使用python进行文本预处理和提取特征的实例
如下所示:
<strong><span style="font-size:14px;">文本过滤</span></strong>
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
文本去除两个以上空格
content=re.sub(r'\s{2,}', '', content)
bas4编码变成中文
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
文本去停用词
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
文本特征提取
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
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