Domain alignment of large language models for aspect based sentiment analysis of architectural heritage perception in Huishan Ancient Town
Year: 2026
Type: article
Abstract: Asian historic districts, exemplified by Huishan Ancient Town, face significant pressures from intensive tourism and commercialization, often leading to structural aging and a loss of authenticity. Conventional sentiment analysis tools frequently struggle with specialized architectural terminology, resulting in a superficial understanding of public perception. This study introduces a domain-specific alignment approach using a Qwen3-14B Large Language Model fine-tuned via Quantized Low-Rank Adaptation (QLoRA). By integrating heritage knowledge into the model’s training objective, we enhance its ability to interpret professional terms – such as “leaking windows” and “borrowed scenery” – within informal visitor narratives. Results demonstrate that the aligned LLM achieves a 78.72% F1-score in heritage value classification, effectively bridging the gap between vernacular language and professional conservation frameworks. This research provides a high-precision digital tool for monitoring sentiment and cultural integrity, offering a scientific basis for urban planners to balance commercial development with the preservation of tangible and intangible values in Asian historic environments.
Institution: Wuxi Taihu University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,165
OpenAlex ID: W7203821074