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Knowledge-enhanced graph convolutional networks for Arabic aspect sentiment classification

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Abstract

Aspect sentiment classification (ASC) is a sub-task of aspect-based sentiment analysis (ABSA) that aims at identifying the sentiment polarity toward a specific aspect in a given text or sentence. Most existing research on Arabic ABSA adopted rule-based or machine learning-based methods, with little attention to deep learning techniques. Additionally, the majority of these deep learning-based models relied on attention mechanisms to capture the interaction between the context and aspect words. However, attention-based methods are generally inefficient in extracting the syntactic dependencies between contextual tokens and aspects. Therefore, we introduce a combined model that incorporates an Arabic BERT model with graph convolutional network and local context focus layers to capture syntactic dependencies relevant to a specific aspect while emphasizing the contribution of semantic-related tokens related to this aspect. We also integrate affective commonsense knowledge into the graph networks to capture the sentiment-related dependencies between contextual words and the specific aspect. The experimental results on an Arabic hotel dataset show that the proposed method outperforms the baseline and related work models and achieves a state-of-the-art accuracy score of 92.77% in Arabic ASC. The achieved results show the effectiveness of the proposed model in enhancing the aspect-specific sentiment representations, which can be promising for future research in this field.

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Notes

  1. https://github.com/songyouwei/ABSA-PyTorch

  2. https://github.com/BinLiang-NLP/Sentic-GCN

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Authors and Affiliations

  1. Laboratory of Innovation in Mathematics and Intelligent Systems, Faculty of Applied Sciences, Agadir, Morocco

    Rajae Bensoltane & Taher Zaki

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  1. Rajae Bensoltane
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  2. Taher Zaki
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Contributions

Rajae Bensoltane: Conceptualization, Methodology, Software, Validation, Investigation, Writing - original draft, Writing - review & editing. Taher Zaki: Conceptualization, Methodology, Investigation, Writing - review & editing, Supervision.

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Correspondence to Rajae Bensoltane.

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Bensoltane, R., Zaki, T. Knowledge-enhanced graph convolutional networks for Arabic aspect sentiment classification. Soc. Netw. Anal. Min. 14, 6 (2024). https://doi.org/10.1007/s13278-023-01166-w

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