Python数据分析8-----网页文本处理

1、去除网页的标签，如<br/>

from bs4 import BeautifulrSoup 
preData=BeautifulSoup(data,'html.parser').get_text()

 

2、将标点符号等去掉，用正则表达式。

import re
#表示将data中的除了大小写字母之外的符号换成空格
preData=re.sub(r'[^a-zA-Z]',' ',data)

　　去除特殊符号：

#two commom ways to clean data
def cleaner(word):
    word = re.sub(r'\#\.', '', word)
    word = re.sub(r'\n', '', word)
    word = re.sub(r',', '', word)
    word = re.sub(r'\-', ' ', word)
    word = re.sub(r'\.', '', word)
    word = re.sub(r'\\', ' ', word)
    word = re.sub(r'\\x\.+', '', word)
    word = re.sub(r'\d', '', word)
    word = re.sub(r'^_.', '', word)
    word = re.sub(r'_', ' ', word)
    word = re.sub(r'^ ', '', word)
    word = re.sub(r' $', '', word)
    word = re.sub(r'\?', '', word)
    word = re.sub(r'é', '', word)
    word = re.sub(r'§', '', word)
    word = re.sub(r'¦', '', word)
    word = re.sub(r'æ', '', word)
    word = re.sub(r'\d+', '', word)
    word = re.sub('(.*?)\d+(.*?)', '', word)
    return word.lower()
def hashing(word):
    word = re.sub(r'ain$', r'ein', word)
    word = re.sub(r'ai', r'ae', word)
    word = re.sub(r'ay$', r'e', word)
    word = re.sub(r'ey$', r'e', word)
    word = re.sub(r'ie$', r'y', word)
    word = re.sub(r'^es', r'is', word)
    word = re.sub(r'a+', r'a', word)
    word = re.sub(r'j+', r'j', word)
    word = re.sub(r'd+', r'd', word)
    word = re.sub(r'u', r'o', word)
    word = re.sub(r'o+', r'o', word)
    word = re.sub(r'ee+', r'i', word)
    if not re.match(r'ar', word):
        word = re.sub(r'ar', r'r', word)
    word = re.sub(r'iy+', r'i', word)
    word = re.sub(r'ih+', r'eh', word)
    word = re.sub(r's+', r's', word)
    if re.search(r'[rst]y', 'word') and word[-1] != 'y':
        word = re.sub(r'y', r'i', word)
    if re.search(r'[bcdefghijklmnopqrtuvwxyz]i', word):
        word = re.sub(r'i$', r'y', word)
    if re.search(r'[acefghijlmnoqrstuvwxyz]h', word):
        word = re.sub(r'h', '', word)
    word = re.sub(r'k', r'q', word)
    return word

def array_cleaner(array):
    X = []
    for sentence in array:
        clean_sentence = ''
        words = sentence.split(' ')
        for word in words:
            clean_sentence = clean_sentence +' '+ cleaner(word)
        X.append(clean_sentence)
    return X
X_train = array_cleaner(X_train)

 

 

3、将文本中的单词小写化，并将data用空格分开

words=data.lower().split()

4、去掉停用词

#可以自己下载停用词
#nltk.download() 
words_notstop=[w for w in words if w not in stopwords]

5、将所有的词连接成一个句子

sentence=' '.join(words)

 6、把空格前缀去除

train_data['review'] = train_data['review'].str.strip() 

7、删除短词，删除句子中词语长度小于3的词，如haa，hi等无意义的词

##删除短单词
train_data['review'] = train_data['review'].apply(lambda x:' '.join([w for w in x.split() if len(w) > 3]))

8、分词

##分词
train_data['review'] = train_data['review'].str.split()

9、提取词干

##提取词干，即基于规则从单词中去除后缀的过程。例如，play，player，played，plays，playing都是play的变种。
from nltk.stem.porter import *
stemmer =PorterStemmer()
train_data['review'] = train_data['review'].apply(lambda x: [stemmer.stem(i) for i in x])