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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, 73.23% on the Human annotated book reviews dataset, and 85.73% on the Arabic news dataset.<\/jats:p>","DOI":"10.1186\/s40537-022-00656-6","type":"journal-article","created":{"date-parts":[[2022,12,3]],"date-time":"2022-12-03T10:02:42Z","timestamp":1670061762000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["Arabic aspect sentiment polarity classification using BERT"],"prefix":"10.1186","volume":"9","author":[{"given":"Mohammed M.","family":"Abdelgwad","sequence":"first","affiliation":[]},{"given":"Taysir Hassan A.","family":"Soliman","sequence":"additional","affiliation":[]},{"given":"Ahmed I.","family":"Taloba","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,3]]},"reference":[{"key":"656_CR1","doi-asserted-by":"crossref","unstructured":"Hu M, Liu B. 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