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.