The main issue for an active learner is the choosing of next unlabeled instance to query. In this paper, we choose the reviews whose labels are most uncertain for the classifier. Following previous work on active learning for SVMs<cite> (Dasgupta and Ng, 2009</cite>; Tong and Koller, 2002) , we define the uncertainty of a review as its distance from the separating hyperplane. In other words, reviews that are near the separating hyperplane are chosen as the labeled training data.