A Generative Aspect Sentiment Triplet Extraction Approach Using Fine-Tuned LLMs

Abstract

Fine-grained analysis of guest satisfaction in the hospitality sector requires moving beyond document-level sentiment classification. It calls for structured extraction of attribute-specific evaluations from multilingual user-generated content. However, existing approaches have yet to address this methodological gap for the Central Asian hotel market. This study presents KAZ-LLM-ABSA, a generative framework for Aspect Sentiment Triplet Extraction applied to trilingual hotel reviews (Kazakh, English, and Russian) in Kazakhstan. The framework fine-tunes Qwen2.5-7B-Instruct via QLoRA on a purpose-built gold standard of 1726 expert-annotated triplets, formulating ASTE as an end-to-end sequence-to-sequence task to eliminate error propagation inherent to pipeline architectures. Benchmarked against six baselines spanning discriminative encoder models (mBERT-base, XLM-RoBERTa-base, BERT-SPC, LCF-BERT, RoBERTa-large) and a generative Seq2Seq model (GAS-mT5-base). KAZ-LLM-ABSA outperforms existing models across the reported metrics, demonstrating superior performance on the trilingual Kazakhstan hotel review corpus. These findings confirm the transferability of fine-tuned LLMs to low-resource multilingual hospitality data and offer practitioners a structured tool for aspect-level service quality diagnostics.