Arabic aspect based sentiment classification using BERT [article]

Mohammed M.Abdelgwad
<span title="2021-11-27">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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
more &raquo; ... ng 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.13290v3">arXiv:2107.13290v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hjkmmmo2y5dstoc64xighnqi2y">fatcat:hjkmmmo2y5dstoc64xighnqi2y</a> </span>
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