Abstract
The Russia-Ukraine War
has dramatically impacted the world, affecting economies, lives, and politics.
The war is a common topic on social media, especially on platforms like
YouTube. In this study, we analyzed YouTube comments from videos posted by
popular news channels like CNN,
BBC, etc., to understand people’s opinions about the war. We used a tool called
VADER for sentiment
analysis and an unsupervised BERT model
to identify ten key topics related to the war, including humanitarian issues,
economic challenges, political debates, and societal
concerns. We then created a model that combines BERT’s ability to
understand context with CNN’s feature extraction strengths. Unlike existing
approaches, our model incorporates an extra input layer that considers the
topic as a significant feature. This hybrid model effectively classifies
sentiments with 92.26% accuracy. Our research provides insights into public
perceptions and discussions about the Russia-Ukraine War, highlighting
essential themes in the conversation.