In today’s interconnected world, the number of social media users, such as Twitter, Facebook, and Reddit, has continued to grow. The vast number of users generates a wealth of social data, which holds significant value regarding semantic sentiment, public opinion, and societal interests. This study delves into topics and sentiments of Facebook page contents related to the Russo-Ukrainian War. In specific, the study employs the unsupervised text clustering method, BERTopic, to identify the predominant themes in these Facebook posts. Additionally, we leverage VADER (Valence Aware Dictionary and sentiment Reasoner) to uncover the semantic emotions conveyed in the posts. The study revealed the main topics discussed on Facebook during the war and the changes in sentiment distribution across different topics over time.