Exploring the Power of Large Language Models for V ietnamese Implitcit Sentiment Analysis (2025)

Abstract: We present the first benchmark for implicit sentiment analysis (ISA) in Vietnamese, aimed at evaluating large language models (LLMs) on their ability to interpret implicit sentiment accompanied by ViISA, a dataset specifically constructed for this task. We assess a variety of open-source and close-source LLMs using state-of-the-art (SOTA) prompting techniques. While LLMs achieve strong recall, they often misclassify implicit cues such as sarcasm and exaggeration, resulting in low precision. Through detailed error analysis, we highlight key challenges and suggest improvements to Chain-of-Thought prompting via more contextually aligned demonstrations.

## introduction implicit sentiment analysis focuses on detecting sentiments conveyed indirectly through context, speaker intent, or pragmatic cues, rather than explicit polar words @xcite . while more challenging than explicit sentiment analysis (esa), isa has benefited from recent advances in llms, which offer stronger reasoning capabilities @xcite . however, most isa research has centered on english or other high-resource languages (duan and @xcite , while vietnamese-despite being widely spoken-remains under-resourced. prior vietnamese sentiment analysis work has largely focused on esa @xcite , with no dedicated isa datasets. given the syntactic and pragmatic distinctions of vietnamese, a focused isa study is both necessary and timely. to address this gap, our work focuses on evaluating the effectiveness of large language models for the task of implicit sentiment analysis in vietnamese using state-of-the-art prompting strategies. our key contributions are threefold: (1) we conduct a comprehensive evaluation of both open-source and proprietary llms on sentencelevel isa in vietnamese; (2) we propose several directions to improve llm performance on this challenging task, particularly in handling rhetorical and pragmatic features; and (3) we also release viisa, a benchmark test set specifically designed for evaluating llms on isa in vietnamese. implicit sentiment analysis isa is more complex, requiring inference beyond surface cues. chain-of-thought prompting improves llm reasoning for subtle sentiment @xcite , while other methods use coherence cues @xcite or combine discourse features with structured prompts @xcite . these approaches stress the importance of reasoning for effective isa. ## dataset construction viisa is a sentence-level dataset for implicit sentiment analysis in vietnamese, containing only positive and negative labels. based on prior studies on indirect language in vietnamese (nguyen, 2020; le, 2015), we developed annotation guidelines to capture key patterns of implicit sentiment. (1) contradiction, (2) exaggeration or understatement, (3) wordplay or ambiguity, (4) rhetorical or sarcastic expressions, (5) tonal irony, and (6) contextual paradoxes. 3. annotation: annotators used example-driven guidelines focusing on implicit sentiment traits. 4. disambiguation: difficult cases were resolved collaboratively and used to refine the guideline. we constructed the dataset using a two-stage process: first, a prompting-based method with llms was applied to extract vietnamese implicit sentiment sentences from an existing dataset; second, human annotators filtered these sentences using predefined guidelines. this approach reduces annotation effort while maintaining linguistic quality. the filtering prompt was designed based on few-shot chain-of-thought prompting @xcite , and includes three key components: for this filtering task, we used gpt-4o. as the source data, we adopted the vlsp 2016 sentiment analysis dataset @xcite , which was released at the vlsp 2016 workshop. this dataset consists of vietnamese technological product review sentences collected from popular online platforms such as tinhte.vn, vnexpress.net, and facebook. the full prompt template is provided in the appendix a. we conducted annotation over 10 rounds, with inter-annotator agreement tracked for consistency (figure1). the label imbalance ( ## conclusion this paper introduces a benchmark for evaluating large language models on implicit sentiment analysis in vietnamese, with a focus on rhetorical and pragmatic aspects such as sarcasm, exaggeration, and contextual irony. we explore several state-of-the-art prompting strategies and observe that while llms achieve relatively high recall, their precision remains low-mainly due to difficulties in accurately detecting sentimentbearing targets and interpreting indirect or ambiguous expressions. these challenges suggest that existing prompting methods may not fully account for the linguistic and cultural subtleties of vietnamese, highlighting the need for more targeted strategies tailored to the nature of implicit sentiment. in future work, we aim to improve model performance by constructing higher-quality in-context examples and designing more structured, reasoning-guided prompts to enhance both accuracy and generalization.