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.- Anthology ID:
- W18-5205
- Volume:
- Proceedings of the 5th Workshop on Argument Mining
- Month:
- November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Noam Slonim, Ranit Aharonov
- Venue:
- ArgMining
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 35–39
- Language:
- URL:
- https://aclanthology.org/W18-5205
- DOI:
- 10.18653/v1/W18-5205
- Cite (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.
- Cite (Informal):
- Predicting the Usefulness of Amazon Reviews Using Off-The-Shelf Argumentation Mining (Passon et al., ArgMining 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/W18-5205.pdf