Python：将列表与TF-IDF一起使用

Question

I have the following piece of code that currently compares all the words in the 'Tokens' with each respective document in the 'df'. Is there any way I would be able to compare a predefined list of words with the documents instead of the 'Tokens'.

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)

Any help is appreciated. Thank you!

Answer 1

Not sure if I understand you correctly, but if you want to make the Vectorizer consider a fixed list of words, you can use the vocabulary parameter.

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)