python - 如何使用 nltk 或 python 删除停用词

Question

所以我有一个数据集，我想删除停用词

stopwords.words('english')

我正在努力如何在我的代码中使用它来简单地取出这些单词。我已经有了这个数据集中的单词列表，我正在努力的部分是与这个列表进行比较并删除停用词。任何帮助表示赞赏。

score 231 · Accepted Answer

from nltk.corpus import stopwords
# ...
filtered_words = [word for word in word_list if word not in stopwords.words('english')]

score 20 · Accepted Answer

你也可以做一个设置差异，例如：

list(set(nltk.regexp_tokenize(sentence, pattern, gaps=True)) - set(nltk.corpus.stopwords.words('english')))

score 18 · Accepted Answer

要排除所有类型的停用词，包括 nltk 停用词，您可以执行以下操作：

from stop_words import get_stop_words
from nltk.corpus import stopwords

stop_words = list(get_stop_words('en'))         #About 900 stopwords
nltk_words = list(stopwords.words('english')) #About 150 stopwords
stop_words.extend(nltk_words)

output = [w for w in word_list if not w in stop_words]

score 14 · Accepted Answer

我想您有一个要从中删除停用词的单词列表（word_list）。你可以这样做：

filtered_word_list = word_list[:] #make a copy of the word_list
for word in word_list: # iterate over word_list
  if word in stopwords.words('english'): 
    filtered_word_list.remove(word) # remove word from filtered_word_list if it is a stopword

score 9 · Accepted Answer

为此，有一个非常简单的轻量级 python 包stop-words。

首先使用以下方法安装软件包： pip install stop-words

然后，您可以使用列表理解在一行中删除您的单词：

from stop_words import get_stop_words

filtered_words = [word for word in dataset if word not in get_stop_words('english')]

这个包下载起来非常轻量级（与 nltk 不同），适用于Python 2和Python 3，并且它具有许多其他语言的停用词，例如：

    Arabic
    Bulgarian
    Catalan
    Czech
    Danish
    Dutch
    English
    Finnish
    French
    German
    Hungarian
    Indonesian
    Italian
    Norwegian
    Polish
    Portuguese
    Romanian
    Russian
    Spanish
    Swedish
    Turkish
    Ukrainian

score 5 · Accepted Answer

使用textcleaner库从数据中删除停用词。

按照这个链接：https ://yugantm.github.io/textcleaner/documentation.html#remove_stpwrds

请按照以下步骤使用此库。

pip install textcleaner

安装后：

import textcleaner as tc
data = tc.document(<file_name>) 
#you can also pass list of sentences to the document class constructor.
data.remove_stpwrds() #inplace is set to False by default

使用上面的代码删除停用词。

score 5 · Accepted Answer

这是我对此的看法，以防您想立即将答案转换为字符串（而不是过滤后的单词列表）：

STOPWORDS = set(stopwords.words('english'))
text =  ' '.join([word for word in text.split() if word not in STOPWORDS]) # delete stopwords from text

score 2 · Accepted Answer

您可以使用此功能，您应该注意到您需要降低所有单词

from nltk.corpus import stopwords

def remove_stopwords(word_list):
        processed_word_list = []
        for word in word_list:
            word = word.lower() # in case they arenet all lower cased
            if word not in stopwords.words("english"):
                processed_word_list.append(word)
        return processed_word_list

score 2 · Accepted Answer

虽然这个问题有点老了，但这里有一个新的库，值得一提，它可以做额外的任务。

在某些情况下，您不想只删除停用词。相反，您可能希望在文本数据中找到停用词并将其存储在列表中，以便您可以找到数据中的噪音并使其更具交互性。

该库称为'textfeatures'. 您可以按如下方式使用它：

! pip install textfeatures
import textfeatures as tf
import pandas as pd

例如，假设您有以下一组字符串：

texts = [
    "blue car and blue window",
    "black crow in the window",
    "i see my reflection in the window"]

df = pd.DataFrame(texts) # Convert to a dataframe
df.columns = ['text'] # give a name to the column
df

现在，调用 stopwords() 函数并传递你想要的参数：

tf.stopwords(df,"text","stopwords") # extract stop words
df[["text","stopwords"]].head() # give names to columns

结果将是：

    text                                 stopwords
0   blue car and blue window             [and]
1   black crow in the window             [in, the]
2   i see my reflection in the window    [i, my, in, the]

如您所见，最后一列包含该文档（记录）中的停用词。

score 2 · Accepted Answer

使用过滤器：

from nltk.corpus import stopwords
# ...  
filtered_words = list(filter(lambda word: word not in stopwords.words('english'), word_list))

score 1 · Accepted Answer

from nltk.corpus import stopwords 

from nltk.tokenize import word_tokenize 

example_sent = "This is a sample sentence, showing off the stop words filtration."

  
stop_words = set(stopwords.words('english')) 
  
word_tokens = word_tokenize(example_sent) 
  
filtered_sentence = [w for w in word_tokens if not w in stop_words] 
  
filtered_sentence = [] 
  
for w in word_tokens: 
    if w not in stop_words: 
        filtered_sentence.append(w) 
  
print(word_tokens) 
print(filtered_sentence)

score 0 · Accepted Answer

如果您的数据存储为Pandas DataFrame.，您可以使用默认情况下remove_stopwords使用 NLTK 停用词列表的 textero 。

import pandas as pd
import texthero as hero
df['text_without_stopwords'] = hero.remove_stopwords(df['text'])

score 0 · Accepted Answer

我将向您展示一些示例首先我从数据框（twitter_df）中提取文本数据以进一步处理如下

     from nltk.tokenize import word_tokenize
     tweetText = twitter_df['text']

然后标记化我使用以下方法

     from nltk.tokenize import word_tokenize
     tweetText = tweetText.apply(word_tokenize)

然后，要删除停用词，

     from nltk.corpus import stopwords
     nltk.download('stopwords')

     stop_words = set(stopwords.words('english'))
     tweetText = tweetText.apply(lambda x:[word for word in x if word not in stop_words])
     tweetText.head()

我认为这会对你有所帮助

score -3 · Accepted Answer

   import sys
print ("enter the string from which you want to remove list of stop words")
userstring = input().split(" ")
list =["a","an","the","in"]
another_list = []
for x in userstring:
    if x not in list:           # comparing from the list and removing it
        another_list.append(x)  # it is also possible to use .remove
for x in another_list:
     print(x,end=' ')

   # 2) if you want to use .remove more preferred code
    import sys
    print ("enter the string from which you want to remove list of stop words")
    userstring = input().split(" ")
    list =["a","an","the","in"]
    another_list = []
    for x in userstring:
        if x in list:           
            userstring.remove(x)  
    for x in userstring:           
        print(x,end = ' ') 
    #the code will be like this

python - 如何使用 nltk 或 python 删除停用词

14 回答 14

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