists, and social media users who want to follow and understand how online communities' attitudes on global issues change. We hope that this study will help to gain a deeper knowledge of how social media works in affecting public debate and how it might be used to gain insights into public opinion on crucial global issues. In this work logistic regression, K-nearest neighbours and decision tree algorithm are used to analyse the sentiments and got classification accuracy of 94.58%, 88.89% and 90.45% respectively. [ABSTRACT FROM AUTHOR]