This study investigates the performance of a state-of-the-art Large Language Model(LLM) in classifying Korean neutral sentiment without additional fine-tuning and proposes an effective prompt design to improve neutral sentiment classification. To enhance the accuracy of neutral sentiment detection, we introduce a prompt based on Aspect-Based Sentiment Analysis(ABSA) and conduct a comparative evaluation using the proposed approach. The proposed prompt consists of a five-step procedure, including aspect identification and sentiment ratio–based classification, enabling more fine-grained sentiment reasoning. Experimental results demonstrate that the proposed prompt significantly improves classification accuracy when applied to the GPT-4-turbo model, thereby validating the effectiveness of prompt-based control for neutral sentiment analysis in Korean.