ATESA-BÆRT: A Heterogeneous Ensemble Learning Model for Aspect-Based Sentiment Analysis
This work addresses the challenge of analyzing sentiment at a granular aspect level in online reviews, which is important for businesses and researchers, but it appears incremental as it builds on existing ensemble and transformer methods.
The authors tackled the problem of aspect-based sentiment analysis, where multiple aspects in a single review each have their own sentiment polarity, by proposing ATESA-BÆRT, a heterogeneous ensemble learning model that outperforms state-of-the-art solutions on two datasets.
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-BÆRT, 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-BÆRT outperforms current state-of-the-art solutions while solving the many aspects problem.