---
id: rake-nltk
version: "1.0.6"
license: MIT
license_treatment: permissive
maintenance: abandoned
---

# rake-nltk — RAKE short for Rapid Automatic Keyword Extraction algorithm, is a domain independent keyword extraction algorithm which tries to determine key phrases in a body of text by analyzing the frequency of word appearance and its co-occurance with other words in the text.

License: permissive · Maintenance: abandoned · Downloads: 316.2K/mo

## What it is and what it does

rake-nltk is a Python implementation of the RAKE (Rapid Automatic Keyword Extraction) algorithm, which identifies important phrases in text by analyzing word frequency and co-occurrence patterns. It wraps NLTK to provide configurable tokenization, language-specific stop words, and ranking metrics, making it suitable for extracting domain-independent keywords from documents without manual tuning.

The package offers a simple interface: initialize a Rake object, pass text or sentences to it, and retrieve ranked phrases either as a list or with their scores. It is designed to be modular and tunable, though it has not been actively maintained since late 2022 and is now abandoned.

Use it for:

- Extract key topics from research papers or articles for quick summarization and indexing.
- Identify important terms from customer feedback or survey responses for sentiment analysis.
- Generate tag suggestions for blog posts or documents based on automatic phrase detection.
- Build a keyword index for search engines or information retrieval systems.
- Analyze text corpora to discover domain terminology without pre-labeled training data.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Extracts keywords and key phrases from text using the RAKE algorithm, which ranks words by their frequency and co-occurrence patterns to identify domain-independent topic terms.

Yes, if you need a lightweight, simple keyword extraction tool and can tolerate an abandoned package. The MIT license is permissive, install friction is low, and there are no known vulnerabilities. However, be aware that no updates or maintenance will occur; test thoroughly with your Python version and nltk version before relying on it in production. For active projects requiring ongoing support, consider maintained alternatives.

## Install

pip install rake-nltk
uv add rake-nltk
poetry add rake-nltk

## Installing rake-nltk

Before you install: Low friction install with a single runtime dependency (nltk). However, the package is abandoned—last release was 2021-09-15 and last commit 2022-12-09—so expect no maintenance, bug fixes, or updates to support newer Python versions beyond what's already declared.

License in practice: MIT license is permissive; you can use, modify, and distribute this package freely in commercial or private projects with minimal restrictions.

Quickstart:

pip install rake-nltk

from rake_nltk import Rake

r = Rake()
r.extract_keywords_from_text("your text here")
print(r.get_ranked_phrases_with_scores())

Requires NLTK's stopwords corpus; download it first with: python -c "import nltk; nltk.download('stopwords')"

Verify before relying:

- Whether the package works reliably with Python versions beyond 3.9 despite being abandoned.
- Performance characteristics on large documents or high-volume keyword extraction tasks.
- Compatibility with recent versions of nltk and whether dependency version pinning is needed.

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: abandoned
- Downloads: 316.2K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

keyword extraction, automatic phrase detection, text mining nlp, rake algorithm, key phrase ranking, document summarization, nlp keyword analysis, keyword-extraction, nlp, text-analysis

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