How to use scikit-learn to extract features from text when I only have positive and unlabeled data?
So you should be clearer with what you are asking.

What are the classes of your classifier? Positive and unlabelled?

To create numeric feature from text you can use:
a) tf-idf, which works well with small datasets/ sentences.
b) Handmade features, like extracting sentence length, occurence of certain words..
c) Word embeddings, like word2vec or sentence2vec. Strong method when a does not work. This will convert every word or sentence/ document to a numeric vector of length n, usually between 100 and 500. See the gensim library for that
d) a combination of the above.


The main criteria of choosing the feature transformer should be that it captures your text well and that is something you can decide only by looking at the data yourself and trying out some experiments.
Good luck!