Metadata-Version: 1.1
Name: biased-stop-words
Version: 2017.5.10.3
Summary: Generates biased stop word lists
Home-page: https://github.com/gregology/python-biased-stop-words
Author: Greg Clarke
Author-email: greg@gho.st
License: MIT License

Copyright (c) 2017 Gregory Clarke

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
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The above copyright notice and this permission notice shall be included in all
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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Description: ========================
        Python Biased Stop Words
        ========================
        
        .. image:: https://badge.fury.io/py/biased-stop-words.svg
            :target: https://badge.fury.io/py/biased-stop-words
        
        .. image:: http://img.shields.io/badge/license-MIT-yellow.svg?style=flat
            :target: https://github.com/gregology/python-biased-stop-words/blob/master/LICENSE
        
        .. image:: https://img.shields.io/badge/contact-Gregology-blue.svg?style=flat
            :target: http://gregology.net/contact/
        
        .. contents::
        
        Overview
        --------
        
        Stop 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:
        
        - He is an astronaut, he is on Venus
        - He is an accountant, he is on Earth
        - She is an astronaut, she is on Mars
        
        Processing these documents into two topics will result in gendered clustering. If we remove the gendered terms:
        
        - is an astronaut, is on Venus
        - is an accountant, is on Earth
        - is an astronaut, is on Mars
        
        Processing 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.
        
        Available genres
        ----------------
        
        * English Gendered Terms
        * US names
        
        More will be available soon. Contribute at https://github.com/gregology/biased-stop-words
        
        Installation
        ------------
        
        ``biased-stop-words`` is available on PyPI
        
        http://pypi.python.org/pypi/biased-stop-words
        
        Install via ``pip``
        ::
        
            $ pip install biased-stop-words
        
        Or via ``easy_install``
        ::
        
            $ easy_install biased-stop-words
        
        Or by cloning ``biased-stop-words``'s `git repo <https://github.com/gregology/python-biased-stop-words>`_ ::
        
            $ git clone --recursive git://github.com/gregology/python-biased-stop-words.git
        
        Then install it by running:
        ::
        
            $ python setup.py install
        
        Basic usage
        -----------
        ::
        
            from biased_stop_words import get_stop_words
            stop_words = get_stop_words('gendered', 'common-us-names')
        
        Running Test
        ------------
        ::
        
            $ python biased_stop_words/tests.py
        
        Python compatibility
        --------------------
        
        Developed for Python 2 & 3.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Classifier: Topic :: Software Development
