new_contents: import feedparser as fp
  from pprint import pprint as pp
  from textblob import TextBlob as tb
  import re
  
  # feed from CNN
  worldNews = fp.parse('http://rss.cnn.com/rss/edition_world.rss')
  
  # number of stories
  numStories = len(worldNews['entries'])
  
  # list to contain polarity of stories
  final = []
  
  for i in range(0,numStories):
      # initial description
      descInit = worldNews['entries'][i]['summary_detail']['value']
      # cleaning out the img tag
      descClean = re.sub('\<img.*$', '', descInit)
      # final description
      desc = tb(descClean)
      # title of the entry
      title = tb(worldNews['entries'][i]['title'])
      # final string which contains description and headline to get a better polarity result
      completeString = title + desc
      # appending story headline and descrition polarity to final list
      final.append(completeString.sentiment.polarity)
  
  # polarity calculations
  finalPolarity = sum(final)/len(final)
  worstPolarity = final.index(min(final))
  bestPolarity = final.index(max(final))
  
  # display output of worst story
  print(worldNews['entries'][worstPolarity]['title'], "-- Worst Story :(")
  worstDesc = worldNews['entries'][worstPolarity]['summary_detail']['value']
  worstDesc = descClean = re.sub('\<img.*$', '', worstDesc)
  print(worstDesc)
  
  print('\n')
  
  # display output of best story
  print(worldNews['entries'][bestPolarity]['title'], "-- Best Story :)")
  bestDesc = worldNews['entries'][bestPolarity]['summary_detail']['value']
  bestDesc = descClean = re.sub('\<img.*$', '', bestDesc)
  print(bestDesc)
  
  # overall polarity off news headlines. 50% is used as a starting baseline.
  print('\nOverall Positivity: ', 50 - round(finalPolarity*100),"%")
  
subject: Create news_sentiment_analysis.py