BERTopic leverages BERT and sentence embeddings to represent documents semantically, then applies a class-based TF-IDF technique to extract coherent topics from the clusters. It supports guided, supervised, semi-supervised, manual, hierarchical, and probabilistic topic modeling approaches. The library integrates with Hugging Face transformers and various embedding backends including sentence-transformers for flexible document representation.