Comparing word embedding models for Arabic aspect category detection using a deep learning-based approach

• Published in: E3S Web of Conferences 2021-01, Vol.297, p.1072
• Main Authors: Bensoltane, Rajae, Zaki, Taher
• Format: Article
• Language: English
• Subjects: arabic; aspect category detection; aspect-based sentiment analysis; bigru; Data mining; Deep learning; Embedding; Learning algorithms; Machine learning; word embeddings
• ISSN: 2267-1242; 2555-0403; 2267-1242
• DOI: 10.1051/e3sconf/202129701072

Aspect category detection (ACD) is a task of aspect-based sentiment analysis (ABSA) that aims to identify the discussed category in a given review or sentence from a predefined list of categories. ABSA tasks were widely studied in English; however, studies in other low-resource languages such as Arabic are still limited. Moreover, most of the existing Arabic ABSA work is based on rule-based or feature-based machine learning models, which require a tedious task of feature-engineering and the use of external resources like lexicons. Therefore, the aim of this paper is to overcome these shortcomings by handling the ACD task using a deep learning method based on a bidirectional gated recurrent unit model. Additionally, we examine the impact of using different vector representation models on the performance of the proposed model. The experimental results show that our model outperforms the baseline and related work models significantly by achieving an enhanced F1-score of more than 7%.