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Arabic aspect sentiment polarity classification using BERT

Research Authors
Mohammed M. Abdelgwad, Taysir Hassan A. Soliman & Ahmed I. Taloba
Research Date
Research Department
Research File
Research Journal
Journal of Big Data
Research Rank
Q1
Research Publisher
SpringerOpen
Research Vol
9
Research Website
https://doi.org/10.1186/s40537-022-00656-6
Research Year
2022
Research_Pages
15
Research Abstract

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, 73.23% on the Human annotated book reviews dataset, and 85.73% on the Arabic news dataset.