如何在单个文档的tfidf矩阵中查找哪个word的tfidf最大？

from sklearn.feature_extraction.text import TfidfVectorizer

# 假设有一个文档集documents，包含多个文档
documents = ["This is the first document.",
             "This document is the second document.",
             "And this is the third one.",
             "Is this the first document?"]

# 创建TfidfVectorizer对象，并进行文本向量化
vectorizer = TfidfVectorizer()
tfidf_matrix = vectorizer.fit_transform(documents)

# 获取所有词的列表
words = vectorizer.get_feature_names()

# 遍历tfidf矩阵的每一列，找到每个词的tfidf最大值所在的位置
max_tfidf_indices = tfidf_matrix.argmax(axis=0)

# 根据位置找到对应的词
max_tfidf_words = [words[idx] for idx in max_tfidf_indices]

# 输出tfidf最大的词
for word in max_tfidf_words:
    print("The word with the maximum tfidf in the document is:", word)