%0 Conference Proceedings %T Predicting the Usefulness of Amazon Reviews Using Off-The-Shelf Argumentation Mining %A Passon, Marco %A Lippi, Marco %A Serra, Giuseppe %A Tasso, Carlo %Y Slonim, Noam %Y Aharonov, Ranit %S Proceedings of the 5th Workshop on Argument Mining %D 2018 %8 November %I Association for Computational Linguistics %C Brussels, Belgium %F passon-etal-2018-predicting %X 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 off-the-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. %R 10.18653/v1/W18-5205 %U https://aclanthology.org/W18-5205 %U https://doi.org/10.18653/v1/W18-5205 %P 35-39