I have built a text classifier using OneClassSVM.
I have the training set which corresponds to only one label i.e("Yes") and I don't have the other("NO") label data. My task is to build a classifier which classifies the new unseen sentence(test data) as 1 if it is very similar to the training data. Else, it classifies as -1 i.e,(anomaly).
I have used Word2Vec to build the word embeddings for my training data. Then, I am using word-vector averaging with OneClassSVM to build a anomaly detector classifier.
This classifier is currently giving accuracy of about 50%-55%. I have to enhance this further to build a robust classifier.
Any suggestions to this problem would be helpful...