ATESA-B{\AE}RT: A Heterogeneous Ensemble Learning Model for Aspect-Based Sentiment Analysis

TL;DR

This paper introduces ATESA-B{ ext}RT, a heterogeneous ensemble model that improves aspect-based sentiment analysis by effectively handling multiple aspects within reviews, outperforming existing methods.

Contribution

The paper proposes a novel ensemble learning approach that divides the task into two sub-tasks and uses multiple transformers to enhance aspect-based sentiment analysis.

Findings

ATESA-B{ ext}RT outperforms current state-of-the-art models.

The model effectively handles multiple aspects in reviews.

Experimental results on two datasets demonstrate improved accuracy.

Abstract

The increasing volume of online reviews has made possible the development of sentiment analysis models for determining the opinion of customers regarding different products and services. Until now, sentiment analysis has proven to be an effective tool for determining the overall polarity of reviews. To improve the granularity at the aspect level for a better understanding of the service or product, the task of aspect-based sentiment analysis aims to first identify aspects and then determine the user's opinion about them. The complexity of this task lies in the fact that the same review can present multiple aspects, each with its own polarity. Current solutions have poor performance on such data. We address this problem by proposing ATESA-B{\AE}RT, a heterogeneous ensemble learning model for Aspect-Based Sentiment Analysis. Firstly, we divide our problem into two sub-tasks, i.e., Aspect Term Extraction and Aspect Term Sentiment Analysis. Secondly, we use the \textit{argmax} multi-class classification on six transformers-based learners for each sub-task. Initial experiments on two datasets prove that ATESA-B{\AE}RT outperforms current state-of-the-art solutions while solving the many aspects problem.