RAS

Product reviews are very useful to help online users appreciate the quality of products and services they may make use of, but the quality of such reviews is very diverse. Reasoning on the arguments in reviews is a promising method to appreciate review quality. However, computational argumentation can be inefficient and computationally expensive. This project was meant to improve the scalability of a computational pipeline for reasoning on arguments of product reviews in order to estimate their quality.
We started from an existing pipeline and introduced two types of speed improvements. First, we parallelized the code, so as to allow processing multiple reviews in parallel to identify their arguments and compare them. This led to an improvement in the computational time, up to a factor of more than 20 times.
Second, we introduced a lossy improvement, i.e., we analyzed reviews after having removed the less important parts of them. This method is promising but needs to be used with care.
The advancements obtained with this project allowed for strengthening the line of research linking argument reasoning with information quality. These findings will be merged with an existing project looking at the possibility of employing a more complex argumentation framework in this area.