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    "info": {
        "author": "Vishwas B Sharma",
        "author_email": "sharma.vishwas88@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Education",
            "License :: OSI Approved :: MIT License",
            "Operating System :: POSIX",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Software Development :: Build Tools",
            "Topic :: Software Development :: Libraries :: Python Modules"
        ],
        "description": "rake-nltk\n=========\n\n|pypiv| |pyv| |Licence| |Build Status| |Coverage Status| |Thanks|\n\nRAKE short for Rapid Automatic Keyword Extraction algorithm, is a domain\nindependent keyword extraction algorithm which tries to determine key\nphrases in a body of text by analyzing the frequency of word appearance\nand its co-occurance with other words in the text.\n\n|Demo|\n\nSetup\n-----\n\nUsing pip\n~~~~~~~~~\n\n.. code:: bash\n\n    pip install rake-nltk\n\nDirectly from the repository\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n.. code:: bash\n\n    git clone https://github.com/csurfer/rake-nltk.git\n    python rake-nltk/setup.py install\n\nPost setup\n----------\n\nIf you see a stopwords error, it means that you do not have the corpus\n`stopwords` downloaded from NLTK. You can download it using command below.\n\n.. code:: bash\n\n    python -c \"import nltk; nltk.download('stopwords')\"\n\nBasic Usage\n-----------\n\n.. code:: python\n\n    from rake_nltk import Rake\n\n    r = Rake() # Uses stopwords for english from NLTK, and all puntuation characters.\n\n    r.extract_keywords_from_text(<text to process>)\n\n    r.get_ranked_phrases() # To get keyword phrases ranked highest to lowest.\n\nAdvanced Usage\n--------------\n\n.. code:: python\n\n    from rake_nltk import Metric, Rake\n\n    # To use it with a specific language supported by nltk.\n    r = Rake(language=<language>)\n\n    # If you want to provide your own set of stop words and punctuations to\n    r = Rake(\n        stopwords=<list of stopwords>,\n        punctuations=<string of puntuations to ignore>\n    )\n\n    # If you want to control the metric for ranking. Paper uses d(w)/f(w) as the\n    # metric. You can use this API with the following metrics:\n    # 1. d(w)/f(w) (Default metric) Ratio of degree of word to its frequency.\n    # 2. d(w) Degree of word only.\n    # 3. f(w) Frequency of word only.\n\n    r = Rake(ranking_metric=Metric.DEGREE_TO_FREQUENCY_RATIO)\n    r = Rake(ranking_metric=Metric.WORD_DEGREE)\n    r = Rake(ranking_metric=Metric.WORD_FREQUENCY)\n\n    # If you want to control the max or min words in a phrase, for it to be\n    # considered for ranking you can initialize a Rake instance as below:\n\n    r = Rake(min_length=2, max_length=4)\n\nReferences\n----------\n\nThis is a python implementation of the algorithm as mentioned in paper\n`Automatic keyword extraction from individual documents by Stuart Rose,\nDave Engel, Nick Cramer and Wendy Cowley`_\n\nWhy I chose to implement it myself?\n-----------------------------------\n\n-  It is extremely fun to implement algorithms by reading papers. It is\n   the digital equivalent of DIY kits.\n-  There are some rather popular implementations out there, in python(\\ `aneesha/RAKE`_) and\n   node(\\ `waseem18/node-rake`_) but neither seemed to use the power of `NLTK`_. By making NLTK\n   an integral part of the implementation I get the flexibility and power to extend it in other\n   creative ways, if I see fit later, without having to implement everything myself.\n-  I plan to use it in my other pet projects to come and wanted it to be\n   modular and tunable and this way I have complete control.\n\nContributing\n------------\n\nBug Reports and Feature Requests\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nPlease use `issue tracker`_ for reporting bugs or feature requests.\n\nDevelopment\n~~~~~~~~~~~\n\nPull requests are most welcome.\n\nBuy the developer a cup of coffee!\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nIf you found the utility helpful you can buy me a cup of coffee using\n\n|Donate|\n\n.. |Donate| image:: https://www.paypalobjects.com/webstatic/en_US/i/btn/png/silver-pill-paypal-44px.png\n   :target: https://www.paypal.com/cgi-bin/webscr?cmd=_donations&business=3BSBW7D45C4YN&lc=US&currency_code=USD&bn=PP%2dDonationsBF%3abtn_donate_SM%2egif%3aNonHosted\n\n.. _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\n.. _aneesha/RAKE: https://github.com/aneesha/RAKE\n.. _waseem18/node-rake: https://github.com/waseem18/node-rake\n.. _NLTK: http://www.nltk.org/\n.. _issue tracker: https://github.com/csurfer/rake-nltk/issues\n\n.. |Build Status| image:: https://travis-ci.org/csurfer/rake-nltk.svg?branch=master\n   :target: https://travis-ci.org/csurfer/rake-nltk\n.. |Licence| image:: https://img.shields.io/badge/license-MIT-blue.svg\n   :target: https://raw.githubusercontent.com/csurfer/rake-nltk/master/LICENSE\n.. |Coverage Status| image:: https://coveralls.io/repos/github/csurfer/rake-nltk/badge.svg?branch=master\n   :target: https://coveralls.io/github/csurfer/rake-nltk?branch=master\n.. |Demo| image:: http://i.imgur.com/wVOzU7y.gif\n.. |pypiv| image:: https://img.shields.io/pypi/v/rake-nltk.svg\n   :target: https://pypi.python.org/pypi/rake-nltk\n.. |pyv| image:: https://img.shields.io/pypi/pyversions/rake-nltk.svg\n   :target: https://pypi.python.org/pypi/rake-nltk\n.. |Thanks| image:: https://img.shields.io/badge/Say%20Thanks-!-1EAEDB.svg\n   :target: https://saythanks.io/to/csurfer",
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