使用TfidfVectorizer的词频

Question

我试着用TF-以色列国防军计算消息数据的单词频率。到目前为止我有这个

import nltk
from sklearn.feature_extraction.text import TfidfVectorizer

new_group['tokenized_sents'] = new_group.apply(lambda row: nltk.word_tokenize(row['message']),axis=1).astype(str).lower()
vectoriser=TfidfVectorizer()
new_group['tokenized_vector'] = list(vectoriser.fit_transform(new_group['tokenized_sents']).toarray())

然而，使用上面的代码，我得到了一堆零，而不是单词频率。如何解决这个问题以获得消息的正确编号。这是我的数据

user_id     date          message      tokenized_sents      tokenized_vector
X35WQ0U8S   2019-02-17    Need help    ['need','help']      [0.0,0.0]
X36WDMT2J   2019-03-22    Thank you!   ['thank','you','!']  [0.0,0.0,0.0]

Answer 1

首先，对于计数，您不想使用TfidfVectorizer，因为它是规范化的。您想要使用CountVectorizer。其次，您不需要标记单词，因为sklearn在TfidfVectorizer和CountVectorizer的令牌程序中都有一个构建。

#add whatever settings you want
countVec =CountVectorizer()

#fit transform
cv = countVec.fit_transform(df['message'].str.lower())

#feature names
cv_feature_names = countVec.get_feature_names()

#feature counts
feature_count = cv.toarray().sum(axis = 0)

#feature name to count
dict(zip(cv_feature_names, feature_count))