计算字符串的tf-idf

Question

我有两个文档doc1.txt和doc2.txt。这两个文件的内容是:

 #doc1.txt
 very good, very bad, you are great
 #doc2.txt
 very bad, good restaurent, nice place to visit

我想让我的语料与,分开，这样我最终的DocumentTermMatrix变成:

      terms
 docs       very good      very bad        you are great   good restaurent   nice place to visit
 doc1       tf-idf          tf-idf         tf-idf          0                    0
 doc2       0               tf-idf         0               tf-idf             tf-idf

我知道，如何计算单个单词的DocumentTermMatrix(使用http://scikit-learn.org/stable/modules/feature_extraction.html)，但不知道如何在Python中计算strings的DocumentTermMatrix。

Answer 1

您可以将TfidfVectorizer的analyzer参数指定为一个函数，该函数以自定义的方式提取特征:

from sklearn.feature_extraction.text import TfidfVectorizer
docs = ['very good, very bad, you are great',
        'very bad, good restaurent, nice place to visit']
tfidf = TfidfVectorizer(analyzer=lambda d: d.split(', ')).fit(docs)
print tfidf.get_feature_names()

得到的特征如下:

['good restaurent', 'nice place to visit', 'very bad', 'very good', 'you are great']

---

如果你真的负担不起将所有的数据加载到内存中，这是一个解决方案:

from sklearn.feature_extraction.text import TfidfVectorizer
docs = ['doc1.txt', 'doc2.txt']
def extract(filename):
    with open(filename) as f:
        features = []
        for line in f:
            features += line.strip().split(', ')
        return features
tfidf = TfidfVectorizer(analyzer=extract).fit(docs)
print tfidf.get_feature_names()