Understanding Visitor Experience in the Hospitality and Tourism Sector Through Text Analytics: A Case Study from a Greek Tourism Destination

In the digital age, online reviews have become essential sources of customer feedback in the hospitality and tourism sectors. Understanding and analyzing this unstructured textual data can provide valuable insights into visitor satisfaction, service quality perceptions, and areas for operational improvement. This study presents a case study focused on a tourism destination in Greece, utilizing natural language processing (NLP) and sentiment analysis to examine visitor experiences through publicly available comments and reviews. It involves collecting user-generated content from major travel platforms (9,304 Google Maps reviews and 1,184 Booking.com reviews collected between January 2024 and June 2025), covering a variety of services, including hotels, restaurants, and attractions. A structured text mining approach was used to identify recurring themes through keyword-based thematic extraction, analyze sentiment trends over time, and determine the most influential factors shaping visitor satisfaction. Visualization tools were used to present key findings in an intuitive format for hospitality managers and local tourism stakeholders. This study demonstrates how transforming unstructured customer feedback into structured knowledge can support data-driven strategic decision-making in the hospitality and destination management sectors. Additionally, the case highlights the practical challenges in working with noisy, multilingual, and context-dependent text data. By leveraging text analytics, the study shows the value of online feedback as a real-time, low-cost resource for service improvement, contributes to the growing field of experience analytics, and offers a replicable framework for analyzing visitor feedback in other tourism destinations.