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.
- Passon M., Lippi M., Serra G., Tasso C. Predicting the Usefulness of Amazon Reviews Using Off-The-Shelf Argumentation Mining. In: Proc. of Workshop on Argument Mining, Brussels, Belgium, 2018.