Argument Mining for Review Helpfulness Prediction
Zaiqian Chen, Daniel Verdi do Amarante, Jenna Donaldson, Yohan Jo, Joonsuk Park · 2022
The importance of reliably determining the helpfulness of product reviews is rising as both helpful and unhelpful reviews continue to accumulate on e-commerce websites.And argumentational features-such as the structure of arguments and the types of underlying elementary units-have shown to be promising indicators of product review helpfulness.However, their adoption has been limited due to the lack of sufficient resources and large-scale experiments investigating their utility.To this end, we present the AMazon Argument Mining (AM 2 ) corpus-a corpus of 878 Amazon reviews on headphones annotated according to a theoretical argumentation model designed to evaluate argument quality.Experiments show that employing argumentational features leads to statistically significant improvements over the state-of-the-art review helpfulness predictors under both text-only and text-and-image settings.1