I'm looking for something similar to this
But instead of positive and negative examples, I have positive examples and a bunch of unlabeled data that will contain some positive examples but is mostly negative.
I'm planning on using this in a pipeline to transform text data into a vector, then feeding it into a classifier using
The issue is I'm not sure the best way to build the preprocessing stage where I transform the raw text data into a vector which would then be fed into the classification model.
If anyone has any different ideas on how I can transform positive and unlabeled raw text into a vector to feed into the pulearn module I would like to hear as well, thanks!