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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.
|pypiv| |pyv| |Licence| |Build Status| |Coverage Status|
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.
|Demo|
Using pip
.. code:: bash
pip install rake-nltk
Directly from the repository
.. code:: bash
git clone https://github.com/csurfer/rake-nltk.git
python rake-nltk/setup.py install
.. code:: python
from rake_nltk import Rake
# Uses stopwords for english from NLTK, and all puntuation characters by
# default
r = Rake()
# Extraction given the text.
r.extract_keywords_from_text(<text to process>)
# Extraction given the list of strings where each string is a sentence.
r.extract_keywords_from_sentences(<list of sentences>)
# To get keyword phrases ranked highest to lowest.
r.get_ranked_phrases()
# To get keyword phrases ranked highest to lowest with scores.
r.get_ranked_phrases_with_scores()
If you see a stopwords error, it means that you do not have the corpus
stopwords
downloaded from NLTK. You can download it using command below.
.. code:: bash
python -c "import nltk; nltk.download('stopwords')"
This is a python implementation of the algorithm as mentioned in paper
Automatic keyword extraction from individual documents by Stuart Rose, Dave Engel, Nick Cramer and Wendy Cowley
_
aneesha/RAKE
) and
node(\ waseem18/node-rake
) but neither seemed to use the power of NLTK
_. By making NLTK
an integral part of the implementation I get the flexibility and power to extend it in other
creative ways, if I see fit later, without having to implement everything myself.Bug Reports and Feature Requests
Please use `issue tracker`_ for reporting bugs or feature requests.
Development
~~~~~~~~~~~
1. Checkout the repository.
2. Make your changes and add/update relavent tests.
3. Install **`poetry`** using **`pip install poetry`**.
4. Run **`poetry install`** to create project's virtual environment.
5. Run tests using **`poetry run tox`** (Any python versions which you don't have checked out will fail this). Fix failing tests and repeat.
6. Make documentation changes that are relavant.
7. Install **`pre-commit`** using **`pip install pre-commit`** and run **`pre-commit run --all-files`** to do lint checks.
8. Generate documentation using **`poetry run sphinx-build -b html docs/ docs/_build/html`**.
9. Generate **`requirements.txt`** for automated testing using **`poetry export --dev --without-hashes -f requirements.txt > requirements.txt`**.
10. Commit the changes and raise a pull request.
Buy the developer a cup of coffee!
If you found the utility helpful you can buy me a cup of coffee using
|Donate|
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.. _Automatic keyword extraction from individual documents by Stuart Rose, Dave Engel, Nick Cramer and Wendy Cowley: https://www.researchgate.net/profile/Stuart_Rose/publication/227988510_Automatic_Keyword_Extraction_from_Individual_Documents/links/55071c570cf27e990e04c8bb.pdf .. _aneesha/RAKE: https://github.com/aneesha/RAKE .. _waseem18/node-rake: https://github.com/waseem18/node-rake .. _NLTK: http://www.nltk.org/ .. _issue tracker: https://github.com/csurfer/rake-nltk/issues
.. |Build Status| image:: https://github.com/csurfer/rake-nltk/actions/workflows/pytest.yml/badge.svg :target: https://github.com/csurfer/rake-nltk/actions .. |Licence| image:: https://img.shields.io/badge/license-MIT-blue.svg :target: https://raw.githubusercontent.com/csurfer/rake-nltk/master/LICENSE .. |Coverage Status| image:: https://codecov.io/gh/csurfer/rake-nltk/branch/master/graph/badge.svg?token=ghRhWVec9X :target: https://codecov.io/gh/csurfer/rake-nltk .. |Demo| image:: http://i.imgur.com/wVOzU7y.gif .. |pypiv| image:: https://img.shields.io/pypi/v/rake-nltk.svg :target: https://pypi.python.org/pypi/rake-nltk .. |pyv| image:: https://img.shields.io/pypi/pyversions/rake-nltk.svg :target: https://pypi.python.org/pypi/rake-nltk
FAQs
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.
We found that rake-nltk demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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