In this paper, we examine prior techniques that have been used to successfully rank helpfulness for product reviews, and adapt them to the peer-review domain.
we use an SVM regression algorithm to predict the helpfulness of peer reviews based on generic linguistic features automatically mined from peer reviews and students' papers, plus specialized features based on existing knowledge about peer reviews.