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

Features

SOTA Transformer-based Topic Models	🧭 S³, 🔑 KeyNMF, 💎 GMM, Clustering Models, CTMs, FASTopic
Models for all Scenarios	📈 Dynamic, 🌊 Online, 🌿 Seeded, and 🌲 Hierarchical topic modeling
Easy Interpretation	📑 Pretty Printing, 📊 Interactive Figures, 🎨 topicwizard compatible
Topic Naming	🤖 LLM-based, N-gram Retrieval, 👋 Manual
Informative Topic Descriptions	🔑 Keyphrases, Noun-phrases, Lemmatization, Stemming

Basics (Documentation)

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]

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.

from sklearn.datasets import fetch_20newsgroups

newsgroups = fetch_20newsgroups(
    subset="all",
    remove=("headers", "footers", "quotes"),
)
corpus = newsgroups.data

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

from turftopic import KeyNMF

model = KeyNMF(20).fit(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()

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
2	christians, christian, bible, christianity, church, god, scripture, faith, jesus, sin
3	encryption, chip, clipper, nsa, security, secure, privacy, encrypted, crypto, cryptography
	....

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

Document	Score
Poor 'Poly'. I see you're preparing the groundwork for yet another retreat from your...	0.40
Then you must be living in an alternate universe. Where were they? An Appeal to Mankind During the...	0.40
It is 'Serdar', 'kocaoglan'. Just love it. Well, it could be your head wasn't screwed on just right...	0.39

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

Topic name	Score
7_gun_guns_firearms_weapons	0.05
17_mail_address_email_send	0.00
3_encryption_chip_clipper_nsa	0.00
19_baseball_pitching_pitcher_hitter	0.00
11_graphics_software_program_3d	0.00

Automated Topic Naming

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

from turftopic import KeyNMF
from turftopic.namers import OpenAITopicNamer

model = KeyNMF(10).fit(corpus)

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

Topic ID	Topic Name	Highest Ranking
0	Operating Systems and Software	windows, dos, os, ms, microsoft, unix, nt, memory, program, apps
1	Atheism and Belief Systems	atheism, atheist, atheists, belief, religion, religious, theists, beliefs, believe, faith
2	Computer Architecture and Performance	motherboard, ram, memory, cpu, bios, isa, speed, 486, bus, performance
3	Storage Technologies	disk, drive, scsi, drives, disks, floppy, ide, dos, controller, boot
	...

Vectorizers Module

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

from turftopic import KeyNMF
from turftopic.vectorizers.spacy import NounPhraseCountVectorizer

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

Topic ID	Highest Ranking
	...
3	fanaticism, theism, fanatism, all fanatism, theists, strong theism, strong atheism, fanatics, precisely some theists, all theism
4	religion foundation darwin fish bumper stickers, darwin fish, atheism, 3d plastic fish, fish symbol, atheist books, atheist organizations, negative atheism, positive atheism, atheism index
	...

Visualization

Turftopic does not come with built-in visualization utilities, topicwizard, an interactive topic model visualization library, is compatible with all models from Turftopic.

pip install 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.

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