如何获得最重要单词的 TF-IDF 分数？

Question

我正在使用 tf-idf 进行一个项目，我的数据框中有一列 (df['liststring'])，其中包含来自我的预处理文本（没有标点符号、停用词等）各种文件。

我运行下面的代码，我得到了 tf-idf 值最高的前 10 个词，但我也想看看它们的分数。

    from sklearn.feature_extraction.text import TfidfVectorizer
    tfidf = TfidfVectorizer()
    X_tfidf = tfidf.fit_transform(df['liststring']).toarray()
    vocab = tfidf.vocabulary_
    reverse_vocab = {v:k for k,v in vocab.items()}
    feature_names = tfidf.get_feature_names()
    df_tfidf = pd.DataFrame(X_tfidf, columns = feature_names)
    idx = X_tfidf.argsort(axis=1)
    tfidf_max10 = idx[:,-10:]
    df_tfidf['top10'] = [[reverse_vocab.get(item) for item in row] for row in tfidf_max10 ]
    
df_tfidf['top10']

0      [kind, pose, world, preventive, sufficient, ke...
1      [mode, california, diseases, evidence, zoonoti...
2      [researcher, commentary, allegranzi, say, mora...
3      [carry, mild, man, whatever, suffering, downpl...
4      [region, service, almost, wednesday, detect, f...
                             ...                        
754    [americans, plan, year, black, online, shop, s...
755    [relate, manor, tuesday, death, portobello, ce...
756    [one, october, eight, exist, transmit, cluster...
757    [wolfe, shelter, county, resident, cupertino, ...
758    [firework, year, blasio, day, marching, reimag...

如果我们以第一行为例，而不是 [kind, pose, world, preventive, sufficient, ke...]，我想让输出看起来像 [kind:0.2, pose:0.3, world:0.4, preventive:0.5, sufficient:0.6, ke...]

Answer 1

df_tfidf['top10'] = [[(reverse_vocab.get(item), X_tfidf[i, item])  for item in row] 
                     for i, row in enumerate(tfidf_max10) ]

测试用例：

df = pd.DataFrame(
    {'liststring': ['this is a cat', 'that is a dog', "a apple on the tree"]}
)
tfidf = TfidfVectorizer()
X_tfidf = tfidf.fit_transform(df['liststring']).toarray()
vocab = tfidf.vocabulary_
reverse_vocab = {v:k for k,v in vocab.items()}
feature_names = tfidf.get_feature_names()
df_tfidf = pd.DataFrame(X_tfidf, columns = feature_names)
idx = X_tfidf.argsort(axis=1)
tfidf_max2 = idx[:,-2:]
print ([[(reverse_vocab.get(item), X_tfidf[i, item])  for item in row] 
                     for i, row in enumerate(tfidf_max2) ])

输出：

[[('cat', 0.6227660078332259), ('this', 0.6227660078332259)],
 [('dog', 0.6227660078332259), ('that', 0.6227660078332259)], 
 [('the', 0.5), ('tree', 0.5)]]

如何获得最重要单词的 TF-IDF 分数？

How to get the TF-IDF scores as well for the most important words?

python

scikit-learn

nlp

tf-idf