Argumentation is the discipline that studies the way in which humans debate and articulate their opinions and beliefs. Argumentation mining is a research area at the cross-road of many fields, such as computational linguistics, machine learning, artificial intelligence, natural-language processing. The main goal of argumentation mining is the automatically extraction and identification of arguments and their relations from natural language text documents.
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.
Related publications:
- Passon M., Lippi M., Serra G., Tasso C. Predicting the Usefulness of Amazon Reviews Using Off-The-Shelf Argumentation Mining. In: Proc. of Workshop on Argument Mining, Brussels, Belgium, 2018.