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A Systematic Literature Review on Vietnamese Aspect-based Sentiment Analysis

Published: 24 August 2023 Publication History

Abstract

Aspect-based sentiment analysis (ABSA) is one of the principal tasks in the automatic deep understanding of texts, widely applied in a broad range of real-world applications. Many studies have been performed on different tasks and datasets for other languages (e.g., English, Chinese) to address this topic. For Vietnamese language, this topic has been attracting considerable interest in recent years. However, we found that many studies tend to repeat the research instead of inheriting and extending the previous works. Moreover, previous studies’ methods of comparison or evaluation metrics have not shown consistency and connection. This might restrict the development of future studies on this research topic. To the best of our knowledge, no research has been conducted to overview the existing studies for the ABSA research in Vietnamese language. The primary objective of this study is to provide a systematic and comprehensive review of the current Vietnamese ABSA research. More specifically, we analyze the early approaches, evaluation metrics, and available published benchmark datasets used in the Vietnamese ABSA task. We also discuss the challenge and recommend potential future directions for Vietnamese ABSA. This work is expected to provide readers with a wealth of knowledge, the research gap, and the challenges in the Vietnamese ABSA field.
Appendix

A Acronyms

This section provides all acronyms used in the whole article.
AcronymMeaningAcronymMeaning
ABSAAspect-based sentiment analysisACSDAspect Category Sentiment Detection
ATEAspect Term ExtractionASQPAspect Sentiment Quad Prediction
ACDAspect Category DetectionTF-IDFTerm Frequency–Inverse Document Frequency
OTEOpinion Term ExtractVLSPVietnamese Language and Speech Processing
ASCAspect Sentiment ClassificationSMOTESynthetic Minority Oversampling TEchnique
AOPEAspect-Opinion Pair ExtractionSVMSupport Vector Machine
ATSAAspect-Term Sentiment AnalysisCNNConvolution Neural Network
ACSAAspect-Category Sentiment AnalysisLSTMLong short-term Memory
ASTEAspect Sentiment Triplet ExtractionBERTBidirectional Encoder Representations from Transformers
SOTAState of the artMLPMulti-layer Perceptron
GRUGated Recurrent UnitBiLSTMBidirectional Long short-term Memory
NLPNatural Language ProcessingCRFConditional Random Field
BIOBegin-Inside-OutsideRNNRecurrent Neural Network

References

[1]
Mark Alves. 2006. Linguistic research on the origins of the Vietnamese language: An overview. J. Viet. Stud. 1, 1-2 (2006), 104–130.
[2]
Xiaoyi Bao, Wang Zhongqing, Xiaotong Jiang, Rong Xiao, and Shoushan Li. 2022. Aspect-based sentiment analysis with opinion tree generation. In Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI-’), Lud De Raedt (Ed.). International Joint Conferences on Artificial Intelligence Organization, 4044–4050. DOI:Main Track
[3]
Rajae Bensoltane and Taher Zaki. 2022. Aspect-based sentiment analysis: An overview in the use of Arabic language. Artif. Intell. Rev. 56 (2022), 2325–2363. DOI:
[4]
Gianni Brauwers and Flavius Frasincar. 2022. A survey on aspect-based sentiment classification. ACM Comput. Surv. 55, 4, Article 65 (Nov.2022), 37 pages. DOI:
[5]
Thanh Hung Bui and Quoc Binh Nguyen. 2020. Aspect-based sentiment analysis using hybrid deep learning approach CNN-LSTM. In Proceedings of the 13th National Conference on Fundamental & Applied Information Technology Research. Publishing House of Natural Science and Technology, 456–460. DOI:
[6]
Siva Uday Sampreeth Chebolu, Franck Dernoncourt, Nedim Lipka, and Thamar Solorio. 2021. Exploring conditional text generation for aspect-based sentiment analysis. In Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation. Association for Computational Lingustics, 119–129. Retrieved from https://aclanthology.org/2021.paclic-1.13
[7]
David Z. Chen, Adam Faulkner, and Sahil Badyal. 2022. Unsupervised data augmentation for aspect based sentiment analysis. In Proceedings of the 29th International Conference on Computational Linguistics. International Committee on Computational Linguistics, 6746–6751. Retrieved from https://aclanthology.org/2022.coling-1.586
[8]
Minh-Son Cong, Tuan-Minh Dam, Anh-My Phuong, Thi-Quyen Nguyen, Duy-Cat Can, Hoang-Quynh Le, Long Hoang, and Mai-Vu Tran. 2021. THANOS: The aspect classification model for imbalanced Vietnamese E-commerce review data. In Proceedings of the International Conference on Asian Language Processing (IALP’21). IEEE, 352–357. DOI:
[9]
Hoang-Quan Dang, Duc-Duy-Anh Nguyen, and Trong-Hop Do. 2022. Multi-task solution for aspect category sentiment analysis on Vietnamese datasets. In Proceedings of the IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom’22). IEEE, 404–409. DOI:
[10]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, 4171–4186. DOI:
[11]
Ting-Wei Hsu, Chung-Chi Chen, Hen-Hsen Huang, and Hsin-Hsi Chen. 2021. Semantics-preserved data augmentation for aspect-based sentiment analysis. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 4417–4422. DOI:
[12]
Bui Thanh Hung, Vijay Bhaskar Semwal, Neha Gaud, and Vishwanth Bijalwan. 2021. Hybrid deep learning approach for aspect detection on reviews. In Proceedings of Integrated Intelligence Enable Networks and Computing, Krishan Kant Singh Mer, Vijay Bhaskar Semwal, Vishwanath Bijalwan, and Rubén González Crespo (Eds.). Springer, 991–999.
[13]
Quang Thắng Đinh, Hồng Phương Lê, Thị Minh Huyền Nguyễn, Cẩm Tú Nguyễn, Mathias Rossignol, and Xuân Lương Vũ. 2008. Word segmentation of Vietnamese texts: A comparison of approaches. In Proceedings of the 6th International Conference on Language Resources and Evaluation (LREC’08). European Language Resources Association (ELRA). Retrieved from http://www.lrec-conf.org/proceedings/lrec2008/pdf/493_paper.pdf
[14]
Barbara Kitchenham. 2004. Procedures for performing systematic reviews. Keele, UK, Keele University 33, 2004 (2004), 1–26. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=29890a936639862f45cb9a987dd599dce9759bf5
[15]
Yash Kumar Lal, Vaibhav Kumar, Mrinal Dhar, Manish Shrivastava, and Philipp Koehn. 2019. De-mixing sentiment from code-mixed text. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. Association for Computational Linguistics, 371–377. DOI:
[16]
An Pha Le, Tran Vu Pham, Thanh-Van Le, Duy V. Huynh, and Dat Ngo. 2022. Robust hierarchical model for joint span detection and aspect-based sentiment analysis in Vietnamese. In Proceedings of the 9th NAFOSTED Conference on Information and Computer Science (NICS’22). IEEE, 35–40. DOI:
[17]
Bao Hoang Le, Hoang Minh Nguyen, Nhi Kieu-Phuong Nguyen, and Binh T. Nguyen. 2022. A new approach for Vietnamese aspect-based sentiment analysis. In Proceedings of the 14th International Conference on Knowledge and Systems Engineering (KSE’22). IEEE, 1–6. DOI:
[18]
Hai Son Le, Thanh Van Le, and Tran Vu Pham. 2015. Aspect analysis for opinion mining of Vietnamese text. In Proceedings of the International Conference on Advanced Computing and Applications (ACOMP’15). IEEE, 118–123. DOI:
[19]
Binh Le-Minh, Thi-Phuong Le, Khanh-Hung Tran, Khanh-Huyen Bui, Hoang-Quynh Le, Duy-Cat Can, Hung Nguyen Chung Thanh, and Mai-Vu Tran. 2021. Aspect-based sentiment analysis using mini-window locating attention for Vietnamese E-commerce reviews. In Proceedings of the 13th International Conference on Knowledge and Systems Engineering (KSE’21). IEEE, 1–4.
[20]
Bing Liu. 2012. Sentiment analysis and opinion mining. Synthes. Lect. Hum. Lang. Technol. 5, 1 (2012), 1–167.
[21]
Luong Luc Phan, Phuc Huynh Pham, Kim Thi-Thanh Nguyen, Sieu Khai Huynh, Tham Thi Nguyen, Luan Thanh Nguyen, Tin Van Huynh, and Kiet Van Nguyen. 2021. SA2SL: From aspect-based sentiment analysis to social listening system for business intelligence. In Knowledge Science, Engineering and Management, Han Qiu, Cheng Zhang, Zongming Fei, Meikang Qiu, and Sun-Yuan Kung (Eds.). Springer International Publishing, Cham, 647–658.
[22]
Ngoc C. Lê, Nguyen The Lam, Son Hong Nguyen, and Duc Thanh Nguyen. 2020. On Vietnamese sentiment analysis: A transfer learning method. In Proceedings of the RIVF International Conference on Computing and Communication Technologies (RIVF’20). IEEE, 1–5. DOI:
[23]
Long Mai and Bac Le. 2018. Aspect-based sentiment analysis of Vietnamese texts with deep learning. In Intelligent Information and Database Systems, Ngoc Thanh Nguyen, Duong Hung Hoang, Tzung-Pei Hong, Hoang Pham, and Bogdan Trawiński (Eds.). Springer International Publishing, Cham, 149–158.
[24]
Long Mai and Bac Le. 2021. Joint sentence and aspect-level sentiment analysis of product comments. Ann. Oper. Res. 300, 2 (2021), 493–513.
[25]
Yue Mao, Yi Shen, Chao Yu, and Longjun Cai. 2021. A joint training dual-MRC framework for aspect based sentiment analysis. Proc. AAAI Conf. Artif. Intell. 35, 15 (May2021), 13543–13551. DOI:
[26]
Ambreen Nazir, Yuan Rao, Lianwei Wu, and Ling Sun. 2022. Issues and challenges of aspect-based sentiment analysis: A comprehensive survey. IEEE Trans. Affect. Comput. 13, 2 (2022), 845–863. DOI:
[27]
Duy Nguyen Ngoc, Tuoi Phan Thi, and Phuc Do. 2021. Preprocessing improves CNN and LSTM in aspect-based sentiment analysis for Vietnamese. In Proceedings of 5th International Congress on Information and Communication Technology, Xin-She Yang, R. Simon Sherratt, Nilanjan Dey, and Amit Joshi (Eds.). Springer, 175–185.
[28]
Dat Quoc Nguyen and Anh Tuan Nguyen. 2020. PhoBERT: Pre-trained language models for Vietnamese. In Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, 1037–1042. DOI:
[29]
Huyen T. M. Nguyen, Hung V. Nguyen, Quyen T. Ngo, Luong X. Vu, Vu Mai Tran, Bach X. Ngo, and Cuong A. Le. 2018. VLSP shared task: Sentiment analysis. J. Comput. Sci. Cyber. 34, 4 (2018), 295–310.
[30]
Minh-Hao Nguyen, Tri Minh Nguyen, Dang Van Thin, and Ngan Luu-Thuy Nguyen. 2019. A corpus for aspect-based sentiment analysis in Vietnamese. In Proceedings of the 11th International Conference on Knowledge and Systems Engineering (KSE’19). IEEE, 1–5. DOI:
[31]
Ngoc Duy Nguyen, Tuoi Phan Thi, and Phuc Do. 2019. A data preprocessing method to classify and summarize aspect-based opinions using deep learning. In Intelligent Information and Database Systems, Ngoc Thanh Nguyen, Ford Lumban Gaol, Tzung-Pei Hong, and Bogdan Trawiński (Eds.). Springer International Publishing, Cham, 115–127.
[32]
Ngoc-Tu Nguyen, Trong-Dat Nguyen, Duy-Cat Can, Mai-Vu Tran, Ha Luu Manh, and Hoang-Quynh Le. 2021. Attention-based deep learning model for aspect classification on Vietnamese E-commerce data. In Proceedings of the 13th International Conference on Knowledge and Systems Engineering (KSE’21). IEEE, 1–6. DOI:
[33]
Ruba Obiedat, Duha Al-Darras, Esra Alzaghoul, and Osama Harfoushi. 2021. Arabic aspect-based sentiment analysis: A systematic literature review. IEEE Access 9 (2021), 152628–152645. DOI:
[34]
Bo Pang and Lillian Lee. 2008. Opinion mining and sentiment analysis. Foundations and Trends® in Information Retrieval 2, 1–2 (2008), 1–135.
[35]
Luan-Nghia Pham, Viet-Hong Tran, and Vinh-Van Nguyen. 2013. Vietnamese text accent restoration with statistical machine translation. In Proceedings of the 27th Pacific Asia Conference on Language, Information, and Computation (PACLIC’13). Department of English, National Chengchi University, 423–429. Retrieved from https://aclanthology.org/Y13-1044
[36]
Long Phan, Hieu Tran, Hieu Nguyen, and Trieu H. Trinh. 2022. ViT5: Pretrained text-to-text transformer for Vietnamese language generation. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop. Association for Computational Linguistics, 136–142. DOI:
[37]
Maria Pontiki, Dimitris Galanis, Haris Papageorgiou, Ion Androutsopoulos, Suresh Manandhar, Mohammad Al-Smadi, Mahmoud Al-Ayyoub, Yanyan Zhao, Bing Qin, Orphée De Clercq, Véronique Hoste, Marianna Apidianaki, Xavier Tannier, Natalia Loukachevitch, Evgeniy Kotelnikov, Nuria Bel, Salud María Jiménez-Zafra, and Gülşen Eryiğit. 2016. SemEval-2016 task 5: Aspect based sentiment analysis. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval’16). Association for Computational Linguistics, 19–30. DOI:
[38]
Ton Nu Thi Sau, Do Phuoc Sang, and Pham Thi Thu Trang. 2021. Aspect-based sentiment analysis on student’s feedback in Vietnamese. TNU J. Sci. Technol. 226, 18 (2021), 48–55.
[39]
Samson Tan and Shafiq Joty. 2021. Code-mixing on Sesame Street: Dawn of the adversarial polyglots. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, 3596–3616. DOI:
[40]
Kim Nguyen Thi Thanh, Sieu Huynh Khai, Phuc Pham Huynh, Luong Phan Luc, Duc-Vu Nguyen, and Kiet Nguyen Van. 2021. Span detection for aspect-based sentiment analysis in Vietnamese. In Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation. Association for Computational Lingustics, 318–328.
[41]
Dang Van Thin, Duc-Vu Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen, and Anh Hoang-Tu Nguyen. 2020. Multi-task learning for aspect and polarity recognition on Vietnamese datasets. In Computational Linguistics, Le-Minh Nguyen, Xuan-Hieu Phan, Kôiti Hasida, and Satoshi Tojo (Eds.). Springer, 169–180.
[42]
Dang Van Thin, Vu Duc Nguyen, Kiet Van Nguyen, and Ngan Luu-Thuy Nguyen. 2018. Deep learning for aspect detection on Vietnamese reviews. In Proceedings of the 5th NAFOSTED Conference on Information and Computer Science (NICS’18). IEEE, 104–109. DOI:
[43]
Van Dang Thin, Duong Ngoc Hao, and Ngan Luu-Thuy Nguyen. 2022. An effective contextual language ensemble model for Vietnamese aspect-based sentiment analysis. In Proceedings of the 9th NAFOSTED Conference on Information and Computer Science (NICS’22). IEEE, 30–34. DOI:
[44]
Van Dang Thin, Lac Si Le, Hao Minh Nguyen, and Ngan Luu-Thuy Nguyen. 2022. A joint multi-task architecture for document-level aspect-based sentiment analysis in Vietnamese. Int. J. Mach. Learn. Comput. 12, 4 (2022), 126–135.
[45]
Van Dang Thin, Ngan Luu-Thuy Nguyen, Tri Minh Truong, Lac Si Le, and Duy Tin Vo. 2021. Two new large corpora for Vietnamese aspect-based sentiment analysis at sentence level. ACM Trans. Asian Low-Resour. Lang. Inf. Process. 20, 4, Article 62 (May2021), 22 pages. DOI:
[46]
Van Dang Thin, Vu Duc Nguyen, Nguyen Van Kiet, and Nguyen Luu Thuy Ngan. 2018. A transformation method for aspect-based sentiment analysis. J. Comput. Sci. Cyber. 34, 4 (2018), 323–333.
[47]
Nguyen Thi Thanh Thuy, Ngo Xuan Bach, and Tu Minh Phuong. 2018. Cross-language aspect extraction for opinion mining. In Proceedings of the 10th International Conference on Knowledge and Systems Engineering (KSE’18). IEEE, 67–72. DOI:
[48]
Nguyen Thi Thanh Thuy, Ngo Xuan Bach, and Tu Minh Phuong. 2020. Leveraging foreign language labeled data for aspect-based opinion mining. In Proceedings of the RIVF International Conference on Computing and Communication Technologies (RIVF’20). IEEE, 1–6. DOI:
[49]
Nguyen Luong Tran, Duong Minh Le, and Dat Quoc Nguyen. 2022. BARTpho: Pre-trained sequence-to-sequence models for Vietnamese. In Proceedings of the 23rd Annual Conference of the International Speech Communication Association. International Speech Communication Association.
[50]
Oanh Thi Tran and Viet The Bui. 2020. A BERT-based hierarchical model for Vietnamese aspect based sentiment analysis. In Proceedings of the 12th International Conference on Knowledge and Systems Engineering (KSE’20). IEEE, 269–274. DOI:
[51]
Quang-Linh Tran, Phan Thanh Dat Le, and Trong-Hop Do. 2022. Aspect-based sentiment analysis for Vietnamese reviews about beauty product on E-commerce websites. In Proceedings of the 36th Pacific Asia Conference on Language, Information and Computation. 767–776. Retrieved from https://aclanthology.org/2022.paclic-1.84
[52]
Tri Cong-Toan Tran, Thien Phu Nguyen, and Thanh-Van Le. 2021. HSUM-HC: Integrating BERT-based hidden aggregation to hierarchical classifier for Vietnamese aspect-based sentiment analysis. In Proceedings of the 8th NAFOSTED Conference on Information and Computer Science (NICS’21). IEEE, 284–289. DOI:
[53]
Dang Van Thin, Duong Ngoc Hao, Vu Xuan Hoang, and Ngan Luu-Thuy Nguyen. 2022. Investigating monolingual and multilingual BERT models for Vietnamese aspect category detection. In Proceedings of the RIVF International Conference on Computing and Communication Technologies (RIVF’22). IEEE, 130–135. DOI:
[54]
Yifei Yang and Hai Zhao. 2022. Aspect-based sentiment analysis as machine reading comprehension. In Proceedings of the 29th International Conference on Computational Linguistics. International Committee on Computational Linguistics, 2461–2471. Retrieved from https://aclanthology.org/2022.coling-1.217
[55]
Wenxuan Zhang, Yang Deng, Xin Li, Yifei Yuan, Lidong Bing, and Wai Lam. 2021. Aspect sentiment quad prediction as paraphrase generation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 9209–9219. DOI:
[56]
Wenxuan Zhang, Xin Li, Yang Deng, Lidong Bing, and Wai Lam. 2022. A Survey on Aspect-based Sentiment Analysis: Tasks, Methods, and Challenges. DOI:

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    cover image ACM Transactions on Asian and Low-Resource Language Information Processing
    ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 22, Issue 8
    August 2023
    373 pages
    ISSN:2375-4699
    EISSN:2375-4702
    DOI:10.1145/3615980
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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 24 August 2023
    Online AM: 19 July 2023
    Accepted: 13 July 2023
    Revised: 22 May 2023
    Received: 06 February 2023
    Published in TALLIP Volume 22, Issue 8

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    Author Tags

    1. Aspect-based sentiment analysis
    2. systematic literature review
    3. methods
    4. datasets
    5. Vietnamese language

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    • (2024)Predictive model for customer satisfaction analytics in E-commerce sector using machine learning and deep learningInternational Journal of Information Management Data Insights10.1016/j.jjimei.2024.1002954:2(100295)Online publication date: Nov-2024

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