Python 3.x Python中的文档矢量化表示

我尝试用python 3进行情感分析，并使用TDF-IDF矢量化器和bag of words模型对文档进行矢量化

所以，对于熟悉这一点的人来说，很明显，得到的矩阵表示是稀疏的

下面是我的代码片段。首先，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)

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

print(vectorized)

和下面的代码来矢量化文档

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)

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

print(vectorized)

当我打印矢量化的

时，它不会输出标准矩阵。相反，这是：

如果我没有错的话，这一定是稀疏矩阵表示法。然而，我无法理解它的格式，以及每个术语的含义

此外，还有30份文件。这就解释了第一列的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tf idf？当我输入问题时，我突然想到了这一点，但如果我错了，请纠正我

有这方面经验的人能帮我更好地理解它吗？
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