Python中的文本数据预处理

Question

我正在提取python中的肯定，否定和中性关键字。我的注释remarks.txt文件（编码为UTF-8）中有10,000条注释。我想导入文本文件，阅读注释的每一行并提取单词（标记）在c2列中提到的注释，并将其存储在下一个相邻的列中。 我已经编写了一个小程序在Python中调用get_keywords函数。我已经创建了get_keywords（）函数，但是遇到了将数据帧的每一行作为参数传递并使用迭代调用以提供关键字并将其存储在相邻列中的问题。

代码没有为df数据框中的所有已处理单词提供预期的列“令牌”。

    import nltk
    import pandas as pd
    import re
    import string
    from nltk import sent_tokenize, word_tokenize
    from nltk.corpus import stopwords
    from nltk.stem.porter import PorterStemmer
    remarks = pd.read_csv('/Users/ZKDN0YU/Desktop/comments/New 
    comments/ccomments.txt')
    df = pd.DataFrame(remarks, columns= ['c2'])
    df.head(50)
    df.tail(50)

    filename = 'ccomments.txt'
    file = open(filename, 'rt', encoding="utf-8")
    text = file.read()
    file.close()

    def get_keywords(row):     
    # split into tokens by white space
      tokens = text.split(str(row))
    # prepare regex for char filtering
      re_punc = re.compile('[%s]' % re.escape(string.punctuation))
    # remove punctuation from each word
      tokens = [re_punc.sub('', w) for w in tokens]
    # remove remaining tokens that are not alphabetic
      tokens = [word for word in tokens if word.isalpha()]
    # filter out stop words
      stop_words = set(stopwords.words('english'))
      tokens = [w for w in tokens if not w in stop_words]
    # stemming of words
      porter = PorterStemmer()
      stemmed = [porter.stem(word) for word in tokens]
    # filter out short tokens
      tokens = [word for word in tokens if len(word) > 1]
      return tokens
      df['tokens'] = df.c2.apply(lambda row: get_keywords(row['c2']), 
       axis=1)
      for index, row in df.iterrows():
      print(index, row['c2'],"tokens : {}".format(row['tokens']))

预期的输出：-一个Comment_modified文件，其中包含具有10,000条注释的数据帧所有行的列1）索引，2）c2（注释）和3）标记的单词。

Answer 1

假设文本文件ccomments.txt没有任何标题（即，数据从第一行本身开始）并且每行只有一个列数据（即，文本文件仅具有注释），则下面的代码将返回单词列表。

import nltk
import pandas as pd
import re
import string
from nltk import sent_tokenize, word_tokenize
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer


def get_keywords(row):     
    # split into tokens by white space
      tokens = row.split()
    # prepare regex for char filtering
      re_punc = re.compile('[%s]' % re.escape(string.punctuation))
    # remove punctuation from each word
      tokens = [re_punc.sub('', w) for w in tokens]
    # remove remaining tokens that are not alphabetic
      tokens = [word for word in tokens if word.isalpha()]
    # filter out stop words
      stop_words = set(stopwords.words('english'))
      tokens = [w for w in tokens if w not in stop_words]
    # stemming of words
      porter = PorterStemmer()
      stemmed = [porter.stem(word) for word in tokens]
    # filter out short tokens
      tokens = [word for word in tokens if len(word) > 1]
      return tokens


df = pd.read_csv('ccomments.txt',header=None,names = ['c2'])                      
df['tokens'] = df.c2.apply(lambda row: get_keywords(row))
for index, row in df.iterrows():
    print(index, row['c2'],"tokens : {}".format(row['tokens']))