Predicting the Usefulness of Amazon Reviews Using Off-The-Shelf Argumentation Mining
This work addresses the need for intelligent systems to filter useful content from vast user-generated data, such as online reviews, but it is incremental as it applies existing methods to a new domain.
The paper tackled the problem of predicting the usefulness of Amazon reviews by using features from an off-the-shelf argumentation mining system, achieving results that support the hypothesis that usefulness is related to argumentative content on a large publicly available corpus.
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.