Predicting the Usefulness of Amazon Reviews Using Off-The-Shelf Argumentation Mining
Marco Passon, Marco Lippi, Giuseppe Serra, Carlo Tasso · 2018
Internet users generate content at unprecedented rates.Building intelligent systems capable of discriminating useful content within this ocean of information is thus becoming a urgent need.In this paper, we aim to predict the usefulness of Amazon reviews, and to do this we exploit features coming from an offthe-shelf argumentation mining system.We argue that the usefulness of a review, in fact, is strictly related to its argumentative content, whereas the use of an already trained system avoids the costly need of relabeling a novel dataset.Results obtained on a large publicly available corpus support this hypothesis.