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https://github.com/csurfer/rake-nltk
Python implementation of the Rapid Automatic Keyword Extraction algorithm using NLTK.
https://github.com/csurfer/rake-nltk
algorithm keyword-extraction nltk python text-mining
Last synced: 29 days ago
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Python implementation of the Rapid Automatic Keyword Extraction algorithm using NLTK.
- Host: GitHub
- URL: https://github.com/csurfer/rake-nltk
- Owner: csurfer
- License: mit
- Created: 2017-01-18T08:17:09.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2022-12-09T05:43:43.000Z (almost 2 years ago)
- Last Synced: 2024-10-01T16:24:03.584Z (about 1 month ago)
- Topics: algorithm, keyword-extraction, nltk, python, text-mining
- Language: Python
- Homepage: https://csurfer.github.io/rake-nltk
- Size: 477 KB
- Stars: 1,061
- Watchers: 32
- Forks: 149
- Open Issues: 25
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.rst
- License: LICENSE
Awesome Lists containing this project
README
# rake-nltk
[](https://pypi.python.org/pypi/rake-nltk)
[](https://pypi.python.org/pypi/rake-nltk)
[](https://github.com/csurfer/rake-nltk/actions)
[](https://codecov.io/gh/csurfer/rake-nltk)
[](https://raw.githubusercontent.com/csurfer/rake-nltk/master/LICENSE)
[](https://pepy.tech/project/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.
## Features
* Ridiculously simple interface.
* Configurable word and sentence tokenizers, language based stop words etc
* Configurable ranking metric.
## Setup
### Using pip
```bash
pip install rake-nltk
```
### Directly from the repository
```bash
git clone https://github.com/csurfer/rake-nltk.git
python rake-nltk/setup.py install
```
## Quick start
```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()
# Extraction given the list of strings where each string is a sentence.
r.extract_keywords_from_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()
```
## Debugging Setup
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.
```bash
python -c "import nltk; nltk.download('stopwords')"
```
## References
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](https://www.researchgate.net/profile/Stuart_Rose/publication/227988510_Automatic_Keyword_Extraction_from_Individual_Documents/links/55071c570cf27e990e04c8bb.pdf)
## Why I chose to implement it myself?
- It is extremely fun to implement algorithms by reading papers. It is the digital equivalent of DIY kits.
- There are some rather popular implementations out there, in python([aneesha/RAKE](https://github.com/aneesha/RAKE)) and node([waseem18/node-rake](https://github.com/waseem18/node-rake)) but neither seemed to use the power of [NLTK](http://www.nltk.org/). 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.
- I plan to use it in my other pet projects to come and wanted it to be modular and tunable and this way I have complete control.
## Contributing
### Bug Reports and Feature Requests
Please use [issue tracker](https://github.com/csurfer/rake-nltk/issues) 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
[](https://www.paypal.com/cgi-bin/webscr?cmd=_donations&business=3BSBW7D45C4YN&lc=US¤cy_code=USD&bn=PP%2dDonationsBF%3abtn_donate_SM%2egif%3aNonHosted)