Aspect-based sentiment analysis(ABSA) is a textual analysis methodology that defines the polarity of opinions on certain aspects related to specific targets. The majority of research on ABSA is in English, with a small amount of work available in Arabic. Most previous Arabic research has relied on deep learning models that depend primarily on context-independent word embeddings (e.g.word2vec), where each word has a fixed representation independent of its context. This article explores the modeling capabilities of contextual embeddings from pre-trained language models, such as BERT, and making use of sentence pair input on Arabic aspect sentiment polarity classification task. In particular, we develop a simple but effective BERT-based neural baseline to handle this task. Our BERT architecture with a simple linear classification layer surpassed the state-of-the-art works, according to the experimental results on three different Arabic datasets. Achieving an accuracy of 89.51% on the Arabic hotel reviews dataset, 73% on the Human annotated book reviews dataset, and 85.73% on the Arabic news dataset.

翻译：方面级情感分析（ABSA）是一种文本分析方法，用于确定特定目标相关方面的意见极性。目前大多数ABSA研究针对英语，仅有少量阿拉伯语相关工作。以往阿拉伯语研究主要依赖基于上下文无关词嵌入（如word2vec）的深度学习模型，这类模型中每个词具有独立于上下文的固定表示。本文探索了预训练语言模型（如BERT）的上下文嵌入建模能力，并利用句子对输入处理阿拉伯语方面情感极性分类任务。我们特别构建了一个简单但有效的基于BERT的神经基线模型来处理该任务。在三个不同阿拉伯语数据集上的实验结果表明，我们采用简单线性分类层的BERT架构超越了现有最优方法：在阿拉伯语酒店评论数据集上准确率达89.51%，在人工标注书评数据集上达73%，在阿拉伯语新闻数据集上达85.73%。