andrewtavis/kwx
BERT, LDA, and TFIDF based keyword extraction in Python
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.
Stars
76
Forks
12
Language
Python
License
BSD-3-Clause
Category
Last pushed
Feb 28, 2026
Commits (30d)
0
Dependencies
20
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/andrewtavis/kwx"
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