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] Sentiment analysis versus aspect-based sentiment analysis versus emotion analysis from text: a comparative study
    Shukla, Diksha
    Dwivedi, Sanjay K.
    INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT, 2025, 16 (02) : 512 - 531
  • [42] A Survey on Multimodal Aspect-Based Sentiment Analysis
    Zhao, Hua
    Yang, Manyu
    Bai, Xueyang
    Liu, Han
    IEEE ACCESS, 2024, 12 : 12039 - 12052
  • [43] 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)
  • [44] Attention-based Sentiment Reasoner for aspect-based sentiment analysis
    Liu, Ning
    Shen, Bo
    Zhang, Zhenjiang
    Zhang, Zhiyuan
    Mi, Kun
    HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES, 2019, 9 (01)
  • [45] Data augmentation for aspect-based sentiment analysis
    Guangmin Li
    Hui Wang
    Yi Ding
    Kangan Zhou
    Xiaowei Yan
    International Journal of Machine Learning and Cybernetics, 2023, 14 : 125 - 133
  • [46] Aspect-Based Sentiment Analysis of Online Reviews for Business Intelligence
    Jain, Abha
    Bansal, Ankita
    Tomar, Siddharth
    INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGIES AND SYSTEMS APPROACH, 2022, 15 (03)
  • [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] 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
  • [49] 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
  • [50] Weakly Supervised Framework for Aspect-Based Sentiment Analysis on Students Reviews of MOOCs
    Kastrati, Zenun
    Imran, Ali Shariq
    Kurti, Arianit
    IEEE ACCESS, 2020, 8 : 106799 - 106810