Our key idea is to distinguish between unambiguous, easy-tomine reviews and ambiguous, hard-to-classify reviews.
given a set of reviews, we applied (1) an unsupervised algorithm to identify and classify those that are unambiguous, (2) an active learner that is trained solely on automatically labeled unambiguous reviews to identify a small number of prototypical ambiguous reviews for manual labeling, and (3) an ensembled transductive learner to train a sophisticated classifier on the reviews labeled so far to handle the ambiguous reviews.