In light of the difficulties posed by ambiguous reviews, we differentiate between ambiguous and unambiguous reviews in our classification process by addressing the task of semi-supervised polarity classification via a "mine the easy, classify the hard" approach.
we propose a novel system architecture where we first automatically identify and label the unambiguous (i.e., "easy") reviews, then handle the ambiguous (i.e., "hard") reviews using a discriminative learner to bootstrap from the automatically labeled unambiguous reviews and a small number of manually labeled reviews that are identified by an active learner.