KnowRhet: A Framework for Joint Analysis of Rhetoric and Sentiment in Classical Vernacular Chinese Fiction

In classical vernacular Chinese fiction, inferring affective stance in the presence of rhetorical devices often depends on implicit semantics and contextual cues, which poses substantial challenges for joint pragmatic modeling with large language models (LLMs). To address these challenges, we propose KnowRhet, a unified framework for joint pragmatic modeling in classical vernacular Chinese fiction. Under a unified structured-output interface, KnowRhet integrates task-specific domain knowledge guidance, reasoning-process control, and LoRA-based parameter-efficient adaptation, thereby improving the ability of LLMs to jointly identify rhetorical devices and affective stance. Here, task-specific domain knowledge refers to interpretive knowledge operationalized through label definitions, category descriptions, and boundary constraints for the current literary analysis setting, rather than knowledge obtained from external retrieval. To evaluate the proposed framework, we construct a sentence-level benchmark based on Dream of the Red Chamber, comprising 2,769 manually annotated instances for multi-label rhetorical device prediction, emotion expression type classification, and sentiment polarity classification. Experimental results show that explicit label definitions, category descriptions, and boundary constraints yield the most consistent gains, improving Avg F1 from 58.53% to 75.31%. Structured reasoning exhibits clear task-dependent effects, while LoRA improves Qwen2.5-7B-Instruct from 57.05% to 75.39% Avg F1, approaching the prompt-only performance of DeepSeek-V3.2 (75.77%) under the same evaluation protocol. These findings suggest that, in the present benchmark setting, robust pragmatic modeling in classical vernacular Chinese fiction depends less on increasing reasoning complexity alone than on combining explicit task constraints with lightweight domain adaptation.