Topic modeling is your turf too.
Contextual topic models with representations from transformers.

Features

Basics

For more details on a particular topic, you can consult our documentation page:

Installation

Turftopic can be installed from PyPI.

pip install turftopic

If you intend to use CTMs, make sure to install the package with Pyro as an optional dependency.

pip install "turftopic[pyro-ppl]"

If you want to use clustering models like BERTopic or Top2Vec, install:

pip install "turftopic[umap-learn]"

Fitting a Model

Turftopic's models follow the scikit-learn API conventions, and as such they are quite easy to use if you are familiar with scikit-learn workflows.

Here's an example of how you use KeyNMF, one of our models on the 20Newsgroups dataset from scikit-learn.

If you are using a Mac, you might have to install the required SSL certificates on your system in order to be able to download the dataset.

from sklearn.datasets import fetch_20newsgroups

newsgroups = fetch_20newsgroups(
    subset="all",
    remove=("headers", "footers", "quotes"),
)
corpus: list[str] = newsgroups.data
print(len(corpus)) # 18846

Turftopic also comes with interpretation tools that make it easy to display and understand your results.

from turftopic import KeyNMF

model = KeyNMF(20)
document_topic_matrix = model.fit_transform(corpus)

Interpreting Models

Turftopic comes with a number of pretty printing utilities for interpreting the models.

To see the highest the most important words for each topic, use the print_topics() method.

model.print_topics()

# Print highest ranking documents for topic 0
model.print_representative_documents(0, corpus, document_topic_matrix)

model.print_topic_distribution(
    "I think guns should definitely banned from all public institutions, such as schools."
)

Automated Topic Naming

Turftopic now allows you to automatically assign human readable names to topics using LLMs or n-gram retrieval!

You will need to pip install "turftopic[openai]" for this to work.

from turftopic import KeyNMF
from turftopic.analyzers import OpenAIAnalyzer

model = KeyNMF(10).fit(corpus)

namer = OpenAIAnalyzer("gpt-4o-mini")
model.rename_topics(namer)
model.print_topics()

Vectorizers Module

You can use a set of custom vectorizers for topic modeling over phrases, as well as lemmata and stems.

You will need to pip install "turftopic[spacy]" for this to work.

from turftopic import BERTopic
from turftopic.vectorizers.spacy import NounPhraseCountVectorizer

model = BERTopic(
    n_components=10,
    vectorizer=NounPhraseCountVectorizer("en_core_web_sm"),
)
model.fit(corpus)
model.print_topics()

Visualization

Turftopic comes with a number of visualization and pretty printing utilities for specific models and specific contexts, such as hierarchical or dynamic topic modelling. You will find an overview of these in the Interpreting and Visualizing Models section of our documentation.

pip install "turftopic[datamapplot, openai]"

from turftopic import ClusteringTopicModel
from turftopic.analyzers import OpenAIAnalyzer

model = ClusteringTopicModel(feature_importance="centroid").fit(corpus)

namer = OpenAIAnalyzer("gpt-5-nano")
model.rename_topics(namer)

fig = model.plot_clusters_datamapplot()
fig.show()

In addition, Turftopic is natively supported in topicwizard, an interactive topic model visualization library, is compatible with all models from Turftopic.

pip install "turftopic[topic-wizard]"

By far the easiest way to visualize your models for interpretation is to launch the topicwizard web app.

import topicwizard

topicwizard.visualize(corpus, model=model)

Screenshot of the topicwizard Web Application

Alternatively you can use the Figures API in topicwizard for individual HTML figures.

Citation

Please cite us when using Turftopic:

@article{
  Kardos2025,
  title = {Turftopic: Topic Modelling with Contextual Representations from Sentence Transformers},
  doi = {10.21105/joss.08183},
  url = {https://doi.org/10.21105/joss.08183},
  year = {2025},
  publisher = {The Open Journal},
  volume = {10},
  number = {111},
  pages = {8183},
  author = {Kardos, Márton and Enevoldsen, Kenneth C. and Kostkan, Jan and Kristensen-McLachlan, Ross Deans and Rocca, Roberta},
  journal = {Journal of Open Source Software} 
}

References

• Kardos, M., Kostkan, J., Vermillet, A., Nielbo, K., Enevoldsen, K., & Rocca, R. (2024, June 13). $S^3$ - Semantic Signal separation. arXiv.org. https://arxiv.org/abs/2406.09556
• Wu, X., Nguyen, T., Zhang, D. C., Wang, W. Y., & Luu, A. T. (2024). FASTopic: A Fast, Adaptive, Stable, and Transferable Topic Modeling Paradigm. ArXiv Preprint ArXiv:2405.17978.
• Grootendorst, M. (2022, March 11). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv.org. https://arxiv.org/abs/2203.05794
• Angelov, D. (2020, August 19). Top2VEC: Distributed representations of topics. arXiv.org. https://arxiv.org/abs/2008.09470
• Bianchi, F., Terragni, S., & Hovy, D. (2020, April 8). Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence. arXiv.org. https://arxiv.org/abs/2004.03974
• Bianchi, F., Terragni, S., Hovy, D., Nozza, D., & Fersini, E. (2021). Cross-lingual Contextualized Topic Models with Zero-shot Learning. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume (pp. 1676–1683). Association for Computational Linguistics.
• Kristensen-McLachlan, R. D., Hicke, R. M. M., Kardos, M., & Thunø, M. (2024, October 16). Context is Key(NMF): Modelling Topical Information Dynamics in Chinese Diaspora Media. arXiv.org. https://arxiv.org/abs/2410.12791