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
相关论文
共 50 条
  • [41] Aspect-Based Sentiment Analysis of Patient Feedback Using Large Language Models
    Alkhnbashi, Omer S.
    Mohammad, Rasheed
    Hammoudeh, Mohammad
    BIG DATA AND COGNITIVE COMPUTING, 2024, 8 (12)
  • [42] Leveraging hierarchical language models for aspect-based sentiment analysis on financial data
    Lengkeek, Matteo
    Knaap, Finn van der
    Frasincar, Flavius
    INFORMATION PROCESSING & MANAGEMENT, 2023, 60 (05)
  • [43] Aspect-Based Sentiment Analysis for User Reviews
    Du, Jinyang
    Zhang, Yin
    Ma, Xiao
    Wen, Haoyu
    Fortino, Giancarlo
    COGNITIVE COMPUTATION, 2021, 13 (05) : 1114 - 1127
  • [44] DRGCN Multitasking for Aspect-Based Sentiment Analysis
    Du, Mengyang
    Wang, Hongbin
    JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS, 2025, 29 (02) : 268 - 276
  • [45] Investigating the Saliency of Sentiment Expressions in Aspect-Based Sentiment Analysis
    Wagner, Joachim
    Foster, Jennifer
    FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2023), 2023, : 12751 - 12769
  • [46] DomBERT: Domain-oriented Language Model for Aspect-based Sentiment Analysis
    Xu, Hu
    Liu, Bing
    Shu, Lei
    Yu, Philip S.
    FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, EMNLP 2020, 2020, : 1725 - 1731
  • [47] Data augmentation for aspect-based sentiment analysis
    Li, Guangmin
    Wang, Hui
    Ding, Yi
    Zhou, Kangan
    Yan, Xiaowei
    INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2023, 14 (01) : 125 - 133
  • [48] Sentence Compression for Aspect-Based Sentiment Analysis
    Che, Wanxiang
    Zhao, Yanyan
    Guo, Honglei
    Su, Zhong
    Liu, Ting
    IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 2015, 23 (12) : 2111 - 2124
  • [49] A comprehensive survey on aspect-based sentiment analysis
    Yadav, Kaustubh
    Kumar, Neeraj
    Maddikunta, Praveen Kumar Reddy
    Gadekallu, Thippa Reddy
    INTERNATIONAL JOURNAL OF ENGINEERING SYSTEMS MODELLING AND SIMULATION, 2021, 12 (04) : 279 - 290
  • [50] Aspect-based sentiment analysis with metaphorical information
    Tian H.
    Yu L.
    Tian S.
    Long J.
    Zhou T.
    Wang B.
    Li Y.
    Journal of Intelligent and Fuzzy Systems, 2024, 46 (04): : 8065 - 8074