Python:在TFIDF中使用列表

Question

我有下面的一段代码，它将“Tokens”中的所有单词与“df”中的每个文档进行比较。有什么方法可以让我将预定义的单词列表与文档（而不是“标记”）进行比较。在

from sklearn.feature_extraction.text import TfidfVectorizer
tfidf_vectorizer = TfidfVectorizer(norm=None)  

list_contents =[]
for index, row in df.iterrows():
    list_contents.append(' '.join(row.Tokens))

# list_contents = df.Content.values

tfidf_matrix = tfidf_vectorizer.fit_transform(list_contents)
df_tfidf = pd.DataFrame(tfidf_matrix.toarray(),columns= [tfidf_vectorizer.get_feature_names()])
df_tfidf.head(10)

感谢任何帮助。谢谢您！在

Answer 1

不确定我是否理解您的意思，但是如果您想让向量器考虑一个固定的单词列表，您可以使用vocabulary参数。在

my_words = ["foo","bar","baz"]

# set the vocabulary parameter with your list of words
tfidf_vectorizer = TfidfVectorizer(
    norm=None,
    vocabulary=my_words)  

list_contents =[]
for index, row in df.iterrows():
    list_contents.append(' '.join(row.Tokens))

# this matrix will have only 3 columns because we have forced
# the vectorizer to use just the words foo bar and baz
# so it'll ignore all other words in the documents.
tfidf_matrix = tfidf_vectorizer.fit_transform(list_contents)