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

Marco Passon, Marco Lippi, Giuseppe Serra, Carlo Tasso

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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.
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
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., ArgMining 2018)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/W18-5205.pdf
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