tt.py

from collections import defaultdict

tweets = open('tweets.txt.aa').read().lower().split()
target = open('tweets.txt.ab').read().lower().split()

#Mapping from term to number of tweets
doc_ctr = defaultdict(int)

for tweet in tweets:
    for word in set(tweet.split()):
        if word[0] == '#':
            doc_ctr[word] += 1

#counts in target set
term_ctr = defaultdict(int)

for tweet in target:
    for word in tweet.split():
        if word[0] == '#':
            term_ctr[word] += 1

def tfidf(word):
    return term_ctr[word] * 1.0 / (1 + doc_ctr[word]) # Add one smoothing to avoid division by zero.

trending_topics = sorted(term_ctr.keys(), key=tfidf, reverse=True)[:10]

print "Top 10 trending topics"
print '\n'.join(trending_topics)