Leveraging Transformers Ensemble to Improve Aspect Mining in Spanish Reviews

Abstract

Aspect-based sentiment analysis is a process aimed to understanding the sentiment expressed in opinions or reviews about specific features of an entity. The automatic extraction of aspects is the most challenging task, as it requires the ability to understand the context and to recognize the relevant and characteristic elements of an entity about which you have an opinion. To increase the quality results in the solution to this problem is still a challenge in Spanish reviews, because very few papers have been reported and the reported efficacy rates need to be improved. The use of deep learning models has proven an advantage for aspect extraction task, but the combination of several models for obtaining a final prediction has not yet been exploited. In this work, an aspect extraction method in which several Transformer models are combined through an ensemble learning approach using the Average Voting technique is presented. The proposed solution was evaluated using the SemEval2016 dataset and the results obtained were compared to those reported by other state-of-the-art solutions. The evaluation process not only provides a starting point to have a broader perception of the performance of the Transformers in this context, but also highlights the improvement of the quality results of the aspect extraction with the Transformers-Based Ensemble