  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.
