Context-Enriched Sentiment Analysis for Short Vietnamese Restaurant Reviews Using Large Language Models
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Abstract
Sentiment analysis of short text has posed a significant challenge in natural language processing, particularly for contextrich and low-resource languages such as Vietnamese. Usergenerated texts are usually brief; therefore, they do not explicitly express their sentiments. Consequently, traditional models struggle to process those reviews. This paper introduces a new approach that leverages the strengths of large language models to address the gap in context scarcity. The method works primarily in two ways: a) by feeding in structured metadata, such as restaurant name and location, directly into the model input, and b) using large language models to automatically generate likely contextual sentences so that short reviews become long informative statements. Results from comprehensive experiments carried out on a newly assembled Vietnamese food review dataset show improved sentiment analysis output based on this kind of context enrichment, beating several strong baselines, including the stateof-the-art monolingual PhoBERT model, particularly when it came to resolving semantic vagueness typical of ultra-short word reviews or even short reviews with implicit subjects. This work offers a strong, flexible approach to addressing the problem of missing context in low-resource languages. This will bring value to both the commercial world and academic study.
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H. T. M. Nguyen et al., “VLSP SHARED TASK: SENTIMENT ANALYSIS,” J. Comput. Sci. Cybern., vol. 34, no. 4, pp. 295–310, Jan. 2019, DOI: https://doi.org/10.15625/1813-9663/34/4/13160.
T. K. Tran and T. T. Phan, “Capturing Contextual Factors in Sentiment Classification: An Ensemble Approach,” IEEE Access, vol. 8, pp. 116856–116865, 2020, DOI: http://doi.org/10.1109/ACCESS.2020.3004180.
D. Q. Nguyen and A. Tuan Nguyen, “PhoBERT: Pre-trained language models for Vietnamese,” in Findings of the Association for Computational Linguistics: EMNLP 2020, Online: Association for Computational Linguistics, 2020, pp. 1037–1042. DOI: http://doi.org/10.18653/v1/2020.findings-emnlp.92.
K. T. Tran, M. H. Dinh, T. N. N. Tran, V. X. Nguyen, and T. T. B. Thi, “Exploring the potential of graph neural networks for Vietnamese sentiment analysis,” HUFLIT Journal of Science, vol. 9, no. 2, pp. 11–11, 2025. url: https://hjs.huflit.edu.vn/index.php/hjs/article/view/266L.
Mei et al., “A Survey of Context Engineering for Large Language Models,” 2025, arXiv. DOI: http://doi.org/10.48550/ARXIV.2507.13334.
H. Q. V. Vo and K. Yamamoto, “VietSentiLex: a sentiment dictionary that considers the polarity of ambiguous sentiment words,” in Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation, 2018. url: http://doi.org/https://aclanthology.org/Y18-1081.pdf
T. Kim Phung, N. A Te, and T. Thi Thu Ha, “A machine learning approach for opinion mining online customer reviews,” in 2021 21st ACIS International Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD-Winter), Ho Chi Minh City, Vietnam: IEEE, Jan. 2021, pp. 243–246. DOI: http://doi.org/10.1109/SNPDWinter52325.2021.00059.
T. Nguyen-Thanh and G. T. C. Tran, “Vietnamese Sentiment Analysis for Hotel Review based on Overfitting Training and Ensemble Learning,” in Proceedings of the Tenth International Symposium on Information and Communication Technology - SoICT 2019, Hanoi, Ha Long Bay, Viet Nam: ACM Press, 2019, pp. 147–153.
DOI: http://doi.org/10.1145/3368926.3369675.
E. B. Ramezani, “Sentiment analysis applications using deep learning advancements in social networks: A systematic review,” Neurocomputing, vol. 634, p. 129862, June 2025, DOI: http://doi.org/10.1016/j.neucom.2025.129862.
L. S. Le, D. V. Thin, N. L.-T. Nguyen, and S. Q. Trinh, “A Multi-filter BiLSTM-CNN Architecture for Vietnamese Sentiment Analysis,” in Advances in Computational Collective Intelligence, vol. 1287, M. Hernes, K. Wojtkiewicz, and E. Szczerbicki, Eds., in Communications in Computer and Information Science, vol. 1287., Cham: Springer International Publishing, 2020, pp. 752–763. DOI: http://doi.org/10.1007/978-3-030-63119-2_61.
D. Van Thin, D. N. Hao, and N. L.-T. Nguyen, “Vietnamese Sentiment Analysis: An Overview and Comparative Study of Fine-tuning Pretrained Language Models,” ACM Trans. Asian Low-Resour. Lang. Inf. Process., vol. 22, no. 6, pp. 1–27, June 2023, DOI: http://doi.org/10.1145/3589131.
D. Q. Nguyen, L. T. Nguyen, C. Tran, D. N. Nguyen, D. Phung, and H. Bui, “PhoGPT: Generative Pre-training for Vietnamese,” Mar. 22, 2024, arXiv: arXiv:2311.02945. DOI: http://doi.org/10.48550/arXiv.2311.02945.
G. Team et al., “Gemma: Open Models Based on Gemini Research and Technology,” Apr. 16, 2024, arXiv: arXiv:2403.08295. DOI: http://doi.org/10.48550/arXiv.2403.08295.
OpenAI et al., “gpt-oss-120b & gpt-oss-20b Model Card,” Aug. 08, 2025, arXiv: arXiv:2508.10925. DOI: http://doi.org/10.48550/arXiv.2508.10925.
H. Touvron et al., “LLaMA: Open and Efficient Foundation Language Models,” Feb. 27, 2023, arXiv: arXiv:2302.13971. DOI: http://doi.org/10.48550/arXiv.2302.13971
A. Q. Jiang et al., “Mistral 7B,” oct. 10, 2023, arXiv:2310.06825. Doi: http://doi.org/10.48550/arXiv.2310.06825.
F. Man et al., “Context-Aware Sentiment Forecasting via LLM-based Multi-Perspective Role-Playing Agents,” May 30, 2025, arXiv: arXiv:2505.24331. DOI: http://doi.org/10.48550/arXiv.2505.24331.
D. Van Thin, D. N. Hao, and N. L.-T. Nguyen, “Prompt Engineering with Large Language Models for Vietnamese Sentiment Classification,” in Proceedings of the 38th Pacific Asia Conference on Language, Information and Computation, 2024, pp. 181–192. url: http://doi.org/https://aclanthology.org/2024.paclic-1.17/