Discovering sentiment insights: streamlining tourism review analysis with Large Language Models

Abstract

With digital technology increasingly shaping the tourism industry, understanding customer sentiment and identifying key themes in reviews is crucial for enhancing service quality. However, traditional sentiment analysis and keyword extraction models typically demand significant time, computational resources, and labelled data for training. In this paper, we explore how Large Language Models (LLMs) can be leveraged to automatically classify reviews as positive or negative and extract relevant keywords without the need for dedicated training. Additionally, we frame the keyword extraction task as a tool to assist human users in comprehending and interpreting review content, especially in scenarios where ground truth labels for keywords are unavailable. To evaluate our approach, we conduct an experimental analysis using several datasets of tourism reviews and various LLMs. Our results demonstrate the reliability of LLMs as zero-shot classifiers for sentiment analysis and showcase the efficacy of the approach in extracting meaningful keywords from reviews, providing valuable insights into customer sentiments and preferences. Overall, this research contributes to the intersection of information technology and tourism by presenting a practical solution for sentiment analysis and keyword extraction in tourism reviews, leveraging the capabilities of LLMs as versatile tools for enhancing decision-making processes in the tourism industry.