使用 TfidfVectorizer 的词频

word frequency with TfidfVectorizer

我正在尝试使用 TF-IDF 计算消息数据帧的词频。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())

但是使用上面的代码我得到了一堆零而不是单词频率。我怎样才能解决这个问题以获得正确的消息号码频率。这是我的数据框

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]

首先,对于计数,您不想使用 TfidfVectorizer,因为它已标准化。你想使用 CountVectorizer。其次,您不需要对单词进行分词,因为 sklearn 内置分词器,同时包含 TfidfVectorizer 和 CountVectorizer。

#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))