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.

Predicting the usefulness of amazon reviews using off-the-shelf argumentation mining / Passon, M.; Lippi, M.; Serra, G.; Tasso, C.. - (2018). ((Intervento presentato al convegno 5th Workshop on Argument Mining tenutosi a Bruxelles nel October 31 – November 1, 2018.

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