Python中的文档矢量化表示

Question

我正在尝试使用python 3中的情感分析，并使用带有词袋模型的TDF-IDF矢量化器来矢量化文档。

因此，对于那些熟悉它的人来说，很明显，结果矩阵表示是稀疏的。

以下是我的代码片段。首先是文件。

tweets = [('Once you get inside you will be impressed with the place.',1),('I got home to see the driest damn wings ever!',0),('An extensive menu provides lots of options for breakfast.',1),('The flair bartenders are absolutely amazing!',1),('My first visit to Hiro was a delight!',1),('Poor service, the waiter made me feel like I was stupid every time he came to the table.',0),('Loved this place.',1),('This restaurant has great food',1),
      ('Honeslty it did not taste THAT fresh :(',0),('Would not go back.',0),
       ('I was shocked because no signs indicate cash only.',0),
        ('Waitress was a little slow in service.',0),
        ('did not like at all',0),('The food, amazing.',1),
        ('The burger is good beef, cooked just right.',1),
        ('They have horrible attitudes towards customers, and talk down to each one when customers do not enjoy their food.',0),
        ('The cocktails are all handmade and delicious.',1),('This restaurant has terrible food',0),
        ('Both of the egg rolls were fantastic.',1),('The WORST EXPERIENCE EVER.',0),
        ('My friend loved the salmon tartar.',1),('Which are small and not worth the price.',0),
        ('This is the place where I first had pho and it was amazing!!',1),
        ('Horrible - do not waste your time and money.',0),('Seriously flavorful delights, folks.',1),
        ('I loved the bacon wrapped dates.',1),('I dressed up to be treated so rudely!',0),
        ('We literally sat there for 20 minutes with no one asking to take our order.',0),
        ('you can watch them preparing the delicious food! :)',1),('In the summer, you can dine in a charming outdoor patio - so very delightful.',1)]

X_train, y_train = zip(*tweets)

以下代码用于矢量化文档。

tfidfvec = TfidfVectorizer(lowercase=True)
vectorized = tfidfvec.fit_transform(X_train)

print(vectorized)

当我打印vectorized时，它不会输出正常的矩阵。相反，这个：

如果我没错，那必须是稀疏矩阵表示。但是，我无法理解它的格式，以及每个术语的含义。

此外，还有30个文件。所以，这解释了第一列的0-29。如果这是趋势那么我猜第二列是单词的索引，最后一个值是它的tf-idf？当我输入我的问题时，它只是让我感到震惊，但如果我错了，请好好纠正我。

有经验的人能帮助我更好地理解它吗？

Answer 1

是的，从技术上讲，前两个元组代表行列位置，第三列是该位置的值。所以它基本上显示了非零值的位置和值。