ATESA-BÆRT: A heterogeneous ensemble learning model for Aspect-Based Sentiment Analysis
Elena‐Simona Apostol, Alin-Georgian Pisică, Ciprian‐Octavian Truică · Knowledge-Based Systems · 2025
The increasing volume of online reviews has made the development of sentiment analysis models possible for determining customers’ opinions 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 argmax multi-class classification on six transformers-based learners for each sub-task. The proposed ensemble integrates representations from pre-trained and fine-tuned BERT and BART models in order to capture diverse linguistic features. By combining these with Linear , BiLSTM , and CNN-BiLSTM models, we aim to achieve enhanced performance on the aspect-based sentiment analysis task. Initial experiments on two publicly available real-world English online review datasets prove that ATESA-BÆRT outperforms current state-of-the-art solutions while tackling the sentiment analysis many aspects problem.