Transformer Based Sentiment Analysis of Russia-Ukraine War using BERT Model

Sentiment analysis is an essential part of gaining deeper insights into particular tweets. Here, we analyse the inclinations that reflect user opinions and their sentiments regarding the tweets about the Russia and Ukraine war. Taking data from social media, we use sentiment analysis to gain insight into the wide range of public opinion. In this paper, we proposed customized Bidirectional Encoder Representations from transformers (BERT) natural language processing model to gauge the tone of the tweets about the war. A proposed model for sentiment analysis is fine- tuned on a dataset related to the Russia- Ukraine war. Proposed model to classify sentiments in domain specific texts to ensure accurate and context aware sentiment predictions. Our unique approach leverages BERTs contextual language understanding model to study Russia- Ukraine relations. The model achieved an accuracy of 94.73% on the fitted dataset. Everyone benefits from this research, and the public as it gives them a better understanding of public opinion. Experiment results shows that the performance of the proposed model is better than the existing approaches.