Python filtering sensitive word records

python filtering sensitive word records

sketch :

On sensitive word filtering can be seen as a text anti spam algorithm , for example
subject : Sensitive word text file filtered_words.txt, When the user enters sensitive words , Then use asterisk * Replace , For example, when the user enters 「 Beijing is a good city 」, Has become a 「** It's a good city 」
Code :

def filterwords(x):
with open(x,'r') as f:
print text.split('\n')
for i in text.split('\n'):
if i in userinput:
return word
print filterwords('filtered_words.txt')

Another example is the anti yellow series :

 Develop sensitive word filters , Prompt users to enter comments , If the user's input contains special characters :
List of sensitive words li = [" Aoi Sora "," Tokyo fever ",” Wutenglan ”,” Yui Hatano ”]
Replace the sensitive words in the user's input with ***, And add it to a list ; If the user's input doesn't have sensitive words , Add it directly to the list above .
content = input(' Please enter your content :')
li = [" Aoi Sora "," Tokyo fever "," Wutenglan "," Yui Hatano "]
i = 0
while i < 4:
for li[i] in content:
li1 = content.replace(' Aoi Sora ','***')
li2 = li1.replace(' Tokyo fever ','***')
li3 = li2.replace(' Wutenglan ','***')
li4 = li3.replace(' Yui Hatano ','***')
i += 1

 Insert picture description here
Practical cases :
Together bat Interview questions : Quick replacement 10 One hundred million titles 5 Ten thousand sensitive words , What are the solutions ?
There are a billion titles , In a file , One title per line . Yes 5 Ten thousand sensitive words , There is another file . Write a program to filter out all sensitive words in all headings , Save to another file .

1、DFA Filter sensitive words algorithm

In the algorithm of text filtering ,DFA Is a better algorithm .DFA namely Deterministic Finite Automaton, That is to say, definite finite automata .
The core of the algorithm is to establish many sensitive word trees based on sensitive words .
python Realization DFA Algorithm :

# -*- coding:utf-8 -*-
import time
# DFA Algorithm
class DFAFilter():
def __init__(self):
self.keyword_chains = {}
self.delimit = '\x00'
def add(self, keyword):
keyword = keyword.lower()
chars = keyword.strip()
if not chars:
level = self.keyword_chains
for i in range(len(chars)):
if chars[i] in level:
level = level[chars[i]]
if not isinstance(level, dict):
for j in range(i, len(chars)):
level[chars[j]] = {}
last_level, last_char = level, chars[j]
level = level[chars[j]]
last_level[last_char] = {self.delimit: 0}
if i == len(chars) - 1:
level[self.delimit] = 0
def parse(self, path):
with open(path,encoding='utf-8') as f:
for keyword in f:
def filter(self, message, repl="*"):
message = message.lower()
ret = []
start = 0
while start < len(message):
level = self.keyword_chains
step_ins = 0
for char in message[start:]:
if char in level:
step_ins += 1
if self.delimit not in level[char]:
level = level[char]
ret.append(repl * step_ins)
start += step_ins - 1
start += 1
return ''.join(ret)
if __name__ == "__main__":
gfw = DFAFilter()
path="F:/ Text anti spam algorithm /sensitive_words.txt"
text=" Xinjiang riot apple new product launch "
result = gfw.filter(text)
time2 = time.time()
print(' The total time is :' + str(time2 - time1) + 's')

Running effect :

 Xinjiang riot apple new product launch
**** Apple launch **
The total time is :0.0010344982147216797s

2、AC Automata filter sensitive words algorithm

AC automata : A common example is to give n Word , Give me another paragraph that contains m One character article , Let you find out how many words appear in the article .
Simply speak ,AC Automata is a dictionary tree +kmp Algorithm + Mismatch pointer

# -*- coding:utf-8 -*-
import time
# AC Automata algorithm
class node(object):
def __init__(self): = {} = None
self.isWord = False
self.word = ""
class ac_automation(object):
def __init__(self):
self.root = node()
# Add sensitive word function
def addword(self, word):
temp_root = self.root
for char in word:
if char not in[char] = node()
temp_root =[char]
temp_root.isWord = True
temp_root.word = word
# Failed pointer function
def make_fail(self):
temp_que = []
while len(temp_que) != 0:
temp = temp_que.pop(0)
p = None
for key,value in
if temp == self.root:[key].fail = self.root
p =
while p is not None:
if key in[key].fail =
p =
if p is None:[key].fail = self.root
# Find sensitive word functions
def search(self, content):
p = self.root
result = []
currentposition = 0
while currentposition < len(content):
word = content[currentposition]
while word in == False and p != self.root:
p =
if word in
p =[word]
p = self.root
if p.isWord:
p = self.root
currentposition += 1
return result
# Load sensitive lexicon functions
def parse(self, path):
with open(path,encoding='utf-8') as f:
for keyword in f:
# The substitution function of sensitive words
def words_replace(self, text):
:param ah: AC automata
:param text: Text
:return: Filter the text after sensitive words
result = list(set(
for x in result:
m = text.replace(x, '*' * len(x))
text = m
return text
if __name__ == '__main__':
ah = ac_automation()
path='F:/ Text anti spam algorithm /sensitive_words.txt'
text1=" Xinjiang riot apple new product launch "
time2 = time.time()
print(' The total time is :' + str(time2 - time1) + 's')

Running results :

 Xinjiang riot apple new product launch
**** Apple launch **
The total time is :0.0010304450988769531s
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本文为[Elementary school students in IT field]所创,转载请带上原文链接,感谢

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