  In online review sites, the analysis of user feedback for assessing its
helpfulness for decision-making is usually carried out by locally studying the
properties of individual reviews. However, global properties should be
considered as well to precisely evaluate the quality of user feedback. In this
paper we investigate the role of deviations in the properties of reviews as
helpfulness determinants with the intuition that "out of the core" feedback
helps item evaluation. We propose a novel helpfulness estimation model that
extends previous ones with the analysis of deviations in rating, length and
polarity with respect to the reviews written by the same person, or concerning
the same item. A regression analysis carried out on two large datasets of
reviews extracted from Yelp social network shows that user-based deviations in
review length and rating clearly influence perceived helpfulness. Moreover, an
experiment on the same datasets shows that the integration of our helpfulness
estimation model improves the performance of a collaborative recommender system
by enhancing the selection of high-quality data for rating estimation. Our
model is thus an effective tool to select relevant user feedback for
decision-making.
