SavedAdd your journal

Journals / Journal of New Tourism Trends / 2025 / Cilt: 6 Sayı: 2

Analyzing Online Comments on Cultural Heritage Sites in Kütahya Using Sentiment Analysis Methods

Kütahya’daki Kültürel Miras Alanlarına Yönelik Çevrimiçi Yorumların Duygu Analizi Yöntemiyle İncelenmesi

İlker Vural(KÜTAHYA DUMLUPINAR ÜNİVERSİTESİ)0000-0002-3357-7725
Elif Tuba Tamer(KÜTAHYA DUMLUPINAR ÜNİVERSİTESİ)0000-0002-1192-5370
Pages
158–171
Publication Date
2025-12-28
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Differentiating itself from sentiment analysis studies that predominantly focus on mainstream destinations, this research offers a data-driven and original perspective for destination managers and policymakers seeking to manage the digital visibility of emerging cultural heritage sites.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study aims to examine visitor perceptions of heritage sites in Kütahya—an emerging cultural heritage tourism destination—through sentiment analysis of online reviews.
📊
Key Findings & Data:In the study, 599 visitor reviews collected from TripAdvisor using the Instant Data Scraper tool were analyzed with the VADER algorithm and the TF-IDF method, taking into account the limitations of machine translation.
💡
Conclusion & Impact:Differentiating itself from sentiment analysis studies that predominantly focus on mainstream destinations, this research offers a data-driven and original perspective for destination managers and policymakers seeking to manage the digital visibility of emerging cultural heritage sites.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Turizm · Kültürel Miras · Duygu Analizi

Abstract

This study aims to examine visitor perceptions of heritage sites in Kütahya—an emerging cultural heritage tourism destination—through sentiment analysis of online reviews. A text mining–based sentiment analysis approach was adopted, as it enables the rapid and systematic analysis of large-scale qualitative data derived from digital platforms. In the study, 599 visitor reviews collected from TripAdvisor using the Instant Data Scraper tool were analyzed with the VADER algorithm and the TF-IDF method, taking into account the limitations of machine translation. The analysis reveals that 75.96% of the reviews express positive sentiment, indicating a high level of visitor satisfaction with the destination experience. The TF-IDF results highlight key terms such as “museum,” “castle,” and “historical,” demonstrating that the cognitive image of the destination is primarily constructed around its architectural and historical features. Differentiating itself from sentiment analysis studies that predominantly focus on mainstream destinations, this research offers a data-driven and original perspective for destination managers and policymakers seeking to manage the digital visibility of emerging cultural heritage sites.

Özet

Bu çalışma, kültürel miras turizmi bağlamında gelişmekte olan bir destinasyon olan Kütahya’daki miras alanlarına yönelik ziyaretçi algılarını çevrimiçi yorumların duygu analizi yöntemiyle incelemeyi amaçlamaktadır. Nitel verilerin büyük hacimli çevrimiçi kaynaklardan hızlı ve sistematik biçimde analizine imkân tanıması nedeniyle çalışma kapsamında metin madenciliğine dayalı duygu analizi yöntemi tercih edilmiştir. Araştırmada, TripAdvisor platformundan Instant Data Scraper aracıyla elde edilen 599 ziyaretçi yorumu, makine çevirisi kısıtları gözetilerek VADER algoritması ve TF-IDF yöntemiyle analiz edilmiştir. Analiz sonuçları, yorumların %75,96’sının pozitif duygu içerdiğini göstererek, ziyaretçilerin destinasyonun sunduğu deneyimden yüksek düzeyde tatmin olduğunu ortaya koymaktadır. TF-IDF analizi sonucunda öne çıkan “müze”, “kale” ve “tarihî” anahtar kelimeleri, destinasyonun bilişsel imajının mimari ve tarihsel unsurlar üzerine inşa edildiğini kanıtlamaktadır. Çalışma, literatürde genellikle ana akım destinasyonlara odaklanan duygu analizi araştırmalarından farklılaşarak, gelişmekte olan kültürel miras alanlarının dijital görünürlüğünün yönetilmesine dair destinasyon yöneticilerine ve politika yapıcılara veri odaklı, özgün bir perspektif sunmaktadır.

🔖

Cite This Article

Vural, İ., Tamer, E. T. (2025). Analyzing Online Comments on Cultural Heritage Sites in Kütahya Using Sentiment Analysis Methods. Journal of New Tourism Trends, Cilt: 6 Sayı: 2, 158–171. https://doi.org/10.58768/joinntt.1819232

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (39)

  1. Abia, V. M. ve Johnson, E. H. (2024). Sentiment analysis techniques: A comparative study of logistic regression, random forest and naive Bayes on general English and Nigerian texts. Journal of Engineering Research and Reports, 26(9), 123–135.
  2. Anwar, A., Ur Rehman, I., Nasralla, M. M., Bin Altaf Khattak, S. ve Khilji, N. (2023). Sentiment analysis and student emotions: Improving satisfaction in online learning platforms. 2023 IEEE International Smart Cities Conference (ISC2), 1–7. https://doi.org/10.1109/isc257844.2023.10293422
  3. Arya, V., Mishra, A. K. M. ve González Briones, A. (2022). Analysis of sentiments on the onset of COVID-19 using machine learning techniques. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 11(1), 45-63
  4. Balahur, A. ve Turchi, M. (2014). Comparative experiments using machine translation for cross-lingual sentiment analysis. Computer Speech & Language, 28(1), 56-75.
  5. Ballantyne, R., Hughes, K., Ding, P. ve Liu, D. (2014). Chinese and international visitor perceptions of interpretation at Beijing built heritage sites. Journal of Sustainable Tourism, 22(5), 705-725.
  6. Bino, D., Dhanalakshmi, V. ve Udupi, P. K. (2024). Sentiment analysis and machine learning for tourism feedback data analysis. AI Technologies for Personalized and Sustainable Tourism, 215–252. https://doi.org/10.4018/979-8-3693-5678-4.ch009
  7. Cao, H., Wang, M., Su, S. ve Kang, M. (2022). Explicit quantification of coastal cultural ecosystem services: A novel approach based on the content and sentimental analysis of social media. Ecological Indicators, 137, 108756.
  8. Chitteti, C., Kopparam, R., Ganesh, B. V. S. S., Sutraya, S., Kamakshi, V. ve Jangam, S. (2024). ML-driven emotion identification for feedback analysis In E-learning Platforms. 2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0, 1–5. https://doi.org/10.1109/otcon60325.2024.10687860
  9. Chun, X., Wang, M., Ren, Y. ve Zhu, S. (2024). Enhancing aspect-based sentiment analysis in tourism using large language models and positional information. arXiv preprint arXiv:2409.14997. https://arxiv.org/abs/2409.14997
  10. Feng, X., Yuan, K., Guan, X. ve Qiu, L. (2022). An emotion analysis dataset of course comment texts in massive online learning course platforms. Interactive Learning Environments, 32(4), 1219–1233. https://doi.org/10.1080/10494820.2022.2115517
  11. George, O. A. ve Ramos, C. M. Q. (2024). Sentiment analysis applied to tourism: Exploring tourist-generated content in the case of a wellness tourism destination. International Journal of Spa and Wellness, 7(2), 139–161. https://doi.org/10.1080/24721735.2024.2352979
  12. Ginzarly, M. ve Teller, J. (2021). Online communities and their contribution to local heritage knowledge. Journal of Cultural Heritage Management and Sustainable Development, 11(4), 361-380.
  13. Gupta, C. P. ve Kumar, V. V. R. (2024). A study on sustainable tourism and application of sentiment analysis in the tourism industry. 2024 International Conference on Emerging Techniques in Computational Intelligence (ICETCI), 52–57. https://doi.org/10.1109/icetci62771.2024.10704180
  14. Gupta, S., Ranjan, R. ve Singh, S. N. (2024). Comprehensive study on sentiment analysis: From rule-based to modern LLM based system. arXiv preprint arXiv:2409.09989.
  15. Gursoy, D., Akova, O. ve Atsız, O. (2022). Understanding the heritage experience: A content analysis of online reviews of World Heritage Sites in Istanbul. Journal of Tourism and Cultural Change, 20(3), 311-334.
  16. Hutto, C. J. ve Gilbert, E. (2014). VADER: A parsimonious rule-based model for sentiment analysis of social media text. In E. Adar & P. Resnick (Eds.), Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media (pp. 216-225). Ann Arbor, MI: AAAI Press.
  17. J, Dr. B. (2024). A study and development of applicatıon on sentiment analysis. International Scientific Journal of Engineering and Management, 03(03), 1–7. https://doi.org/10.55041/isjem01354
  18. Kourtit, K., Nijkamp, P. ve Romão, J. (2019). Cultural heritage appraisal by visitors to global cities: The use of social media and urban analytics in urban buzz research. Sustainability, 11(12), 3470.
  19. Kumar, B. K., Prajwal, M. L. ve Nivedita. (2024). Sentiment analysis of Indian tourist place reviews: A machine learning-based exploration. 4th International Conference on Intelligent Technologies (CONIT), 1–5. https://doi.org/10.1109/conit61985.2024.10626602
  20. Kumari, P. ve Jijja, A. (2023). Sentiment analysis on online social networking data for the identification of depression using several AI techniques: A literature review. 2023 International Conference on Communication, Security and Artificial Intelligence (ICCSAI), 457–464. https://doi.org/10.1109/iccsai59793.2023.10420945
  21. Modi, A. (2024). Sentiment Analysis on textual data - A comparison of accuracy using different algorithms. Interantıonal Journal of Scıentıfıc Research in Engıneerıng and Management, 08(06), 1–5. https://doi.org/10.55041/ijsrem36009
  22. Mohammad, S. M., Salameh, M. ve Kiritchenko, S. (2016). How translation alters sentiment. Journal of Artificial Intelligence Research, 55, 95-130.
  23. Nguyen, H. L. ve Dao, T. H. (2024). Text mining: sentiment analysis of reviews on TripAdvisor for Vietnam’s Michelin-starred and selected restaurants. In the 2023 Michelin Guide. Journal of Multimedia Information System, 11(2), 131–148. https://doi.org/10.33851/jmis.2024.11.2.131
  24. Poria, Y., Butler, R. ve Airey, D. (2003). The core of heritage tourism. Annals of Tourism Research, 30(1), 238-254.
  25. Radecki, A. ve Rybicki, T. (2024). Comparison of sentiment analysis methods used to investigate the quality of teaching aids based on virtual simulators of embedded systems. Electronics, 13(10), 1811. https://doi.org/10.3390/electronics13101811
  26. Raja, J. G. J. S. ve Juliet, S. (2023). Deep learning-based sentiment analysis of TripAdvisor reviews. In 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC) (pp. 560-565). Salem, India: IEEE. doi:10.1109/ICAAIC56838.2023.10140848
  27. Rasoolimanesh, S. M., Seyfi, S., Hall, C. M. ve Hatamifar, P. (2021). Understanding memorable tourism experiences and behavioural intentions of heritage tourists. Journal of Destination Marketing & Management, 21, 100621.
  28. Richards, G. ve Raymond, C. (2000). Creative tourism. ATLAS News, 23, 16–20.
  29. Riswanto, A. L., Kim, S. ve Kim, H. S. (2023). Analyzing online reviews to uncover customer satisfaction factors in Indian cultural tourism destinations. Behavioral Sciences, 13(11), 923.
  30. Tripadvisor. (2024). Kütahya gezilecek yerler: https://www.tripadvisor.com.tr/Attractions-g1413798-Activities-Kutahya.html, Erişim tarihi: 22 Kasım 2024
  31. Tsiligaridis, J. (2024). Approaches of classification models for sentiment analysis. Advanced Natural Language Processing, 81–86. https://doi.org/10.5121/csit.2024.141007
  32. Türker, N. ve Yaşar, Z. (2019). Batı Karadeniz bölümü antik kentlerinin kültürel miras turizmi açısından değerlendirilmesi. Karabük Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 9(1), 1-27.
  33. Vishwakarma, S. (2023). A review paper on sentiment analysis using machine learning. International Journal for Research in Applied Science and Engineering Technology, 11(11), 528–531. https://doi.org/10.22214/ijraset.2023.56545
  34. Wabiser, Y. D. ve Singgalen, Y. A. (2024). Sentiment and toxicity analysis in the narratives of Wamena’s Cultural Heritage: Understanding Community Perspectives and External Influences. KLIK, 5, 242-262.
  35. Wang, C., Liu, J., Wei, L. ve Zhang, T. (2020). Impact of tourist experience on memorability and authenticity: a study of creative tourism. Journal of Travel & Tourism Marketing, 37(1), 48-63.
  36. Xiang, Z., Schwartz, Z., Gerdes Jr, J. H. ve Uysal, M. (2015). What can big data and text analytics tell us about hotel guest experience and satisfaction?. International Journal of Hospitality Management, 44, 120-130.
  37. Zakarija, I., Škopljanac-Mačina, F., Marušić, H., ve Blašković, B. (2024). A sentiment analysis model based on user experiences of Dubrovnik on the Tripadvisor platform. Applied Sciences, 14(18), 8304. https://doi.org/10.3390/app14188304
  38. Zhang, A., ve Xiao, H. (2025). Digitalising cultural heritage through online reviews: A sentiment analysis. Current Issues in Tourism, 1-18.
  39. Zhang, J. (2024). Enhancing sentiment analysis through text classification using data mining approach. 2024 2nd International Conference on Mechatronics, IoT and Industrial Informatics (ICMIII), 857–865. https://doi.org/10.1109/icmiii62623.2024.00166