OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
AI Data & Analytics

Turftopic

Turftopic is an open-source Python library for contextual topic modeling with transformer-based representations. It includes model approaches for fitting topics to text, plus tools for interpreting, visualizing and analyzing model results.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Turftopic — OpenViST project profile in AI Data & Analytics.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Turftopic when you have text documents and want a Python-based workflow for discovering, inspecting or comparing topics, including analyses that call for specialized model variants.

Who it is for

  • Python practitioners analyzing collections of documents.
  • Researchers comparing topic-model approaches or examining topics across documents and time.
  • Developers adding topic modeling and interpretation to a Python text-analysis workflow.

Documented uses

  • Fit a topic model to a corpus and obtain document-topic assignments.
  • Inspect important words and representative documents for each topic.
  • Review the topic distribution for an individual text.
  • Explore dynamic, online, seeded, hierarchical or multimodal topic-modeling approaches.
Before you choose

Requirements & limits

The repository is public, declares an MIT license and documents installation from PyPI.

  • The models follow scikit-learn API conventions.
  • Some model families call for optional dependencies: Pyro for CTMs and UMAP-learn for clustering models such as BERTopic or Top2Vec.
  • The library includes distinct options for dynamic, online, seeded, hierarchical and multimodal topic modeling.
  • Topic interpretation options include keyphrases, noun phrases, lemmatization, stemming, manual naming and LLM-generated descriptions.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install Turftopic and its documented model extras

Set up the library and add the optional dependencies identified for CTM or clustering-model workflows.

Before you start

  • A Python environment with pip.

Steps

  1. Install the base package with the command below.

    Documented command
    pip install turftopic

  2. For CTMs, install the Pyro extra with the command below.

    Documented command
    pip install "turftopic[pyro-ppl]"

  3. For clustering models such as BERTopic or Top2Vec, install the UMAP-learn extra with the command below.

    Documented command
    pip install "turftopic[umap-learn]"

Expected outcome

The base package is installed, with the documented optional dependency added for the selected model family.

Guide limitations

  • The Pyro and UMAP-learn extras are specified for particular model groups, not as requirements for every Turftopic workflow.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
14 October 2026
Original website title
x-tabdeveloping/turftopic · Official homepage title · retained in its source language
How we review sourcesSuggest a correction