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

arXiv: https://arxiv.org/abs/1809.08145 · HTML: https://mlsanity.com/paper/1809.08145/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2018.

## Built on by (most cited first, 3 of 3)

- [Scientia Potentia Est -- On the Role of Knowledge in Computational Argumentation](https://mlsanity.com/paper/2107.00281/) (2021)
- [Information Types in Product Reviews](https://mlsanity.com/paper/2502.14335/) (2025)
- [Improving Neural Argumentative Stance Classification in Controversial Topics with Emotion-Lexicon Features](https://mlsanity.com/paper/2602.22846/) (2026)

## Abstract (arXiv)

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.