Published December 30, 2025 | Version CC-BY-NC-ND 4.0
Journal article Open

Context-Enriched Sentiment Analysis for Short Vietnamese Restaurant Reviews Using Large Language Models

Authors/Creators

  • 1. Department of Information Security, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam.

Contributors

  • 1. Department of Information Technology, Posts and Telecommunications Institute of Technology, A2, Hanoi, Vietnam.
  • 2. Department of Information Security, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam.

Description

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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Additional details

Identifiers

DOI
10.35940/ijitee.A1203.15011225
EISSN
2278-3075

Dates

Accepted
2025-12-15
Manuscript received on 26 November 2025 | First Revised Manuscript received on 03 December 2025 | Second Revised Manuscript received on 08 December 2025 | Manuscript Accepted on 15 December 2025 | Manuscript published on 30 December 2025.

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