Domain-Specific Named Entity Recognition in Hotel Reviews Using Large Language Models

Abstract

Extracting meaningful insights from large volumes of hotel review text remains a key challenge in natural language processing. Traditional methods often rely on manual feature design or limited rule-based systems. This study introduces an automated workflow that uses large language models (LLMs) to transform unstructured hotel reviews into structured, analytical data. The process integrates predefined prompts with the review texts to guide the model in identifying entities, classifying them into relevant aspects, and determining sentiment polarity. The approach effectively captures contextual information and produces highly interpretable, structured outputs. By leveraging prompt engineering and LLM inference, it reduces human intervention while maintaining analytical depth. The proposed workflow demonstrates the potential of LLM-driven automation to streamline sentiment and aspect extraction from usergenerated content, enabling scalable and data-driven insights in hospitality and related domains.