  Topic models can be useful tools to discover latent topics in collections of
documents. Recent studies have shown the feasibility of approach topic modeling
as a clustering task. We present BERTopic, a topic model that extends this
process by extracting coherent topic representation through the development of
a class-based variation of TF-IDF. More specifically, BERTopic generates
document embedding with pre-trained transformer-based language models, clusters
these embeddings, and finally, generates topic representations with the
class-based TF-IDF procedure. BERTopic generates coherent topics and remains
competitive across a variety of benchmarks involving classical models and those
that follow the more recent clustering approach of topic modeling.
