从tf-idf稀疏矩阵中获取最重要的单词(最高tf-idf值)

Question

我有一个大小为208 (208个句子数组)的列表，如下所示：

all_words = [["this is a sentence ... "] , [" another one hello bob this is alice ... "] , ["..."] ...] 

我想获取tf-idf值最高的单词。我创建了一个tf-idf矩阵：

from sklearn.feature_extraction.text import TfidfVectorizer

tokenize = lambda doc: doc.split(" ")
sklearn_tfidf = TfidfVectorizer(norm='l2', tokenizer=tokenize, ngram_range=(1,2))
tfidf_matrix = sklearn_tfidf.fit_transform(all_words)
sentences = sklearn_tfidf.get_feature_names()

dense_tfidf = tfidf_matrix.todense()

现在我不知道如何获得tf-idf值最高的单词。

dense_tfidf的每一列表示一个单词/2个单词。(矩阵为208x5481)

当我对每一列进行求和时，并没有真正的帮助--简单的前几个单词得到了相同的结果(我猜是因为它和简单的单词计数是一样的)。

如何获取tf-idf值最高的单词？或者我如何才能明智地将其规范化？

Answer 1

有类似的问题，但在https://towardsdatascience.com/multi-class-text-classification-with-scikit-learn-12f1e60e0a9f上发现了这个，只需根据数据帧更改X和Y输入即可。博客中的代码如下。Sklearn的文档帮助了我：http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.chi2.html

from sklearn.feature_selection import chi2
import numpy as np
N = 2
for Product, category_id in sorted(category_to_id.items()):
features_chi2 = chi2(features, labels == category_id)
indices = np.argsort(features_chi2[0])
feature_names = np.array(tfidf.get_feature_names())[indices]
unigrams = [v for v in feature_names if len(v.split(' ')) == 1]
bigrams = [v for v in feature_names if len(v.split(' ')) == 2]
print("# '{}':".format(Product))
print("  . Most correlated unigrams:\n. {}".format('\n. '.join(unigrams[-N:])))
print("  . Most correlated bigrams:\n. {}".format('\n. '.join(bigrams[-N:])))