A GRAPH-BASED CLUSTERING AND LARGE LANGUAGE MODEL-ENHANCED ASPECT-BASED SENTIMENT ANALYSIS FRAMEWORK FOR MODELING PASSENGER SATISFACTION IN EUROPEAN CRUISE ITINERARIES
TL;DR
This study highlights the effectiveness of the GPT-4o mini large language model in extracting nuanced sentiment from informal online reviews and identifies the most and least satisfying cruise regions in Europe based on customer feedback, while providing an aspect×region knowledge map for decision support.
Abstract
Cruise tourism offers travelers the opportunity to visit multiple destinations while enjoying various onboard services. It has emerged as one of the fastest growing sectors in the tourism industry. Online reviews have become an essential resource for both travelers and cruise operators, with the rapid increase in user-generated content on online platforms. This study presents a novel framework for analyzing cruise passenger satisfaction across European itineraries by integrating transitive clustering, topic modeling, and Aspect Based Sentiment Analysis (ABSA). Cruises were grouped into four major geographic regions based on the ports they visited. Eight key aspects from online reviews were identified where three of them are itinerary related. The GPT-4o mini large language model, accessed through the OpenAI API, was used for fine-grained sentence-level aspect-based sentiment analysis. Results show that cruises in the Eastern Mediterranean received the highest satisfaction scores across itinerary related aspects while those in the Western European and Atlantic Coastal scored lowest. This study highlights the effectiveness of the GPT-4o mini large language model in extracting nuanced sentiment from informal online reviews. It also identifies the most and least satisfying cruise regions in Europe based on customer feedback, while providing an aspect×region knowledge map for decision support.