AMAR_ABSA: Arabic Mobile App Reviews Dataset Targeting Aspect-based Sentiment Analysis Tasks

Voskergian, Daniel; Saheb, Mahmoud

doi:10.1109/asyu56188.2022.9925324

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2024

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The Integration of NLP and Topic-Modeling-Based Machine Learning Approaches for Arabic Mobile App Review Classification

Voskergian,

Khamayseh

2024

Communications in Computer and Information Science

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The Integration of NLP and Topic-Modeling-Based Machine Learning Approaches for Arabic Mobile App Review Classification

Voskergian,

Khamayseh

2024

Communications in Computer and Information Science

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Arabic App Reviews: Analysis and Classification

Aljeezani,

Alomari,

Ahmad

2024

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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User opinions and feedback on mobile applications are crucial for application developers, offering insights into issues like bugs, popular features, and enhancement requests. Given the vast number of feedback for each app, it is impractical for developers to manually extract valuable information. To better understand and analyze user opinions, developers can benefit from automatic sentiment analysis and classification of app reviews. Existing research has primarily focused on reviews written in English, with some studies addressing sentiment analysis of Arabic reviews but overlooking the classification task. Given the widespread use and complexity of Arabic compared to English, our work investigates both sentiment analysis and classification of Arabic app reviews. We introduce the AURA ( A pp U ser R eview in A rabic) dataset. AURA dataset has two versions: AURA-Sentiment with 29,700 labeled reviews for sentiment analysis, and AURA-Classification with 2,900 labeled reviews for classification. Using these datasets, we applied deep learning (DL) and natural language processing (NLP) techniques for analyzing and classifying Arabic app reviews. Leveraging the MarBert model, we achieved an F1-score of 0.89 for sentiment analysis and an F1-score of 0.62 for a four-class classification problem. Our findings provide valuable insights and suggest directions for future research.

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AMAR_ABSA: Arabic Mobile App Reviews Dataset Targeting Aspect-based Sentiment Analysis Tasks

Cited by 2 publications

References 0 publications

The Integration of NLP and Topic-Modeling-Based Machine Learning Approaches for Arabic Mobile App Review Classification

The Integration of NLP and Topic-Modeling-Based Machine Learning Approaches for Arabic Mobile App Review Classification

Arabic App Reviews: Analysis and Classification

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