Mining Worse and Better Opinions. Unsupervised and Agnostic Aggregation of Online Reviews

TL;DR

This paper introduces an unsupervised, domain-agnostic method for aggregating online reviews based on opinion adherence, effectively grouping reviews by expressed opinions without relying on labeled data or domain-specific assumptions.

Contribution

It presents a novel unsupervised and domain-agnostic approach for opinion aggregation in online reviews, utilizing an adherence metric to group reviews by sentiment.

Findings

Adherence correlates with review scores.

Effective review grouping based on opinion adherence.

Validated on Booking and Amazon datasets.

Abstract

In this paper, we propose a novel approach for aggregating online reviews, according to the opinions they express. Our methodology is unsupervised - due to the fact that it does not rely on pre-labeled reviews - and it is agnostic - since it does not make any assumption about the domain or the language of the review content. We measure the adherence of a review content to the domain terminology extracted from a review set. First, we demonstrate the informativeness of the adherence metric with respect to the score associated with a review. Then, we exploit the metric values to group reviews, according to the opinions they express. Our experimental campaign has been carried out on two large datasets collected from Booking and Amazon, respectively.