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.

翻译：在线评论数量的不断增长推动了情感分析模型的发展，这些模型用于判断顾客对不同产品和服务的意见。迄今为止，情感分析已被证明是确定评论整体极性的有效工具。为了提高在方面层面的粒度，从而更好地理解服务或产品，基于方面的情感分析任务旨在首先识别方面，然后确定用户对这些方面的意见。该任务的复杂性在于，同一评论可能包含多个方面，每个方面都有其自身的极性。目前的解决方案在此类数据上表现不佳。我们通过提出ATESA-BÆRT（一种用于基于方面的情感分析的异构集成学习模型）来解决这一问题。首先，我们将问题分为两个子任务，即方面术语提取和方面术语情感分析。其次，我们对每个子任务采用基于六个Transformer学习器的\textit{argmax}多类分类。在两个数据集上的初步实验证明，ATESA-BÆRT在解决多方面问题的同时，优于当前最先进的解决方案。