Aspect-based sentiment analysis: an overview in the use of Arabic language

被引:16
|
作者
Bensoltane, Rajae [1 ]
Zaki, Taher [1 ]
机构
[1] Ibn Zohr Univ, Fac Sci, IRF SIC Lab, Agadir, FP, Morocco
关键词
Natural language processing; Sentiment analysis; Aspect-based; Aspect extraction; Aspect sentiment classification; Arabic language; DEEP LEARNING-MODEL; NEURAL-NETWORK; TWEETS; COMBINATION; EXTRACTION; ATTENTION; MACHINE; SET;
D O I
10.1007/s10462-022-10215-3
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Sentiment analysis has become one of the most active research areas in natural language processing, and the Arabic language retains its importance in this field. It is so because of the increased use of Arabic on the internet that pushes many users to share their views or thoughts about certain products and services. Despite its crucial importance, most of the existing Arabic sentiment analysis studies have been performed on document or sentence levels with little attention to the aspect level. However, the aspect level's main objective, also known as aspect-based sentiment analysis, is to extract the discussed aspects and identify their related sentiment polarities from a given review or text. The result is to provide more detailed information than general sentiment analysis. Therefore, this paper seeks to provide a comprehensive review of the Arabic aspect-based sentiment analysis studies and highlights the main challenges that face the different proposed approaches. The relevant gaps in the current literature and the future research directions in this area are also discussed. This survey can guide future researchers who want to contribute to the improvement of this domain.
引用
收藏
页码:2325 / 2363
页数:39
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