在python中计算没有停用词的tfidf矩阵

Question

我正在尝试计算一个tfidf没有停用词的矩阵。这是我的代码：def removeStopWords(documents):    stop_words = set(stopwords.words('italian'))    english_stop_words = set(stopwords.words('english'))    stop_words.update(list(set(english_stop_words)))    for d in documents:        document = d['document']        word_tokens = word_tokenize(document)         filtered_sentence = ''        for w in word_tokens:            if not inStopwords(w, stop_words):                 filtered_sentence = w + ' ' + filtered_sentence        d['document'] = filtered_sentence[:-1]    return calculateTFIDF(documents)def calculateTFIDF(corpus):    tfidf = TfidfVectorizer()    x = tfidf.fit_transform(corpus)    df_tfidf = pd.DataFrame(x.toarray(), columns=tfidf.get_feature_names())    return {c: s[s > 0] for c, s in zip(df_tfidf, df_tfidf.T.values)}但是当我返回矩阵（使用形式{word:value}）时，它还包含一些停用词，例如whenor il。我该如何解决？谢谢

Answer 1

有更好的方法来删除 TF-IDF 计算中的停用词。有TfidfVectorizer一个参数stop_words，您可以在其中传递要排除的单词集合。

from nltk.corpus import stopwords

from sklearn.feature_extraction.text import TfidfVectorizer

import pandas as pd

documents = ['I went to the barbershop when my hair was long.', 'The barbershop was closed.']

# create set of stopwords to remove

stop_words = set(stopwords.words('italian'))

english_stop_words = set(stopwords.words('english'))

stop_words.update(english_stop_words)

# check if word in stop words

print('when' in stop_words)  # True

print('il' in stop_words)  # True

# else add word to the set

print('went' in stop_words)  # False

stop_words.add('went')

# create tf-idf from original documents

tfidf = TfidfVectorizer(stop_words=stop_words)

x = tfidf.fit_transform(documents)

df_tfidf = pd.DataFrame(x.toarray(), columns=tfidf.get_feature_names())

print({c: s[s > 0] for c, s in zip(df_tfidf, df_tfidf.T.values)})

# {'barbershop': array([0.44943642, 0.57973867]), 'closed': array([0.81480247]), 'hair': array([0.6316672]), 'long': array([0.6316672])}