DCASAM: advancing aspect-based sentiment analysis through a deep context-aware sentiment analysis model-Reference-Cited by-同舟云学术

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DCASAM: advancing aspect-based sentiment analysis through a deep context-aware sentiment analysis model

Published:2024-08-10 Issue: Volume: Page:
ISSN:2199-4536
Container-title:Complex & Intelligent Systems
language:en
Short-container-title:Complex Intell. Syst.

Author:

Jiang Xiangkui^ORCID,Ren Binglong,Wu Qing,Wang Wuwei,Li Hong

Funder

Innovative Research Group Project of the National Natural Science Foundation of China

Shaanxi Youth Talent Lifting Plan of Shaanxi Association for Science and Technology

Key Research and Development Program of Shaanxi Province of China

Natural science Basic research project of Shaanxi Province

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s40747-024-01570-5.pdf

Reference45 articles.

1. Chvanova MS, Khramova MV, Pitsik EN (2017) Investigation of internet influence on users social needs. In: 2017 International conference "Quality management,transport and information security, information technologies" (IT &QM &IS), pp 652–657. https://doi.org/10.1109/ITMQIS.2017.8085908

2. Nazir A, Rao Y, Wu L, Sun L (2020) Issues and challenges of aspect-based sentiment analysis: a comprehensive survey. IEEE Trans Affect Comput 13(2):845–863

3. Wankhade M, Rao ACS, Kulkarni C (2022) A survey on sentiment analysis methods, applications, and challenges. Artif Intell Rev 55(7):5731–5780

4. Taboada M, Brooke J, Tofiloski M, Voll K, Stede M (2011) Lexicon-based methods for sentiment analysis. Comput Linguist 37(2):267–307

5. Al-Ghuribi SM, Noah SAM, Tiun S (2020) Unsupervised semantic approach of aspect-based sentiment analysis for large-scale user reviews. IEEE Access 8:218592–218613

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