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Internet users generate content at unprecedented rates. Building intelligentsystems capable of discriminating useful content within this ocean ofinformation is thus becoming a urgent need. In this paper, we aim to predictthe usefulness of Amazon reviews, and to do this we exploit features comingfrom an off-the-shelf argumentation mining system. We argue that the usefulnessof a review, in fact, is strictly related to its argumentative content, whereasthe use of an already trained system avoids the costly need of relabeling anovel dataset. Results obtained on a large publicly available corpus supportthis hypothesis.

Predicting the Usefulness of Amazon Reviews Using Off-The-Shelf Argumentation Mining