Dergiler / İşletme Araştırmaları Dergisi / 2020 / Cilt: 12 - Sayı: 1

Determination of Customer Satisfaction by Text Mining: Case of Cappadocia Hotels

Sayfa
546–556
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

It is thought that providing online environments with such facilities is important in obtaining reliable and valid information.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Purpose – The aim of this study is to analyze the comments of travel web sites on 28 hotels that have 4 or 5 stars, operating in Cappadocia region by text mining and to determine the satisfaction level of customers from hotels.
📊
Temel Bulgular & Veri:For this purpose, text mining methods were used and 10 263 comments were downloaded and analyzed.
💡
Sonuç & Katkı:It is thought that providing online environments with such facilities is important in obtaining reliable and valid information.
🔬Tür:Literatür & Derleme Çalışması
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

Özet

Purpose – The aim of this study is to analyze the comments of travel web sites on 28 hotels that have 4 or 5 stars, operating in Cappadocia region by text mining and to determine the satisfaction level of customers from hotels. For this purpose, text mining methods were used and 10 263 comments were downloaded and analyzed. Design/methodology/approach – The research focuses on 5 and 4-star hotel reviews of the Cappadocia region in the English language version. For this study, 10263 comments were obtained from 28 different hotels. The excel file created in the context of data acquisition was added to the RapidMiner data mining program. Then, a model was created for the individual separation of the words mentioned in the headlines and comments. The aim is to make the word frequencies by determining the words in the comments as attributes, and also to make them suitable for the association analysis. Findings – It was determined that the tourists made comments on the room experience, staff and hotel dimensions. They have stated that the rooms were great, clean, gorgeous, amazing, unique and spacious. The hotel staff was found to be helpful and friendly. It is concluded that hotels are generally clean, beautiful and unique. It can be said that there is a high level of satisfaction from the hotels. Discussion – The fact that the data obtained by the method of text mining has facilitated generalizations. This method can be defined as the most common factors that tourists perceive when expressing their opinions and sharing their experiences online. In this respect, they have directly received the service quality, physical condition, etc., they expressed their comments about many factors as easily as they felt. It is thought that providing online environments with such facilities is important in obtaining reliable and valid information.

🔖

Bu Makaleye Atıf Yap

ÇATIR, O., SAKALLI, K., ARMUTLU, H., ARMUTLU, Ş. (2020). Determination of Customer Satisfaction by Text Mining: Case of Cappadocia Hotels. İşletme Araştırmaları Dergisi, Cilt: 12 - Sayı: 1, 546–556. https://ajindex.com/articles/198229

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (14)

  1. Albayrak, M. (2015). Bilimsel araştırmalarda veri madenciliği kullanımı. International Journal of Social Sciences and Education Research, 3(3), 751-760.
  2. Arunadevi, J., Ramya, S., and Raja, M. R. (2018). A study of classification algorithms using Rapidminer. International Journal of Pure and Applied Mathematics, 119(12), 15977-15988.
  3. Berezina, K., Bilgihan, A., Cobanoglu, C., and Okumus, F. (2016). Understanding satisfied and dissatisfied hotel customers: Text mining of online hotel reviews. Journal of Hospitality Marketing and Management, 25(1), 1–24.
  4. Blal, I., and Sturman, M.C., (2014). The differential effects of the quality and quantity of online reviews on hotel room sales. Cornell Hospitality Q. 55 (4), 365–375.
  5. Büschken, J., and Allenby, G. M. (2016). Sentence-based text analysis for customer reviews. Marketing Science, 35(6), 953–975.
  6. Chau, M., and Xu, J. (2012). Business intelligence in blogs: Understanding consumer interactions and communities. MIS quarterly, 36(4), 1189-1216.
  7. Chung, N. and Koo, C. (2015), “The use of social media in travel information search”, Telematics and Informatics, Vol. 32 No. 2, pp. 215-229.
  8. Choi, T. Y., and Chu, R. (2001). Determinants of hotel guests' satisfaction and repeat patronage in the Hong Kong hotel industry. International Journal of HospitalityManagement, 20(3), 277-297.
  9. Clemons, E., Gao, G., and Hitt, L. (2006). When online reviews meet hyperdifferentiation: A study of the craft beer industry. Journal of Management Information Systems, 23(2), 149-171.
  10. Gao, B., Hu, N., and Bose, I. (2017). Follow the herd or be myself? An analysis of consistency in the behavior of reviewers and helpfulness of their reviews. Decision Support Systems, 95, 1–11.
  11. Gao, S., Tang, O., Wang, H., and Yin, P., (2018). Identifying competitors through comparative relation mining of online reviews in the restaurant industry. Int. J. Hospitality Manag. 71, 19–32.
  12. Geetha, M., Singha, P., and Sinha, S., (2017). Relationship between customer sentiment and online customer ratings for hotels-An empirical analysis. Tourism Manag. 61, 43–54.
  13. Ghose, A., and Ipeirotis, P. (2011). Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE Transactions on Knowledge and Data Engineering (TKDE), 23(10), 1498-1512.
  14. Guo, Y., Barnes, S. J., and Jia, Q. (2017). Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent Dirichlet allocation. Tourism Management, 59, 467–483.