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.

pip install rake-nltk

git clone https://github.com/csurfer/rake-nltk.git
python rake-nltk/setup.py install

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.

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

• 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) 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.
• 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.

Please use issue tracker for reporting bugs or feature requests.

Pull requests are most welcome.

If you found the utility helpful you can buy me a cup of coffee using