twiiter_mining.py

import twitter

#INPUT YOUR AUTH KEY HERE
CONSUMER_KEY = ''
CONSUMER_SECRET = ''
OAUTH_TOKEN = ''
OAUTH_TOKEN_SECRET = ''

auth = twitter.oauth.OAuth(OAUTH_TOKEN, OAUTH_TOKEN_SECRET,
                           CONSUMER_KEY, CONSUMER_SECRET)

twitter_api = twitter.Twitter(auth=auth)

#RETIEVING WORLD AND INDIA TRENDS
world_trends = twitter_api.trends.place(_id=WORLD_WOE_ID)
ind_trends = twitter_api.trends.place(_id=IND_WOE_ID)

#Finding the most popular retweets
retweets = [
            # Store out a tuple of these three values ...
            (status['retweet_count'], 
             status['retweeted_status']['user']['screen_name'],
             status['text']) 
            
            # ... for each status ...
            for status in statuses 
            
            # ... so long as the status meets this condition.
                if status.has_key('retweeted_status')
           ]

# Slice off the first 5 from the sorted results and display each item in the tuple

pt = PrettyTable(field_names=['Count', 'Screen Name', 'Text'])
[ pt.add_row(row) for row in sorted(retweets, reverse=True)[:5] ]
pt.max_width['Text'] = 50
pt.align= 'l'
print pt




#MINING text, screen names, and hashtags from tweets
status_texts = [ status['text'] 
                 for status in statuses ]

screen_names = [ user_mention['screen_name'] 
                 for status in statuses
                     for user_mention in status['entities']['user_mentions'] ]

hashtags = [ hashtag['text'] 
             for status in statuses
                 for hashtag in status['entities']['hashtags'] ]

# Compute a collection of all words from all tweets
words = [ w 
          for t in status_texts 
              for w in t.split() ]

# Explore the first 5 items for each...

print json.dumps(status_texts[0:5], indent=1)
print json.dumps(screen_names[0:5], indent=1) 
print json.dumps(hashtags[0:5], indent=1)
print json.dumps(words[0:5], indent=1)