Makine Öğrenmesi ile Çevrimiçi Otel Yorumlarının Değerlendirilmesi: Duygu Analizi ve Konu Modelleme ile Derinlemesine Bakış

Yıl 2025, Sayı: 3, 1 - 23, 30.11.2025

Egemen Güneş Tükenmez

Öz

Turizm sektörü içerisinde bulunan seyahat platformlarına yapılan çevrimiçi kullanıcı değerlendirmeleri, otel işletmelerinin hizmet kalitesini ve sahip olduğu tüketici memnuniyetinin belirlenmesinde önemli bir faktör haline gelmiştir. Bu kapsamda ilgili turizm platformlarındaki bilgi birikiminin artması, otel yönetimlerinin büyük veri kapsamında kullanılan yöntemler ile çevrimiçi kullanıcıların yaptığı değerlendirmelerin net bir şekilde anlamasını sağlamaktadır. Bu değerlendirmelerin yapısal ve anlamsal özelliklerinin analiz edilmesi, otellerin misafir memnuniyet düzeyini etkileyen unsurları bulmalarına yardım etmektedir. Bu gelişmeler kapsamında bu çalışmada Türkiye’nin önemli bir turizm destinasyonu olan Alanya şehrinde bulunan otellerin çevrimiçi yorumları, makine öğrenmesi tabanlı doğal dil işleme ve metin madenciliği teknikleri kullanılarak analiz edilmiştir. Yorumların müşteri memnuniyeti durumunun analizi için duygu analizi, içermiş olduğu konuların ortaya çıkarılması için ise konu modelleme yöntemleri veri setine uygulanmıştır. Ayrıca çalışmada ortaya konan duygu sınıfları için hangi konuların bu sınıflara en fazla etkiye sahip olduğunu anlamaya yönelik ise lojistik regresyon analizi ek olarak yapılmıştır. Araştırma sonucunda otelin sahip olduğu olanaklar, animasyon ve personel konuları, misafirlerin memnuniyetine en fazla olumlu yansıyan konular olurken, önbüro ve oda konularının ise daha çok olumsuz duygularla ifade edildiği ortaya çıkmıştır.

Anahtar Kelimeler

Turizm İşletmeciliği , Otel , Makine Öğrenmesi , Konu Modelleme , Duygu Analizi , Alanya

Etik Beyan

Bu çalışma Etik Kurul beyanı gerektiren çalışmalar kapsamına girmemektedir. Bu çalışmanın hazırlanma sürecinde bilimsel ve etik ilkelere uyulduğu ve yararlanılan tüm çalışmaların kaynakçada belirtildiği beyan olunur.

Destekleyen Kurum

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Teşekkür

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Evaluating Online Hotel Reviews with Machine Learning: Insights from Sentiment Analysis and Topic Modeling

Öz

User generated content platforms in tourism industry have become a significant factor according to determine customer satisfaction and service performance of hotel businesses. In this context, the increase in information on tourism platforms has enabled hotel managements to understand their shares more clearly by using several methods in the scope of big data management. Analyzing the structural and semantic features of online reviews helps hotel managements better find out the factors influencing guest satisfaction level. Taking into consideration of this fact, online reviews of hotels which located in Alanya, one of the Turkey's major tourism destinations, were analyzed using machine learning-based natural language and text mining techniques. Topic modeling and sentiment analysis were implemented into dataset to identify the most frequently mentioned topics and their impact on customer satisfaction level. Furthermore, logistic regression analysis was performed to achieve which topics have the most influence for the determined sentiment classes. The results show that amenities, animation and staff-related topics have the most positive influence on topics on satisfaction, whereas front desk and room-related topics are associated with negative sentiments.

Anahtar Kelimeler

Tourism Management , Otel , Machine Learning , Topic Modeling , Sentiment Analysis , Alanya

Etik Beyan

This study does not fall within the scope of studies requiring an Ethics Committee declaration. We declare that scientific and ethical principles were adhered to throughout the preparation of this study, and that all studies cited are cited in the references.

Destekleyen Kurum

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Teşekkür

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Kaynakça

• Ağca, Y., & Gündüz, C. (2023). Türkiye’deki otel konuk yorumları ve puanlarının metin madenciliği ile analizi. Yönetim ve Ekonomi Dergisi, 30(2), 397–411. https://doi.org/10.18657/yonveek.1063592
• Al-Hakeem, L. M. H., Ismail, A. H., Jarallah, M. A., Amanah, A. A., & Abbood, N. H. (2024). The relationship between strategic physiognomy and e-service delivery: The mediating role of marketing intelligence. Journal of Management and Economic Studies, 6(4), 398–407. https://doi.org/10.26677/tr1010.2024.1483
• Ali, T., Omar, B., & Soulaimane, K. (2022). Analyzing tourism reviews using an LDA topic-based sentiment analysis approach. MethodsX, 9, Article 101894. https://doi.org/10.1016/j.mex.2022.101894
• Ameur, A., Hamdi, S., & Ben Yahia, S. (2023). Sentiment analysis for hotel reviews: A systematic literature review. ACM Computing Surveys, 56(2), 1–38. https://doi.org/10.1145/3581902
• Aydınbaş, G. (2023). Akıllı turizm (Turizm 4.0) teknolojileri üzerine iktisadi bir yaklaşım: Türkiye örneği. Journal of Tourism Intelligence and Smartness, 6(1), 26–44. https://doi.org/10.58636/jtis.1244836
• Badouch, M., & Boutaounte, M. (2023). Personalized travel recommendation systems: A study of machine learning approaches in tourism. Journal of Artificial Intelligence Machine Learning and Neural Network, 33, 35–45. https://doi.org/10.55529/jaimlnn.33.35.45
• Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3(1), 993–1022.
• Borrajo-Millán, F., Alonso-Almeida, M. D. M., Escat-Cortes, M., & Yi, L. (2021). Sentiment analysis to measure quality and build sustainability in tourism destinations. Sustainability, 13(11), Article 6015. https://doi.org/10.3390/su13116015
• Buhalis, D. (2000). Tourism and information technologies: Past, present and future. Tourism Recreation Research, 25(1), 41–58. https://doi.org/10.1080/02508281.2000.11014899
• Büyükeke, A., Sökmen, A., & Gencer, C. (2020). Metin madenciliği ve duygu analizi yöntemleri ile sosyal medya verilerinden rekabetçi avantaj elde etme: Turizm sektöründe bir araştırma. Journal of Tourism and Gastronomy Studies, 8(1), 322–335. https://doi.org/10.21325/jotags.2020.550
• Cabi Bilge, A. (2024). Online otel yorumlarının duygu analizi ile incelenmesi: Konya beş yıldızlı oteller örneği. Selçuk Üniversitesi Akşehir Meslek Yüksekokulu Sosyal Bilimler Dergisi, 17, 84–93. https://doi.org/10.29249/selcuksbmyd.1431702
• Calheiros, A. C., Moro, S., & Rita, P. (2017). Sentiment classification of consumer-generated online reviews using topic modeling. Journal of Hospitality Marketing & Management, 26(7), 675–693. https://doi.org/10.1080/19368623.2017.1310075
• Cantallops, A. S., & Salvi, F. (2014). New consumer behavior: A review of research on eWOM and hotels. International Journal of Hospitality Management, 36, 41–51. https://doi.org/10.1016/j.ijhm.2013.08.007
• Chen, C. F., & Tsai, D. (2007). How destination image and evaluative factors affect behavioral intentions? Tourism Management, 28(4), 1115–1122. https://doi.org/10.1016/j.tourman.2006.07.007
• Cheung, C. M., & Lee, M. K. (2012). What drives consumers to spread electronic word of mouth in online consumer-opinion platforms. Decision Support Systems, 53(1), 218–225. https://doi.org/10.1016/j.dss.2012.01.015 Chittiprolu, V., Singh, S., Bellamkonda, R. S., & Vanka, S. (2021). A text mining analysis of online reviews of Indian hotel employees. Anatolia, 32(2), 232–245. https://doi.org/10.1080/13032917.2020.1856157
• De Pelsmacker, P., Van Tilburg, S., & Holthof, C. (2018). Digital marketing strategies, online reviews and hotel performance. International Journal of Hospitality Management, 72, 47–55. https://doi.org/10.1016/j.ijhm.2018.01.003
• Demsar, J., Curk, T., Erjavec, A., Gorup, C., Hočevar, T., Milutinović, M., Možina, M., Polajnar, M., Toplak, M., Starič, A., Štajdoher, M., Umek, L., Žagar, L., Zbontar, J., Žitnik, M., & Zupan, B. (2013). Orange: Data mining toolbox in Python. Journal of Machine Learning Research, 14, 2349–2353.
• Doğan, S., Basaran, M. A., & Kantarci, K. (2020). Determination of attributes affecting price-performance using fuzzy rule-based systems: Online ratings of hotels by travel 2.0 users. Journal of Hospitality and Tourism Technology, 11(2), 291–311. https://doi.org/10.1108/jhtt-07-2018-0067
• Falces Delgado, C., Sierra Díez, B., Becerra Grande, A., & Briñol Turnes, P. (1999). HOTELQUAL: A scale for measuring perceived quality in lodging services. Esic Market, 104, 139–199. https://doi.org/10.61520/et.1391999.1042
• Grönroos, C. (1984). A service quality model and its marketing implications. European Journal of Marketing, 18(4), 36–44. https://doi.org/10.1108/EUM0000000004784
• Gürbüz, M., Sürmeli, D., Taşkın, K., & Cebeci, H. İ. (2024). Otellere için paylaşılan çevre ile alakalı yorumların metin madenciliği ile analizi: Antalya otelleri üzerine bir araştırma. Business & Management Studies: An International Journal, 12(1), 218–239. https://doi.org/10.15295/bmij.v12i1.2369
• Hennig-Thurau, T. (2004). Customer orientation of service employees: Its impact on customer satisfaction, commitment, and retention. International Journal of Service Industry Management, 15(5), 460–478. https://doi.org/10.1108/09564230410564939
• Hogenboom, A., Heerschop, B., Frasincar, F., Kaymak, U., & de Jong, F. (2014). Multilingual support for lexicon-based sentiment analysis guided by semantics. Decision Support Systems, 62, 43–53. https://doi.org/10.1016/j.dss.2014.03.004
• Hu, N., Zhang, T., Gao, B., & Bose, I. (2019). What do hotel customers complain about? Text analysis using structural topic model. Tourism Management, 72, 417–426. https://doi.org/10.1016/j.tourman.2019.01.002
• Hutto, C., & Gilbert, E. (2014, May). VADER: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 8, No. 1, pp. 216–225). https://doi.org/10.1609/icwsm.v8i1.14550
• Jain, S., Jain, S. K., & Vasal, S. (2024, April). An effective TF-IDF model to improve the text classification performance. In 2024 IEEE 13th International Conference on Communication Systems and Network Technologies (CSNT) (pp. 1–4). IEEE. https://doi.org/10.1109/csnt60213.2024.10545818
• Kim, H., Yoon, J., & Nicolau, J. L. (2023). Unveiling technological innovation in hospitality and tourism through patent data: Development perspective and competition landscaping. International Journal of Hospitality Management, 111, Article 103478. https://doi.org/10.1016/j.ijhm.2023.103478
• Kim, Y. J., & Kim, H. S. (2022). The impact of hotel customer experience on customer satisfaction through online reviews. Sustainability, 14(2), Article 848. https://doi.org/10.3390/su14020848
• Knutson, B., Stevens, P., Wullaert, C., Patton, M., & Yokoyama, F. (1990). LODGSERV: A service quality index for the lodging industry. Hospitality Research Journal, 14(2), 277–284. https://doi.org/10.1177/109634809001400230
• Leung, D., Law, R., Van Hoof, H., & Buhalis, D. (2013). Social media in tourism and hospitality: A literature review. Journal of Travel & Tourism Marketing, 30(1–2), 3–22. https://doi.org/10.1080/10548408.2013.750919
• Li, H., Ye, Q., & Law, R. (2013). Determinants of customer satisfaction in the hotel industry: An application of online review analysis. Asia Pacific Journal of Tourism Research, 18(7), 784–802. https://doi.org/10.1080/10941665.2012.708351
• Li, J., Xu, L., Tang, L., Wang, S., & Li, L. (2018). Big data in tourism research: A literature review. Tourism Management, 68, 301–323. https://doi.org/10.1016/j.tourman.2018.03.009
• Litvin, S. W., Goldsmith, R. E., & Pan, B. (2008). Electronic word-of-mouth in hospitality and tourism management. Tourism Management, 29(3), 458–468. https://doi.org/10.1016/j.tourman.2007.05.011
• Mehraliyev, F., Chan, I. C. C., & Kirilenko, A. P. (2022). Sentiment analysis in hospitality and tourism: A thematic and methodological review. International Journal of Contemporary Hospitality Management, 34(1), 46–77. https://doi.org/10.1108/ijchm-02-2021-0132
• Mei, A. W. O., Dean, A. M., & White, C. J. (1999). Analysing service quality in the hospitality industry. Managing Service Quality, 9(2), 136–143. https://doi.org/10.1108/09604529910257920
• Momtazi, S., & Naumann, F. (2013). Topic modeling for expert finding using latent Dirichlet allocation. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(5), 346–353. https://doi.org/10.1002/widm.1102
• Nunkoo, R., Teeroovengadum, V., Ringle, C. M., & Sunnassee, V. (2020). Service quality and customer satisfaction: The moderating effects of hotel star rating. International Journal of Hospitality Management, 91, Article 102414. https://doi.org/10.1016/j.ijhm.2019.102414
• Palanisamy, P., Yadav, V., & Elchuri, H. (2013). Serendio: Simple and practical lexicon based approach to sentiment analysis. In Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (pp. 543–548).
• Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12–40.
• Puh, K., & Bagić Babac, M. (2023). Predicting sentiment and rating of tourist reviews using machine learning. Journal of Hospitality and Tourism Insights, 6(3), 1188–1204. https://doi.org/10.1108/jhti-02-2022-0078
• Qi, M., Li, X., Zhu, E., & Shi, Y. (2017). Evaluation of perceived indoor environmental quality of five-star hotels in China: An application of online review analysis. Building and Environment, 111, 1–9. https://doi.org/10.1016/j.buildenv.2016.09.027
• Roozen, I., & Raedts, M. (2018). The effects of online customer reviews and managerial responses on travelers’ decision-making processes. Journal of Hospitality Marketing & Management, 27(8), 973–996. https://doi.org/10.1080/19368623.2018.1488229
• Rossetti, M., Stella, F., & Zanker, M. (2016). Analyzing user reviews in tourism with topic models. Information Technology & Tourism, 16(1), 5–21. https://doi.org/10.1007/s40558-015-0035-y
• Roy, G. (2023). Travelers’ online review on hotel performance: Analyzing facts with the theory of lodging and sentiment analysis. International Journal of Hospitality Management, 111, Article 103459. https://doi.org/10.1016/j.ijhm.2023.103459
• Ruan, J., Satjawathee, T., & Awirothananon, T. (2025). The impact of digital technology on tourism economic growth: Empirical analysis based on provincial panel data, 2010–2022. Tourism and Hospitality, 6(2), Article 73. https://doi.org/10.3390/tourhosp6020073
• Seker, S. E. (2016). Duygu analizi (Sentimental analysis). YBS Ansiklopedi, 3(3), 21–36.
• Sezgin, M., & Duman, A. (2023). Elektronik ağızdan ağıza pazarlama kapsamında konaklama işletmelerine yönelik çevrimiçi yorumların duygu analizi yöntemiyle incelenmesi: Alanya örneği. Türk Turizm Araştırmaları Dergisi, 7(2), 244–265. https://doi.org/10.26677/tr1010.2023.1240
• Sparks, B. A., & Browning, V. (2011). The impact of online reviews on hotel booking intentions and perception of trust. Tourism Management, 32(6), 1310–1323. https://doi.org/10.1016/j.tourman.2010.12.011
• Tuna, M. F. (2019). Çevrimiçi yorum ve şikâyetlerin otel işletmeleri üzerinden duygu analizi ile incelenmesi [Yayımlanmamış doktora tezi]. Erciyes Üniversitesi.
• Vermeulen, I. E., & Seegers, D. (2009). Tried and tested: The impact of online hotel reviews on consumer consideration. Tourism Management, 30(1), 123–127. https://doi.org/10.1016/j.tourman.2008.04.008
• Wang, Y. X., & Zhang, Y. J. (2012). Nonnegative matrix factorization: A comprehensive review. IEEE Transactions on Knowledge and Data Engineering, 25(6), 1336–1353. https://doi.org/10.1109/TKDE.2012.51
• Xu, H., & Lv, Y. (2022). Mining and application of tourism online review text based on natural language processing and text classification technology. Wireless Communications and Mobile Computing, Article 9905114. https://doi.org/10.1155/2022/9905114
• Yang, Y., Park, S., & Hu, X. (2018). Electronic word of mouth and hotel performance: A meta-analysis. Tourism Management, 67, 248–260. https://doi.org/10.1016/j.tourman.2018.01.015
• Zagar, A. P. (2022, March 18). LDAvis: Visualization for LDA topic modelling. Orange Data Mining. https://orangedatamining.com/blog/2022/2022-03-18-ldavis/
• Zarezadeh, Z. Z., Rastegar, R., & Xiang, Z. (2022). Big data analytics and hotel guest experience: A critical analysis of the literature. International Journal of Contemporary Hospitality Management, 34(6), 2320–2336. https://doi.org/10.1108/ijchm-10-2021-1293
• Zhong, L., Morrison, A. M., Zheng, C., & Li, X. (2023). Destination image: A consumer-based, big data-enabled approach. Tourism Review, 78(4), 1060–1077. https://doi.org/10.1108/tr-04-2022-0190