Arwa Saif Fadel^1,2,*, Osama Ahmed Abulnaja¹, Mostafa Elsayed Saleh¹

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4419-4444, 2023, DOI:10.32604/cmc.2023.037112

Abstract Aspect-based sentiment analysis (ABSA) is a fine-grained process. Its fundamental subtasks are aspect term extraction (ATE) and aspect polarity classification (APC), and these subtasks are dependent and closely related. However, most existing works on Arabic ABSA content separately address them, assume that aspect terms are preidentified, or use a pipeline model. Pipeline solutions design different models for each task, and the output from the ATE model is used as the input to the APC model, which may result in error propagation among different steps because APC is affected by ATE error. These methods are impractical for real-world scenarios where the… More >

Manar Ahmed Hamza^1,*, Hala J. Alshahrani², Jaber S. Alzahrani³, Heba Mohsen⁴, Mohamed I. Eldesouki⁵, Mohammed Rizwanullah¹

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2619-2635, 2023, DOI:10.32604/csse.2023.034519

Abstract Aspect-Based Sentiment Analysis (ABSA) on Arabic corpus has become an active research topic in recent days. ABSA refers to a fine-grained Sentiment Analysis (SA) task that focuses on the extraction of the conferred aspects and the identification of respective sentiment polarity from the provided text. Most of the prevailing Arabic ABSA techniques heavily depend upon dreary feature-engineering and pre-processing tasks and utilize external sources such as lexicons. In literature, concerning the Arabic language text analysis, the authors made use of regular Machine Learning (ML) techniques that rely on a group of rare sources and tools. These sources were used for… More >