SENTIMENT ANALYSIS OF PUBLIC OPINION REGARDING THE WAR IN UKRAINE BASED ON REDDIT DATA USING NLP AND MACHINE LEARNING METHODS

Abstract

The article examines public opinion among Americans regarding the war in Ukraine by analysing text data from the social platform Reddit. The relevance of the work lies in the significant influence of public sentiment in the United States on the formation of foreign policy and on public support for Ukraine. The purpose of the study is to conduct a comprehensive sentiment analysis of English-language comments of Reddit users using modern methods of natural language processing (NLP) and machine learning. The work uses a dataset from the Kaggle platform that contains millions of comments on the Russian-Ukrainian war. Preprocessing of text data was carried out, including cleaning, tokenisation, lemmatisation and removal of stop words. For tone analysis, both classical approaches (VADER, TextBlob) and modern machine learning models were used, including Logistic Regression, Random Forest, SVM, XGBoost, and Naive Bayes. A hybrid approach to text vectorisation (TF-IDF with Word2Vec) was implemented. The results obtained allow us to determine the distribution of emotional assessments (positive, negative, neutral), identify thematic clusters of discussions and investigate the dynamics of changes in public sentiment over time. A comparative analysis of the effectiveness of the models based on the main quality metrics was conducted. Particular attention was paid to the specifics of Reddit discourse, including sarcasm, irony and political polarisation. The practical value of the study lies in the creation of analytical tools for monitoring public opinion, which can be used in diplomacy, politics, and the media to develop effective communication strategies.