我有一个来自twitter的评论数据集（例如10个实例）。我想使用Scikit-learn Python作为输出来对相似的单词进行分类和计数，如下所示：

**Dataset:** 
  comment_text 
 r u cmng or u not cmng   
I am fine,r u fine  
my frnd is gr8,wll dn.  
 we r nt going tday   
I have a fever.  

应显示为以下输出

 Words    Count

u         3
r         3
i         2
cmng      2
fine,1
wll       1
have      1
fever.    1
not       1
tday      1
my        1
we        1
a         1
or        1
nt        1
going     1
fine      1
dn.       1
gr8,1
frnd      1
am        1
is        1
dtype: int64

我使用此代码，但显示错误的输出

    text = train_dataset_male['comment_text']
    print(text)
    vectorizer = TfidfVectorizer()
    # tokenize and build vocab
    vectorizer.fit(text)
    # summarize
    print(vectorizer.vocabulary_)
    print(vectorizer.idf_)
    # encode document
    vector = vectorizer.transform([text[0]])
    # summarize encoded vector
    print(vector.shape)
    print(vector.toarray())