python - 我们可以在 python-TextBlob 中使用 SVM 分类器对文本进行分类吗？

Question

>>> train = [
   ('I love this sandwich.', 'pos'),
   ('this is an amazing place!', 'pos'),
   ('I feel very good about these beers.', 'pos'),
   ('this is my best work.', 'pos'),
   ("what an awesome view", 'pos'),
   ('I do not like this restaurant', 'neg'),
   ('I am tired of this stuff.', 'neg'),
   ("I can't deal with this", 'neg'),
   ('he is my sworn enemy!', 'neg'),
   ('my boss is horrible.', 'neg')
   ]
>>> test = [
    ('the beer was good.', 'pos'),
    ('I do not enjoy my job', 'neg'),
    ("I ain't feeling dandy today.", 'neg'),
    ("I feel amazing!", 'pos'),
    ('Gary is a friend of mine.', 'pos'),
    ("I can't believe I'm doing this.", 'neg')
    ]
>>> from textblob.classifiers import NaiveBayesClassifier
>>> cl = NaiveBayesClassifier(train)
>>> cl.classify("This is an amazing library!")
'pos'

上面的代码用于使用 Python 通过 NaiveBayesClassifier 对文本进行分类。同样，我使用了 MaxEntClassifier、DecisionTreeClassifier。现在我想知道除了我提到的用于在 python 中分类的分类器之外，还有其他分类器。请告诉我！！！