LLMs for Sentiment Analysis in Tourism Reviews: A Resource-Efficient Approach

This paper investigates the utility of open source Large Language Models for sentiment analysis in tourism reviews, with particular focus on the hospitality sector. By harnessing the power of Large Language Models and zero-shot classification techniques, we propose a resource-efficient solution that enables firms to analyse sentiments and extract keywords from reviews without the need for extensive model customisation. Through a comprehensive analysis of various open source models, experimentation, and validation on real-world tourism datasets, we demonstrate the viability and effectiveness of our approach. Our findings highlight the potential of these models as accessible tools for enhancing decision-making processes in the tourism sector, enabling firms in the hospitality domain to leverage cutting-edge technology for competitive advantage.