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    <abstract>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.</abstract>
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%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

Markdown (Informal)

[Predicting the Usefulness of Amazon Reviews Using Off-The-Shelf Argumentation Mining](https://aclanthology.org/W18-5205) (Passon et al., ArgMining 2018)

• Predicting the Usefulness of Amazon Reviews Using Off-The-Shelf Argumentation Mining (Passon et al., ArgMining 2018)

ACL

• Marco Passon, Marco Lippi, Giuseppe Serra, and Carlo Tasso. 2018. Predicting the Usefulness of Amazon Reviews Using Off-The-Shelf Argumentation Mining. In Proceedings of the 5th Workshop on Argument Mining, pages 35–39, Brussels, Belgium. Association for Computational Linguistics.