Python 使用nltk获取TF-IDF

#!/usr/bin/python # -*- coding: utf-8 -*-'''Created on 2015-1-19@author: beyondzhou@name: explore_google_tfidf.py'''# Querying Google+ data with TF-IDFimport jsonimport nltk# Load in human language data from wherever you've saved itDATA = r'E:\eclipse\Google\dFile\107033731246200681024.json'data = json.loads(open(DATA).read())# Provide your own query terms hereQUERY_TERMS = ['best']activities = [activity['object']['content'].lower().split() \              for activity in data \                  if activity['object']['content'] != ""]#print activities,# TextCollection provides tf, idf, and tf_idf abstractions so# that we don't have to maintan/compute them ourselvestc = nltk.TextCollection(activities)relevant_activities = []for idx in range(len(activities)):    #print 'idx:', idx    score = 0    for term in [t.lower() for t in QUERY_TERMS]:        #print 'term:', term        #print 'activities[idx]:', activities[idx]        score += tc.tf_idf(term, activities[idx])    if score > 0:        relevant_activities.append({'score':score, 'title':data[idx]['title'], 'url':data[idx]['url']})#print relevant_activities,# Sort by score and display resultsrelevant_activities = sorted(relevant_activities, key=lambda p: p['score'], reverse=True)for activity in relevant_activities:    print activity['title']    print '\tLink: %s' % (activity['url'], )    print '\tScore: %s' % (activity['score'], )    print

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