This study explores the use of generative language models for sentiment analysis of classical Chinese poetry, aiming to better understand emotional expression in literary texts. Using the FSPC dataset, we evaluate two models, Qwen-2.5 and LLaMA-3.1, under various prompting strategies. Initial experiments show that base models struggle with task-specific instructions. By applying different instruction tuning strategies with Low-Rank Adaptation (LoRA), we significantly enhance the models’ ability to follow task instructions and capture poetic sentiment, with LLaMA-3.1 achieving the best results (67.10% accuracy, 65.42% macro F1), demonstrate competitive performance against data-intensive, domain-adapted baselines. We further examine the effects of prompt language and multi-task learning, finding that English prompts outperform Chinese ones. These results highlight the promise of instruction-tuned generative models in sentiment analysis of classical Chinese poetry, and underscore the importance of prompt formulation in literary understanding tasks.