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    "info": {
        "author": "Greg Clarke",
        "author_email": "greg@gho.st",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Topic :: Software Development :: Build Tools"
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        "description": "========================\nPython Biased Stop Words\n========================\n\n.. image:: https://badge.fury.io/py/biased-stop-words.svg\n    :target: https://badge.fury.io/py/biased-stop-words\n\n.. image:: http://img.shields.io/badge/license-MIT-yellow.svg?style=flat\n    :target: https://github.com/gregology/biased-stop-words/blob/master/LICENSE\n\n.. image:: https://img.shields.io/badge/contact-Gregology-blue.svg?style=flat\n    :target: http://gregology.net/contact/\n\n.. contents::\n\nOverview\n--------\n\n*Biases are bugs*\n\nStop words are words which are filtered out before processing of natural language data. Often in text analysis there are non-casual correlations, consider the following documents:\n\n- He is an astronaut, he is on Venus\n- He is an accountant, he is on Earth\n- She is an astronaut, she is on Mars\n\nProcessing these documents into two topics will result in gendered clustering. If we remove the gendered terms:\n\n- is an astronaut, is on Venus\n- is an accountant, is on Earth\n- is an astronaut, is on Mars\n\nProcessing will result in job clustering. Both clusterings are valid, however if you are interested in employing an astronaut, you don't want male accountants showing up. There are many other examples of non casual relationships occurring in natural language; religion, ethnicity, and age to name but a few.\n\nAvailable genres\n----------------\n\n* Gendered Terms\n* US Names\n* Religious Terms (Partial)\n\nMore will be available soon. Contribute at https://github.com/gregology/biased-words\n\nInteractive Notebook\n--------------------\n\nExplore this package in an  `Interactive Notebook <https://mybinder.org/v2/gh/gregology/biased-stop-words/jupyter>`__\n\n.. image:: https://user-images.githubusercontent.com/1595448/48975588-00661d00-f042-11e8-97c6-ded19ad45f51.png\n         :width: 80%\n\nHosted by `binder <https://mybinder.org>`__\n\nInstallation\n------------\n\n``biased-stop-words`` is available on PyPI\n\nhttp://pypi.python.org/pypi/biased-stop-words\n\nInstall via ``pip``\n::\n\n    $ pip install biased-stop-words\n\nOr via ``easy_install``\n::\n\n    $ easy_install biased-stop-words\n\nOr directly from ``biased-stop-words``'s `git repo <https://github.com/gregology/biased-words>`\n::\n\n    $ git clone --recursive git://github.com/gregology/biased-stop-words.git\n    $ cd biased-stop-words\n    $ python setup.py install\n\nBasic usage\n-----------\n::\n\n    >>> from biased_stop_words import genres, get_stop_words\n    >>> genres()\n    'religious, gendered, us-common-names, us-names, us-male-names, us-female-names, gendered-nouns'\n    >>> get_stop_words('gendered', 'us-common-names')\n    [u'trenton', u'augustine', u'khalil', u'aiden', u'elisabeth', u'andre', u'khanum', u'elva', u'fran...\n\n\nRunning Test\n------------\n::\n\n    $ python biased_stop_words/tests.py\n\nPython compatibility\n--------------------\n\nDeveloped for Python 2 & 3.",
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