can we classify text using SVM classifier in python-TextBlob? -

  & gt; & Gt; & Gt; Train '[(' I love this sandwich ',' pause '), (' this is a wonderful place! ',' Paus '), (' I feel great about these beer ',' Pos '), (' This is my best job ',' pause '), ("a wonderful view",' pause '), (' I do not like this restaurant ',' neg '), (' I 'Neg'), ("I can not deal with this", 'Neg'), ('He is my cursed enemy!', 'Negative'), ('My boss is terrible . ',' Neg ')]> Gt; & Gt; & Gt; Test = [(('Beer was good', 'pause'), ('I do not like my work', 'neg'), ("today I do not feel bored.", 'Neig'), (" I feel amazing! ", 'Pause'), ('Gary is my friend.', 'Poiss'), (" I do not believe I am doing this. ", 'Neg')]> > NaiveBayesClassifier>> CL = Nibbless Classifier (Train)>> Cl.classify ("This is a wonderful Library!") Import from Textblob.classifiers. ) 'Pos'  

The above code to classify a text Similarly, I have used the Maxextclifier, the Designation Classifier, and now I want to know that in order to classify Ajay I have mentioned any class squares other than those people. Please tell me!