old_contents: import codecs
  import os
  from setuptools import setup, find_packages
  
  HERE = os.path.abspath(os.path.dirname(__file__))
  def read(*parts):
      """
      Build an absolute path from *parts* and and return the contents of the
      resulting file.  Assume UTF-8 encoding.
      """
      with codecs.open(os.path.join(HERE, *parts), "rb", "utf-8") as f:
          return f.read()
  
  setup(
    name = 'vaderSentiment',
    #packages = ['vaderSentiment'], # this must be the same as the name above
    packages = find_packages(exclude=['tests*']), # a better way to do it than the line above -- this way no typo/transpo errors
    include_package_data=True,
    version = '3.2.1',
    description = 'VADER Sentiment Analysis. VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, and works well on texts from other domains.',
    long_description = read("README.rst"),
    long_description_content_type = 'text/markdown',
    author = 'C.J. Hutto',
    author_email = 'cjhutto@gatech.edu',
    license = 'MIT License: http://opensource.org/licenses/MIT',
    url = 'https://github.com/cjhutto/vaderSentiment', # use the URL to the github repo
    download_url = 'https://github.com/cjhutto/vaderSentiment/archive/master.zip', 
    keywords = ['vader', 'sentiment', 'analysis', 'opinion', 'mining', 'nlp', 'text', 'data', 
                'text analysis', 'opinion analysis', 'sentiment analysis', 'text mining', 'twitter sentiment',
                'opinion mining', 'social media', 'twitter', 'social', 'media'], # arbitrary keywords
    classifiers = ['Development Status :: 4 - Beta', 'Intended Audience :: Science/Research',
                   'License :: OSI Approved :: MIT License', 'Natural Language :: English',
                   'Programming Language :: Python :: 3.5', 'Topic :: Scientific/Engineering :: Artificial Intelligence',
                   'Topic :: Scientific/Engineering :: Information Analysis', 'Topic :: Text Processing :: Linguistic',
                   'Topic :: Text Processing :: General'],
  )
new_contents: import codecs
  import os
  from setuptools import setup, find_packages
  
  HERE = os.path.abspath(os.path.dirname(__file__))
  def read(*parts):
      """
      Build an absolute path from *parts* and and return the contents of the
      resulting file.  Assume UTF-8 encoding.
      """
      with codecs.open(os.path.join(HERE, *parts), "rb", "utf-8") as f:
          return f.read()
  
  setup(
    name = 'vaderSentiment',
    #packages = ['vaderSentiment'], # this must be the same as the name above
    packages = find_packages(exclude=['tests*']), # a better way to do it than the line above -- this way no typo/transpo errors
    include_package_data=True,
    version = '3.2.2',
    description = 'VADER Sentiment Analysis. VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, and works well on texts from other domains.',
    long_description = read("README.rst"),
    long_description_content_type = 'text/markdown',
    author = 'C.J. Hutto',
    author_email = 'cjhutto@gatech.edu',
    license = 'MIT License: http://opensource.org/licenses/MIT',
    url = 'https://github.com/cjhutto/vaderSentiment', # use the URL to the github repo
    download_url = 'https://github.com/cjhutto/vaderSentiment/archive/master.zip',
    install_requires = ['requests'],
    keywords = ['vader', 'sentiment', 'analysis', 'opinion', 'mining', 'nlp', 'text', 'data',
                'text analysis', 'opinion analysis', 'sentiment analysis', 'text mining', 'twitter sentiment',
                'opinion mining', 'social media', 'twitter', 'social', 'media'], # arbitrary keywords
    classifiers = ['Development Status :: 4 - Beta', 'Intended Audience :: Science/Research',
                   'License :: OSI Approved :: MIT License', 'Natural Language :: English',
                   'Programming Language :: Python :: 3.5', 'Topic :: Scientific/Engineering :: Artificial Intelligence',
                   'Topic :: Scientific/Engineering :: Information Analysis', 'Topic :: Text Processing :: Linguistic',
                   'Topic :: Text Processing :: General'],
  )
subject: Add missing requests dependency