Abstract
In recent years, numerous studies have been conducted on the phenomenon of dark tourism. This study seeks to understand what motivates people to visit dark tourism sites such as the Chernobyl exclusion zone by applying an automated text analytics approach. Tripadvisor was chosen as a source for data collection as tourists are increasingly sharing their experiences and leave feedback online. Several natural language processing methods, such as topic modelling (LDA) and sentiment analysis, were applied to extract the primary motivators behind a visit to Chernobyl. Topic modelling results present five main topics discussed by tourists. Based on the results, the main motivational factors are discussed in detail. Furthermore, the total sentiment score shows a positive perception of the dark tourism destination. This study follows an interdisciplinary research approach applying innovative data analytics methods to investigate dark tourism through social media. By implementing NLP methods, this study reveals tourists’ perceptions from online reviews, which are not easy to discover by traditional approaches. Moreover, the results provide guidelines to tourism managers in monitoring new trends in tourism, understanding tourists’ needs and wishes, and evaluating the quality of products or services.
About the author
Aleksandra Kleshcheva is a graduate of Salzburg University of Applied Sciences, where she studied innovation and management in tourism. Her research focuses on using automated text analysis techniques to discover customer experience from online reviews. She is interested in the application of data science in tourism to find new ways of exploring image from user-generated content.
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