Developing a Deep Learning-Based Sentiment Analysis System of Hotel Customer Reviews for Sustainable Tourism

Dilşad Erdoğan, Mehmet Kayakuş, Pinar Çelik Çaylak (), Nisa Ekşili, Georgiana Moiceanu (), Onder Kabas and Mirona Ana Maria Ichimov ()
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Dilşad Erdoğan: Department of Finance, Banking and Insurance, Korkuteli Vocational School, Akdeniz University, 07800 Antalya, Türkiye
Mehmet Kayakuş: Department of Management Information Systems, Faculty of Manavgat Social Sciences and Humanities, Akdeniz University, 07070 Antalya, Türkiye
Pinar Çelik Çaylak: Department of Tourism Management, Serik Faculty of Business Administration, Akdeniz University, 07058 Antalya, Türkiye
Nisa Ekşili: Department of Aviation Management, Faculty of Applied Sciences, Akdeniz University, 07058 Antalya, Türkiye
Georgiana Moiceanu: Department of Entrepreneurship and Management, Faculty of Entrepreneurship, Business Engineering and Management, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania
Onder Kabas: Department of Machine, Technical Science Vocational School, Akdeniz University, 07070 Antalya, Türkiye
Mirona Ana Maria Ichimov: Department of Entrepreneurship and Management, Faculty of Entrepreneurship, Business Engineering and Management, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania

Sustainability, 2025, vol. 17, issue 13, 1-30

Abstract: This study highlights the importance of managing and analyzing customer reviews to gain a competitive advantage and improve customer experience in the hospitality industry. In this context, a deep learning-based sentiment analysis system of hotel customer reviews is developed to evaluate service quality within the scope of sustainable tourism. The study analyzed 15,522 customer reviews of five-star hotels in Antalya using text mining, topic modelling, and deep learning-based sentiment analysis. The reviews were classified as positive, negative, or neutral. The findings show that Hotel HB2 has the highest performance, with an F1 score of 97.9%. Overall customer satisfaction is 91%, while emotional satisfaction stands at 77%. Key factors, such as cleanliness, food quality, and staff professionalism, were found to play a critical role in customer loyalty. Additionally, this study integrates sustainability-orientated themes by identifying customer feedback related to environmentally friendly practices and sustainable hotel operations. The results provide evidence that customer satisfaction is not only influenced by service quality but also by the perceived environmental and social responsibility of the hotel. Machine learning techniques have emerged as effective tools for analyzing large-scale customer reviews, offering valuable insights to rapidly and accurately capture customers’ emotions, expectations, and perceptions. As a comprehensive application of sentiment analysis and text mining, this research offers hotel managers a practical framework to enhance service quality, foster customer loyalty, and develop sustainability-orientated strategies. This study contributes to the literature by linking AI-driven sentiment analysis with sustainability practices in the tourism sector.

Keywords: tourism; hotel; customers; sentiment analysis; deep learning; text mining (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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