An Aspect-Based Review Analysis Using ChatGPT for the Exploration of Hotel Service Failures

Abstract

In this study, we employed ChatGPT, an advanced large language model, to analyze hotel reviews, focusing on aspect-based feedback to understand service failures in the hospitality industry. The shift from traditional feedback analysis methods to natural language processing (NLP) was initially hindered by the complexity and ambiguity of hotel review texts. However, the emergence of ChatGPT marks a significant breakthrough, offering enhanced accuracy and context-aware analysis. This study presents a novel approach to analyzing aspect-based hotel complaint reviews using ChatGPT. Employing a dataset from TripAdvisor, we methodically identified ten hotel attributes, establishing aspect–summarization pairs for each. Customized prompts facilitated ChatGPT’s efficient review summarization, emphasizing explicit keyword extraction for detailed analysis. A qualitative evaluation of ChatGPT’s outputs demonstrates its effectiveness in succinctly capturing crucial information, particularly through the explicitation of key terms relevant to each attribute. This study further delves into topic distributions across various hotel market segments (budget, midrange, and luxury), using explicit keyword analysis for the topic modeling of each hotel attribute. This comprehensive approach using ChatGPT for aspect-based summarization demonstrates a significant advancement in the way hotel reviews can be analyzed, offering deeper insights into customer experiences and perceptions.