Python 使用nltk获取TF-IDF

最新推荐文章于 2023-12-31 22:33:59 发布

翻译最新推荐文章于 2023-12-31 22:33:59 发布 · 7.5k 阅读

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通过TF-IDF技术查询Google+数据，提供个性化查询术语，并从人类语言数据集中筛选相关活动，按得分排序展示结果。

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#!/usr/bin/python 
# -*- coding: utf-8 -*-

'''
Created on 2015-1-19
@author: beyondzhou
@name: explore_google_tfidf.py
'''

# Querying Google+ data with TF-IDF
import json
import nltk

# Load in human language data from wherever you've saved it
DATA = r'E:\eclipse\Google\dFile\107033731246200681024.json'
data = json.loads(open(DATA).read())

# Provide your own query terms here
QUERY_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 ourselves
tc = 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 results
relevant_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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