python 实现敏感词过滤的方法

如下所示：

#!/usr/bin/python2.6  # -*- coding: utf-8 -*- import time class Node(object):   def __init__(self):     self.children = None  # The encode of word is UTF-8 def add_word(root,word):   node = root   for i in range(len(word)):     if node.children == None:       node.children = {}       node.children[word[i]] = Node()      elif word[i] not in node.children:       node.children[word[i]] = Node()      node = node.children[word[i]]  def init(path):   root = Node()   fp = open(path,'r')   for line in fp:     line = line[0:-1]     #print len(line)     #print line     #print type(line)     add_word(root,line)   fp.close()   return root  # The encode of word is UTF-8 # The encode of message is UTF-8 def is_contain(message, root):   for i in range(len(message)):     p = root     j = i     while (j<len(message) and p.children!=None and message[j] in p.children):       p = p.children[message[j]]       j = j + 1      if p.children==None:       #print '---word---',message[i:j]       return True      return False    def dfa():   print '----------------dfa-----------'   root = init('/tmp/word.txt')    message = '四处乱咬乱吠，吓得家中11岁的女儿躲在屋里不敢出来，直到辖区派出所民警赶到后，才将孩子从屋中救出。最后在征得主人同意后，民警和村民合力将这只发疯的狗打死'   #message = '不顾'   print '***message***',len(message)   start_time = time.time()   for i in range(1000):     res = is_contain(message,root)     #print res   end_time = time.time()   print (end_time - start_time)   def is_contain2(message,word_list):   for item in word_list:     if message.find(item)!=-1:       return True   return False  def normal():   print '------------normal--------------'   path = '/tmp/word.txt'   fp = open(path,'r')   word_list = []   message = '四处乱咬乱吠，吓得家中11岁的女儿躲在屋里不敢出来，直到辖区派出所民警赶到后，才将孩子从屋中救出。最后在征得主人同意后，民警和村民合力将这只发疯的狗打死'   print '***message***',len(message)   for line in fp:     line = line[0:-1]     word_list.append(line)   fp.close()   print 'The count of word:',len(word_list)   start_time = time.time()   for i in range(1000):     res = is_contain2(message,word_list)     #print res   end_time = time.time()   print (end_time - start_time)    if __name__ == '__main__':   dfa()   normal() 

测试结果:

1) 敏感词 100个

----------------dfa-----------***message*** 2240.325479984283------------normal--------------***message*** 224The count of word: 1000.107350111008

2) 敏感词 1000 个

----------------dfa-----------***message*** 2240.324251890182------------normal--------------***message*** 224The count of word: 10001.05939006805

从上面的实验我们可以看出，在DFA 算法只有在敏感词较多的情况下，才有意义。在百来个敏感词的情况下，甚至不如普通算法