andrewtavis/kwx

BERT, LDA, and TFIDF based keyword extraction in Python

60
/ 100
Established

This tool helps you quickly understand the main subjects and themes in large amounts of text. You input a collection of documents, like survey responses or social media posts, and it outputs a list of relevant keywords or topics. This is ideal for researchers, marketers, or anyone who needs to make sense of qualitative data without manually reading every document.

Available on PyPI.

Use this if you need to extract key terms and themes from a collection of text documents, such as customer feedback, research abstracts, or news articles, to gain insights efficiently.

Not ideal if you need a very deep, nuanced human interpretation of texts or if your dataset is too small to benefit from statistical keyword extraction.

content-analysis text-mining market-research social-listening information-retrieval
Maintenance 10 / 25
Adoption 9 / 25
Maturity 25 / 25
Community 16 / 25

How are scores calculated?

Stars

76

Forks

12

Language

Python

License

BSD-3-Clause

Last pushed

Feb 28, 2026

Commits (30d)

0

Dependencies

20

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curl "https://pt-edge.onrender.com/api/v1/quality/nlp/andrewtavis/kwx"

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