Python CountVectorizer失败，出现了一些不好的词

我使用的是pandas数据帧，我试图获取具有字符串的特定列的单词出现次数。代码运行良好，直到某行出现以下错误

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-36-af8291199984> in <module>
      6 
      7 cv = CountVectorizer(stop_words=None)
----> 8 cv_fit=cv.fit_transform(texts)
      9 word_list = cv.get_feature_names();
     10 count_list = cv_fit.toarray().sum(axis=0)

~/anaconda3/envs/turiCreate/lib/python3.8/site-packages/sklearn/feature_extraction/text.py in fit_transform(self, raw_documents, y)
   1196         max_features = self.max_features
   1197 
-> 1198         vocabulary, X = self._count_vocab(raw_documents,
   1199                                           self.fixed_vocabulary_)
   1200 

~/anaconda3/envs/turiCreate/lib/python3.8/site-packages/sklearn/feature_extraction/text.py in _count_vocab(self, raw_documents, fixed_vocab)
   1127             vocabulary = dict(vocabulary)
   1128             if not vocabulary:
-> 1129                 raise ValueError("empty vocabulary; perhaps the documents only"
   1130                                  " contain stop words")
   1131 

ValueError: empty vocabulary; perhaps the documents only contain stop words

---

如何使CountVectorizer克服此问题？

您遇到的问题是使用标记化模式，在

标记\u模式='（？u）\\b\\w\\w+\\b'

中指定。您可以根据您的任务对其进行调整：

import pandas as pd
import numpy as np    
from sklearn.feature_extraction.text import CountVectorizer

texts=["hello :)"]    

cv = CountVectorizer(stop_words=None, token_pattern=r'(?u)\b\w\w+\b|[:)]+')  
cv_fit=cv.fit_transform(texts)    
word_list = cv.get_feature_names();    
count_list = cv_fit.toarray().sum(axis=0)

print(word_list)
print(dict(zip(word_list,count_list)))
[':)', 'hello']
{':)': 1, 'hello': 1}

如果您关心emojis，那么spacy可以为您的目标提供一个更强大的解决方案（“工业”如他们所说）：

导入空间
从spacymoji导入表情符号
从收款进口柜台
nlp=spacy.load（'en\u core\u web\u sm'）
表情符号=表情符号（nlp）
nlp.add_管道（表情符号，first=True）
tokens=[tok for tok in nlp.tokenizer（“嗨：）非常感谢。
import pandas as pd
import numpy as np    
from sklearn.feature_extraction.text import CountVectorizer

texts=["hello :)"]    

cv = CountVectorizer(stop_words=None, token_pattern=r'(?u)\b\w\w+\b|[:)]+')  
cv_fit=cv.fit_transform(texts)    
word_list = cv.get_feature_names();    
count_list = cv_fit.toarray().sum(axis=0)

print(word_list)
print(dict(zip(word_list,count_list)))
[':)', 'hello']
{':)': 1, 'hello': 1}