Getting Started

Turftopic is a topic modeling library which intends to simplify and streamline the usage of contextually sensitive topic models. We provide stable, minimal and scalable implementations of several types of models along with extensive documentation.

Build and Train Topic Models	Explore, Interpret and Visualize your Models	Modify and Fine-tune Topic Models
Choose the Right Model for your Use-Case	Explore Topics Changing over Time	Use Phrases or Lemmas for Topic Models
Extract Topics from a Stream of Documents	Find Hierarchical Order in Topics	Name Topics with Large Language Models

Basic Usage

Turftopic can be installed from PyPI.

pip install turftopic

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.

from turftopic import KeyNMF
from sklearn.datasets import fetch_20newsgroups

newsgroups = fetch_20newsgroups(
    subset="all",
    remove=("headers", "footers", "quotes"),
)
corpus = newsgroups.data
model = KeyNMF(20).fit(corpus)
model.print_topics()

Topic ID	Top 10 Words
0	armenians, armenian, armenia, turks, turkish, genocide, azerbaijan, soviet, turkey, azerbaijani
1	sale, price, shipping, offer, sell, prices, interested, 00, games, selling
	....

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} 
}