BERTopic

BERTopic approaches topic modeling with a modular pipeline built on transformer embeddings: sentence-transformers encode documents, UMAP reduces dimensionality, HDBSCAN clusters the results, and a class-based TF-IDF procedure (c-TF-IDF) extracts interpretable keywords for each topic. Every stage can be swapped, which lets one library support more than a dozen variants including guided, supervised, semi-supervised, dynamic topics over time, hierarchical, zero-shot, online or incremental, and multimodal topic modeling over images and text. Topic representations can be refined with LLMs such as OpenAI models, and multilingual embedding models extend coverage to over 50 languages. Interactive visualizations show topic hierarchies, similarity heatmaps, and per-document distributions. Created and maintained by Maarten Grootendorst, released under the MIT license with an accompanying paper, BERTopic is widely used by researchers and data scientists analyzing large document collections. It requires Python 3.10 or newer and installs from PyPI.