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

TL;DR

This paper presents a method to predict the usefulness of Amazon reviews by leveraging features from an off-the-shelf argumentation mining system, demonstrating effectiveness on a large dataset.

Contribution

It introduces the use of pre-trained argumentation mining features to assess review usefulness, avoiding costly relabeling of new datasets.

Findings

Features from argumentation mining improve usefulness prediction.

The approach performs well on a large publicly available corpus.

Using off-the-shelf systems reduces annotation costs.

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.