Sentiment and Topic Modelling Analysis of Museum Reviews in the Context of Traveller Types: The Case of Kazakhstan

Year 2025, Volume: 11 Issue: 1, 51 - 72, 30.07.2025

Egemen Güneş Tükenmez , Kemal Kantarcı , Alper Kürşat Uysal , Nurzhan Abdirazakhov , Turganbai Abdrassilov Kurmanbaevich

https://doi.org/10.26650/jot.2025.10.1.1562642

Abstract

Kazakhstan stands out as one of the key destinations in Central Asia, with a unique and diverse range of cultural and historical resources. Within these resources, museums are vital institutions where the region’s rich heritage is displayed and shared with the public, fostering a deeper understanding of its cultural identity. In this context, the present study aims to conduct a comprehensive analysis of online reviews of museums in Kazakhstan by applying advanced text mining and machine learning techniques. The research seeks to uncover the sentiments and topics expressed by visitors regarding their experiences with museums in Kazakhstan. The methodology uses a combination of sentiment analysis, topic modelling and text classification to provide a nuanced understanding of the visitor feedback. The topics identified from the analysis are further categorised according to different traveller types, providing insight into how different groups of visitors engage with the museum offer. The results show that visitors are more likely to make statements about the exhibition, services, outside and experience topics. Furthermore, each traveller type focuses on different aspects of museums in their reviews. In light of these issues, it aims to improve the interaction between visitors and the destination and provide practical implications for museum management.

Keywords

Kazakhstan, Tourism, Museum, Text mining, Traveller types

Ethical Statement

There is no need to approval of ethics committee because of the using secondary data and it is reachable by public

Supporting Institution

Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan

Project Number

АР19679687

Thanks

Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan

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