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Bertopic

Bertopic is the public project for BERTopic, a Python library that uses BERT-based text representations and class-based TF-IDF to discover and represent interpretable topics in document collections.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Bertopic — 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 BERTopic when a text collection needs data-driven topic discovery with topic descriptions that retain important representative words. Its documented modeling modes also support workflows that need supervision, guidance, hierarchy, multiple topics per document or temporal analysis.

Who it is for

  • Python developers building natural-language processing workflows.
  • Researchers exploring themes in text corpora.
  • Teams analyzing customer feedback, support tickets or survey responses.

Documented uses

  • Cluster customer feedback into interpretable themes.
  • Explore document-topic relationships in research corpora.
  • Create topic dashboards for support tickets or survey responses.
  • Analyze how themes change across documents over time.
Before you choose

Requirements & limits

The repository is public, open source and MIT-licensed. Installation is documented through uv or pip for Python environments.

  • The core approach combines BERT-based representations with c-TF-IDF.
  • The project documents guided, supervised, semi-supervised, hierarchical, dynamic, online, multimodal, multi-aspect and zero-shot approaches.
  • Embedding choices include Transformers and documented alternative backends such as Flair, Gensim, spaCy, USE and Model2Vec-related lightweight workflows.
  • A vision installation extra is documented for topic modeling with images.
  • The repository identifies Python as its primary language and lists NLP, sentence embeddings, Transformers and topic modeling among its topics.

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 BERTopic in a Python environment

Set up the BERTopic library with its documented sentence-transformers installation.

Before you start

  • A Python environment.
  • uv or pip.

Steps

  1. Add BERTopic with uv using the documented package command.

  2. Alternatively, install BERTopic with pip using the documented package command.

  3. For a different embedding backend, select the documented Flair, Gensim, spaCy or USE extra.

Expected outcome

BERTopic is added to the selected Python environment with the chosen embedding setup.

Guide limitations

  • The installation notes do not establish that a particular embedding backend is suitable for every corpus or workflow.
  • Additional configuration is required for topic modeling with images or for lightweight installations.
Documentation 1 ↗Documentation 2 ↗
Evidence and transparency

Sources & review

Official repository ↗Guide documentation ↗
Source record details
Source availability
Active
Sources checked
2 October 2026
Original website title
MaartenGr/BERTopic · Official homepage title · retained in its source language
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