使用python进行文本预处理和提取特征的实例

 文本过滤

 result = re.sub(r'[^\u4e00-\u9fa5,。？！，、；：“ ”‘ '（ ）《 》〈 〉]', "", content)#只保留中文和标点 

 result = re.sub(r'[^\u4e00-\u9fa5]', "",content)#只保留中文 result = re.sub(r'[^\0-9\.\u4e00-\u9fa5,。？！，、；：“ ”‘ '（ ）《 》〈 〉]', "", content)#只保留中文和标点和数字 result = re.sub(r'[^\u4e00-\u9fa5,A-Za-z0-9]', "",content)#只保留中文、英文和数字 

 content=re.sub(r'\s{2,}', '', content)

 def bas4_decode(bas4_content): decodestr= base64.b64decode(bas4_content) result = re.sub(r'[^\0-9\.\u4e00-\u9fa5,。？！，、；：“ ”‘ '（ ）《 》〈 〉]', "", decodestr.decode())#只保留中文和标点和数字 return result 

 def text_to_wordlist(text): result = re.sub(r'[^\u4e00-\u9fa5]', "",text) f1_seg_list = jieba.cut(result)#需要添加一个词典，来弥补结巴分词中没有的词语，从而保证更高的正确率 f_stop = codecs.open(".\stopword.txt","r","utf-8") try: f_stop_text = f_stop.read() finally: f_stop.close() f_stop_seg_list = f_stop_text.split() test_words = [] for myword in f1_seg_list: if myword not in f_stop_seg_list: test_words.append(myword) return test_words 

 import jieba import jieba.analyse import numpy as np #import json import re def Textrank(content): result = re.sub(r'[^\u4e00-\u9fa5]', "",content) seg = jieba.cut(result) jieba.analyse.set_stop_words('stopword.txt') keyList=jieba.analyse.textrank('|'.join(seg), topK=10, withWeight=False) return keyList def TF_IDF(content): result = re.sub(r'[^\u4e00-\u9fa5]', "",content) seg = jieba.cut(result) jieba.analyse.set_stop_words('stopword.txt') keyWord = jieba.analyse.extract_tags( '|'.join(seg), topK=10, withWeight=False, allowPOS=())#关键词提取，在这里对jieba的tfidf.py进行了修改 return keyWord