Unsupervised binary classification on small dataset
Correct me if I'm wrong, but your unsupervised classification is not much different than clustering. This is because since it is unsupervised, you do not actually have a user-provided indication of what your classes should be. Thus an unsupervised machine learning system would guess its own classes, and an straightforward way would be to make these classes correspond to the clusters.

In your case you could represent each document as bag-of-words. Then, if necessary, you can do feature reduction (e.g. using PCA). Finally, you could use k-means (in fact 2-means, since you want binary classification). Assigning a new element to any of the two clusters is your classifier.