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

Marco Passon, Marco Lippi, Giuseppe Serra, Carlo Tasso


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
Venues:
ArgMining | EMNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
35–39
Language:
URL:
https://aclanthology.org/W18-5205
DOI:
10.18653/v1/W18-5205
Bibkey:
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., 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-5205.pdf