Research Article
BibTex RIS Cite

Türkiye’deki Otel Konuk Yorumları ve Puanlarının Metin Madenciliği ile Analizi

Year 2023, , 397 - 411, 15.06.2023
https://doi.org/10.18657/yonveek.1063592

Abstract

Konaklama tesislerindeki konuk yorumları ve verilen puanlar, günümüzde seyahat planlaması yapan misafirler için oldukça önemli bir faktör haline gelmiştir. İnternet üzerindeki seyahat acenteleri ve platformları, misafirlerin konaklama tercihlerini şekillendirmede kritik bir rol oynamaktadır. Bu platformlar, kullanıcıların gerçek deneyimlerini paylaşmasına ve diğer potansiyel misafirlere yol gösterici bilgiler sunmasına olanak sağlamaktadır. Seyahat acenteleri ve seyahat platformları, konaklama tesislerine ait kullanıcı yorumlarını ve verilen puanları genellikle detaylı bir şekilde sunmaktadır. Misafirler, otel veya diğer konaklama seçenekleri hakkında daha fazla bilgi edinmek, deneyimleri hakkında fikir sahibi olmak ve olumlu/negatif yönleri değerlendirmek için bu yorumlara güvenirler. Bu yorumlar, otelin temizlik düzeyi, hizmet kalitesi, personel yardımseverliği, konum avantajları, oda konforu, yiyecek ve içecek seçenekleri gibi birçok önemli unsuru içerebilir. Bu çalışma, Türkiye'deki konaklama tesisleri hakkında Türkçe olarak yapılan yorumları ve puanları metin madenciliği yöntemiyle analiz etmektedir. Bu amaçla, bir çevrimiçi seyahat acentesinden elde edilen Türkçe konaklama tesisleriyle ilgili yorumlar ve puanlar web madenciliği kullanılarak toplanmış ve ardından metin madenciliği işlemlerine tabi tutulmuştur. Çalışmada 60,252 Türkçe konuk yorumu ve puanı analiz edilmiştir. Türkiye'deki konaklama tesislerinin ortalama konuk puanı 3.93 olarak belirlenmiştir. Villa tipi tesisler en yüksek puanı almıştır (p=4.22; n=854). Coğrafi olarak, en yüksek puan İç Anadolu bölgesinde (p=4.07; n=5131), il olarak ise Nevşehir'de (p=4.53; n=2320) tespit edilmiştir. Metin madenciliği uygulaması sonucunda otel yorumlarında en sık tekrarlanan tekil kelimeler, puanlara göre gruplandırıldığında, misafirlerin 1 puan verdikleri tesisleri tavsiye etmedikleri, ancak 4 ve 5 puan verdikleri tesisleri tavsiye ettikleri ortaya çıkmıştır. Düşük puan verilen tesislerde, misafirlerin özellikle oda, kahvaltı, su ve temizlik konularında görüşlerini dile getirdikleri belirlenmiştir. Yüksek puan alan tesislerde ise misafirlerin otelin temiz olduğunu ve personelin misafirlerle ilgili olduğunu ifade eden kelimeler kullandıkları gözlemlenmiştir. Araştırma sonucunda, Türkiye'deki konaklama tesislerine yönelik Türkçe yorumlarda genel olarak, oda, kahvaltı, temizlik ve sıcak su sorunu gibi faktörlerin beğenilmeme ve dolayısıyla düşük puan verilmesine sebep olduğu tespit edilmiştir. Yüksek puan alımını etkileyen faktörlerin ise temizlik ve personelin ilgisiyle ilgili olduğu görülmektedir. Bu araştırmanın, sektör yöneticilerine, girişimcilere ve araştırmacılara, konuk memnuniyeti, konuk şikâyetleri ve memnuniyetle ilgili faktörlerin bilinmesi açısından katkı sağlayacağı düşünülmektedir. Türkiye'deki konaklama tesislerinin konuk yorumlarının metin madenciliği yöntemiyle analizini ele alan bu makaleden elde edilen sonuçlar, sektörün hizmet kalitesini ve konuk memnuniyetini artırmak için değerli bir rehber sağlamaktadır. Ayrıca, bu çalışma, gelecekteki araştırmalar için bir temel oluşturarak konaklama sektöründeki girişimciler ve akademisyenlere de yol gösterecektir.

References

  • Ağca, Y. (2021a). Otel Oda Fiyatlarını Açıklamada Makine Öğrenmesi Algoritmalarının Kıyaslanması. İşletme Araştırmaları Dergisi, 450-463
  • Ağca, Y. (2021b). Alternatif Veri Elde Etme Yöntemi Web Madenciliği: Otel Oda Fiyatlarının Zamansal Analizi. Çanakkale Onsekiz Mart Üniversitesi Yönetim Bilimleri Dergisi, 19(42), 1013-1034
  • Ağca, Y. (2021c). R Programlama Dili ile İstatistiksel Analiz ve Veri Madenciliği. İstanbul: Cinius Yayınları. Akter, S. ve Wamba, S. F. (2016). Big data analytics in E-commerce: a systematic review and agenda. Electron Markets, 173-194.
  • Albayrak, T. ve Caber, M. (2015). Prioritisation of the hotel attributes according to their influence on satisfaction: a comparison of two techniques. Tourism Management, 46, 43-50.
  • Atabay, L. ve Cizel, B. (2020). Comparative Content Analysis of Hotel Reviews by Mass Tourism Destination. Journal of Tourism and Services, 11(21), 147-166.
  • Boice, M. (2021, 03 19). The 13 Top Reasons Consumers Shop Online. https://www.junglescout.com/blog/reasons-consumers-shop-online/ adresinden alındı
  • Chaffey, D. (2017, 11 19). The reasons why consumers shop online instead of in stores. https://www.smartinsights.com/ecommerce/ecommerce-strategy/the-reasons-why-consumers-shop-online-instead-of-in-stores/ adresinden alındı
  • Chanwisitkul, P., Shahgholian, A. ve Mehandjiev, N. (2018). The Reason Behind the Rating: Text Mining of Online Hotel Reviews. 2018 IEEE 20th Conference on Business Informatics (CBI). Vienna, Austria : IEEE.
  • Chevalier, S. (2021, 07 26). Main reasons why global consumers chose to shop online 2021. statista.com: https://www.statista.com/statistics/676358/reasons-online-shoppers-prefer-to-shop-online/ adresinden alındı
  • Chittiprolu, V., Samala, N. ve Bellamkonda, R. S. (2021). Heritage hotels and customer experience: a text mining analysis of online reviews. International Journal of Culture, Tourism and Hospitality Research, 15(2), 131-156.
  • Cui, G., Lui, H.-K. ve Guo, X. (2014). The Effect of Online Consumer Reviews on New Product Sales. International Journal of Electronic Commerce, 17(1), 39-58.
  • Davras, Ö. ve Caber, M. (2019). Analysis of hotel services by their symmetric and asymmetric effects on overall customer satisfaction: A comparison of market segments. International Journal of Hospitality Management, 81, 83-93.
  • Dominici, G. ve Guzzo, R. (2010). Customer Satisfaction in the Hotel Industry: A Case Study from Sicily. International Journal of Marketing Studies, 2(2), 3-12.
  • Ekinci, Y., Dawes, P. ve Massey, G. (2008). Document details - An extended model of the antecedents and consequences of consumer satisfaction for hospitality services. European Journal of Marketing, 42(1-2), 35-68.
  • Gaikwad, S. V., Chaugule, A. ve Patil, P. (2014). Text Mining Methods and Techniques. International Journal of Computer Applications, 85(17), 42-45.
  • Gündüz, C., ve Gündüz, S. (2017), Mesleki Eğitimde Toplam Kalite Yönetimi ve Verimlilik: Niksar Mesleki Eğitim Merkezi Üzerine Bir Uygulama. Sosyal Bilimler Dergisi / The Journal of Social Science. 4(11), 925-940.
  • Han, H. J., Mankad, S., Gavirneni, N. ve Verma, R. (2016). What Guests Really Think of Your Hotel: Text Analytics of Online Customer Reviews. Cornell Hospitality Report, 16(2), 1-19.
  • Hananto, A. (2015). Application of Text Mining to Extract Hotel Attributes and Construct Perceptual Map of Five Star Hotels from Online Review: Study of Jakarta and Singapore Five-Star Hotels. ASEAN Marketing Journal, 7(2), 58-80.
  • Henneberry, R. (2012, 06 20). How To Cash In On The 5 Reasons People Buy Products Online. https://www.crazyegg.com/blog/reasons-people-buy-products-online/ adresinden alındı
  • Hu, N., Bose, I., Koh, N. S. ve Liu, L. (2012). Manipulation of online reviews: An analysis of ratings, readability, and sentiments. Decision Support Systems, 52(3), 674-684.
  • Jeong, M. ve Jeon, M. M. (2008). Customer Reviews of Hotel Experiences through Consumer Generated Media (CGM). Journal of Hospitality & Leisure Marketing, 17(1-2), 121-138.
  • Keskinkılıç, M., Ağca, Y. ve Karaman, E. (2016). İnternet ve Bilgi Sistemleri Kullanımının Turizm Dağıtım Kanallarına Etkisi Üzerine Bir Uygulama. İşletme Araştırmaları Dergisi, 8(4), 445-472. doi:10.20491/isarder.2016.227
  • Kim, Y. A. ve Srivastava, J. (2007). Impact of social influence in e-commerce decision making. ICEC '07: Proceedings of the ninth international conference on Electronic commerce, (s. 293-302).
  • Kuhzady, S. ve Ghasemi, V. (2019). Factors Influencing Customers' Satisfaction and Dissatisfaction with Hotels: A Text-Mining Approach. Tourism Analysis, 24(1), 69-79.
  • Kumar, S. ve Aulia, A. (2021). The Influence of Hotel Review Towards E-Trust and Its Implication on Hotel Booking Intention. (S. Kumar, Dü.) Indonesia.
  • Lau, K.-N., Lee, K.-H. ve Ho, Y. (2015). Text Mining for the Hotel Industry. Cornell Hotel and Restaurant Administration Quarterly, 46(3), 344-362.
  • Melián-González, S., Bulchand-Gidumal, J. ve López-Valcárcel, B. G. (2013). Online Customer Reviews of Hotels: As Participation Increases, Better Evaluation Is Obtained. Cornell Hospitality Quarterly, 53(3), 274-283. Miner, G. D., Elder, J., Fast, A., Hill, T., Nisbet, R. ve Delen, D. (2012). Practical Text Mining and Statistical Analysis for Non-structured Text Data. Oxford, UK: Academic Press.
  • Nicholas, C. K.-W. ve Lee, A. S. (2017). Voice of Customers: Text Analysis of Hotel Customer Reviews. CBDR 2017 (s. 104-111). Osaka, Japan: Association for Computing Machinery.
  • O'Fallon, M. J. ve Rutherford, D. G. (2011). Hotel Management and Operations. New Jersey, US: John Wiley & Sons.
  • Öğüt, H. ve Taş, B. K. (2012). The influence of internet customer reviews on the online sales and prices in hotel industry. The Service Industries Journal, 32(2), 197-214.
  • Palese, B. ve Usai, A. (2018). The relative importance of service quality dimensions in E-commerce experiences. International Journal of Information Management, 40, 132-140.
  • Pradana, A. W. ve Hayaty, M. (2019). The effect of stemming and removal of stopwords on the accuracy of sentiment analysis on indonesian-language texts. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Journal, 4(4), 375-380.
  • Prajapati, V. (2021, 01 22). Why Do People Shop Online? https://www.techprevue.com/reasons-prefer-online-shopping/ adresinden alındı
  • Rai, A. (2019, 06 01). What is Text Mining: Techniques and Applications. upgrad.com: https://www.upgrad.com/blog/what-is-text-mining-techniques-and-applications/ adresinden alındı
  • Schuckert, M. (2015). Hospitality and Tourism Online Reviews: Recent Trends and Future Directions. Journal of Travel & Tourism Marketing, 32(5), 608-621.
  • Shen, Z., Yang, X., Liu, C. ve Li, J. (2021). Assessment of Indoor Environmental Quality in Budget Hotels Using Text-Mining Method: Case Study of Top Five Brands in China. Sustainability, 13(8).
  • Singh, P. K., Sachdeva, A., Mahajan, D., Pande, N. ve Sharma, A. (2014). An approach towards feature specific opinion mining and sentimental analysis across e-commerce websites. 5th International Conference - Confluence The Next Generation Information Technology Summit (Confluence). Noida, India : IEEE.
  • Somprasertsri, G. ve Lalitrojwong, P. (2010). Mining Feature-Opinion in Online Customer Reviews for Opinion Summarization. Journal of Universal Computer Science, 16(6), 938-955.
  • Trenz, M. ve Berger, B. (2013). Analyzing Online Customer Reviews - An Interdisciplinary Literature Review And Research Agenda. ECIS 2013 Completed Research, (s. 83).
  • Truyens, M. ve Eecke, P. V. (2014). Legal aspects of text mining. Comput. Law Secur. Rev., 2182-2186. Tyagi, N. (2021, 05 10). Top 7 Text Mining Techniques. analyticssteps.com: https://www.analyticssteps.com/blogs/top-7-text-mining-techniques adresinden alındı
  • Wei, P.-S. ve Lu, H.-P. (2013). An examination of the celebrity endorsements and online customer reviews influence female consumers’ shopping behavior. Computers in Human Behavior, 29(1), 193-201.
  • Weiss, S. M., Indurkhya, N. ve Zhang, T. (2015). Fundamentals of Predictive Text Mining (2 b.). New York, US: Springer.
  • Xu, X. ve Li, Y. (2016). The antecedents of customer satisfaction and dissatisfaction toward various types of hotels: A text mining approach. International Journal of Hospitality Management, 55, 57-69.
  • Zhan, J., Loh, H. T. ve Liu, Y. (2009). Gather customer concerns from online product reviews – A text summarization approach. Expert Systems with Applications, 36(2), 2107-2115.

Analysis of Hotel Guest Reviews and Ratings in Turkey with Text Mining

Year 2023, , 397 - 411, 15.06.2023
https://doi.org/10.18657/yonveek.1063592

Abstract

Guest reviews and ratings of accommodation facilities have become a very important factor for guests planning their trips today. Travel agencies and platforms on the Internet play a critical role in shaping guests' accommodation choices. These platforms allow users to share real-life experiences and provide guidance to other potential guests. Travel agencies and travel platforms often provide detailed user reviews and ratings of accommodation facilities. Guests rely on these reviews to learn more about their hotel or other accommodation options, gain insight into their experience, and rate the positives and negatives. These comments can include many important factors such as the hotel's cleanliness level, service quality, staff helpfulness, location advantages, room comfort, and food and beverage options. This study analyses the comments and scores in Turkish about accommodation facilities in Turkey by text mining. For this purpose, reviews and ratings of Turkish accommodation facilities obtained from an online travel agency were collected using web mining and then subjected to text mining processes. In the study, 60,252 Turkish guest comments and scores were analysed. The average guest rating of accommodation facilities in Turkey was determined to be 3.93. Villa-type facilities got the highest score (p = 4.22; n = 854). Geographically, the highest score was found in the Central Anatolia region (p = 4.07; n = 5131), and the province was Nevşehir (p = 4.53; n = 2320). As a result of the text mining application, when the most frequently repeated single words in hotel comments were grouped according to scores, it was revealed that guests did not recommend the facilities they gave 1 point for, but the facilities they gave 4 and 5 points for. It was determined that in the facilities with low scores, guests expressed their opinions, especially on the room, breakfast, water, and cleanliness. In facilities with high scores, it has been observed that the guests use words that express that the hotel is clean and that the staff is related to the guests. As a result of the research, it has been determined that factors such as room, breakfast, cleaning, and hot water problems in Turkish comments on accommodation facilities in Turkey cause dislike and therefore low scores. It is seen that the factors affecting the high score are related to cleanliness and the interest of the staff. It is thought that this research will contribute to sector managers, entrepreneurs, and researchers in terms of knowing guest satisfaction, guest complaints, and factors related to satisfaction. The results obtained from this article, which deals with the analysis of guest reviews of accommodation establishments in Turkey by text mining, provide a valuable guide for improving the service quality and guest satisfaction of the sector. In addition, this study will guide entrepreneurs and academics in the hospitality industry by providing a basis for future research.

References

  • Ağca, Y. (2021a). Otel Oda Fiyatlarını Açıklamada Makine Öğrenmesi Algoritmalarının Kıyaslanması. İşletme Araştırmaları Dergisi, 450-463
  • Ağca, Y. (2021b). Alternatif Veri Elde Etme Yöntemi Web Madenciliği: Otel Oda Fiyatlarının Zamansal Analizi. Çanakkale Onsekiz Mart Üniversitesi Yönetim Bilimleri Dergisi, 19(42), 1013-1034
  • Ağca, Y. (2021c). R Programlama Dili ile İstatistiksel Analiz ve Veri Madenciliği. İstanbul: Cinius Yayınları. Akter, S. ve Wamba, S. F. (2016). Big data analytics in E-commerce: a systematic review and agenda. Electron Markets, 173-194.
  • Albayrak, T. ve Caber, M. (2015). Prioritisation of the hotel attributes according to their influence on satisfaction: a comparison of two techniques. Tourism Management, 46, 43-50.
  • Atabay, L. ve Cizel, B. (2020). Comparative Content Analysis of Hotel Reviews by Mass Tourism Destination. Journal of Tourism and Services, 11(21), 147-166.
  • Boice, M. (2021, 03 19). The 13 Top Reasons Consumers Shop Online. https://www.junglescout.com/blog/reasons-consumers-shop-online/ adresinden alındı
  • Chaffey, D. (2017, 11 19). The reasons why consumers shop online instead of in stores. https://www.smartinsights.com/ecommerce/ecommerce-strategy/the-reasons-why-consumers-shop-online-instead-of-in-stores/ adresinden alındı
  • Chanwisitkul, P., Shahgholian, A. ve Mehandjiev, N. (2018). The Reason Behind the Rating: Text Mining of Online Hotel Reviews. 2018 IEEE 20th Conference on Business Informatics (CBI). Vienna, Austria : IEEE.
  • Chevalier, S. (2021, 07 26). Main reasons why global consumers chose to shop online 2021. statista.com: https://www.statista.com/statistics/676358/reasons-online-shoppers-prefer-to-shop-online/ adresinden alındı
  • Chittiprolu, V., Samala, N. ve Bellamkonda, R. S. (2021). Heritage hotels and customer experience: a text mining analysis of online reviews. International Journal of Culture, Tourism and Hospitality Research, 15(2), 131-156.
  • Cui, G., Lui, H.-K. ve Guo, X. (2014). The Effect of Online Consumer Reviews on New Product Sales. International Journal of Electronic Commerce, 17(1), 39-58.
  • Davras, Ö. ve Caber, M. (2019). Analysis of hotel services by their symmetric and asymmetric effects on overall customer satisfaction: A comparison of market segments. International Journal of Hospitality Management, 81, 83-93.
  • Dominici, G. ve Guzzo, R. (2010). Customer Satisfaction in the Hotel Industry: A Case Study from Sicily. International Journal of Marketing Studies, 2(2), 3-12.
  • Ekinci, Y., Dawes, P. ve Massey, G. (2008). Document details - An extended model of the antecedents and consequences of consumer satisfaction for hospitality services. European Journal of Marketing, 42(1-2), 35-68.
  • Gaikwad, S. V., Chaugule, A. ve Patil, P. (2014). Text Mining Methods and Techniques. International Journal of Computer Applications, 85(17), 42-45.
  • Gündüz, C., ve Gündüz, S. (2017), Mesleki Eğitimde Toplam Kalite Yönetimi ve Verimlilik: Niksar Mesleki Eğitim Merkezi Üzerine Bir Uygulama. Sosyal Bilimler Dergisi / The Journal of Social Science. 4(11), 925-940.
  • Han, H. J., Mankad, S., Gavirneni, N. ve Verma, R. (2016). What Guests Really Think of Your Hotel: Text Analytics of Online Customer Reviews. Cornell Hospitality Report, 16(2), 1-19.
  • Hananto, A. (2015). Application of Text Mining to Extract Hotel Attributes and Construct Perceptual Map of Five Star Hotels from Online Review: Study of Jakarta and Singapore Five-Star Hotels. ASEAN Marketing Journal, 7(2), 58-80.
  • Henneberry, R. (2012, 06 20). How To Cash In On The 5 Reasons People Buy Products Online. https://www.crazyegg.com/blog/reasons-people-buy-products-online/ adresinden alındı
  • Hu, N., Bose, I., Koh, N. S. ve Liu, L. (2012). Manipulation of online reviews: An analysis of ratings, readability, and sentiments. Decision Support Systems, 52(3), 674-684.
  • Jeong, M. ve Jeon, M. M. (2008). Customer Reviews of Hotel Experiences through Consumer Generated Media (CGM). Journal of Hospitality & Leisure Marketing, 17(1-2), 121-138.
  • Keskinkılıç, M., Ağca, Y. ve Karaman, E. (2016). İnternet ve Bilgi Sistemleri Kullanımının Turizm Dağıtım Kanallarına Etkisi Üzerine Bir Uygulama. İşletme Araştırmaları Dergisi, 8(4), 445-472. doi:10.20491/isarder.2016.227
  • Kim, Y. A. ve Srivastava, J. (2007). Impact of social influence in e-commerce decision making. ICEC '07: Proceedings of the ninth international conference on Electronic commerce, (s. 293-302).
  • Kuhzady, S. ve Ghasemi, V. (2019). Factors Influencing Customers' Satisfaction and Dissatisfaction with Hotels: A Text-Mining Approach. Tourism Analysis, 24(1), 69-79.
  • Kumar, S. ve Aulia, A. (2021). The Influence of Hotel Review Towards E-Trust and Its Implication on Hotel Booking Intention. (S. Kumar, Dü.) Indonesia.
  • Lau, K.-N., Lee, K.-H. ve Ho, Y. (2015). Text Mining for the Hotel Industry. Cornell Hotel and Restaurant Administration Quarterly, 46(3), 344-362.
  • Melián-González, S., Bulchand-Gidumal, J. ve López-Valcárcel, B. G. (2013). Online Customer Reviews of Hotels: As Participation Increases, Better Evaluation Is Obtained. Cornell Hospitality Quarterly, 53(3), 274-283. Miner, G. D., Elder, J., Fast, A., Hill, T., Nisbet, R. ve Delen, D. (2012). Practical Text Mining and Statistical Analysis for Non-structured Text Data. Oxford, UK: Academic Press.
  • Nicholas, C. K.-W. ve Lee, A. S. (2017). Voice of Customers: Text Analysis of Hotel Customer Reviews. CBDR 2017 (s. 104-111). Osaka, Japan: Association for Computing Machinery.
  • O'Fallon, M. J. ve Rutherford, D. G. (2011). Hotel Management and Operations. New Jersey, US: John Wiley & Sons.
  • Öğüt, H. ve Taş, B. K. (2012). The influence of internet customer reviews on the online sales and prices in hotel industry. The Service Industries Journal, 32(2), 197-214.
  • Palese, B. ve Usai, A. (2018). The relative importance of service quality dimensions in E-commerce experiences. International Journal of Information Management, 40, 132-140.
  • Pradana, A. W. ve Hayaty, M. (2019). The effect of stemming and removal of stopwords on the accuracy of sentiment analysis on indonesian-language texts. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Journal, 4(4), 375-380.
  • Prajapati, V. (2021, 01 22). Why Do People Shop Online? https://www.techprevue.com/reasons-prefer-online-shopping/ adresinden alındı
  • Rai, A. (2019, 06 01). What is Text Mining: Techniques and Applications. upgrad.com: https://www.upgrad.com/blog/what-is-text-mining-techniques-and-applications/ adresinden alındı
  • Schuckert, M. (2015). Hospitality and Tourism Online Reviews: Recent Trends and Future Directions. Journal of Travel & Tourism Marketing, 32(5), 608-621.
  • Shen, Z., Yang, X., Liu, C. ve Li, J. (2021). Assessment of Indoor Environmental Quality in Budget Hotels Using Text-Mining Method: Case Study of Top Five Brands in China. Sustainability, 13(8).
  • Singh, P. K., Sachdeva, A., Mahajan, D., Pande, N. ve Sharma, A. (2014). An approach towards feature specific opinion mining and sentimental analysis across e-commerce websites. 5th International Conference - Confluence The Next Generation Information Technology Summit (Confluence). Noida, India : IEEE.
  • Somprasertsri, G. ve Lalitrojwong, P. (2010). Mining Feature-Opinion in Online Customer Reviews for Opinion Summarization. Journal of Universal Computer Science, 16(6), 938-955.
  • Trenz, M. ve Berger, B. (2013). Analyzing Online Customer Reviews - An Interdisciplinary Literature Review And Research Agenda. ECIS 2013 Completed Research, (s. 83).
  • Truyens, M. ve Eecke, P. V. (2014). Legal aspects of text mining. Comput. Law Secur. Rev., 2182-2186. Tyagi, N. (2021, 05 10). Top 7 Text Mining Techniques. analyticssteps.com: https://www.analyticssteps.com/blogs/top-7-text-mining-techniques adresinden alındı
  • Wei, P.-S. ve Lu, H.-P. (2013). An examination of the celebrity endorsements and online customer reviews influence female consumers’ shopping behavior. Computers in Human Behavior, 29(1), 193-201.
  • Weiss, S. M., Indurkhya, N. ve Zhang, T. (2015). Fundamentals of Predictive Text Mining (2 b.). New York, US: Springer.
  • Xu, X. ve Li, Y. (2016). The antecedents of customer satisfaction and dissatisfaction toward various types of hotels: A text mining approach. International Journal of Hospitality Management, 55, 57-69.
  • Zhan, J., Loh, H. T. ve Liu, Y. (2009). Gather customer concerns from online product reviews – A text summarization approach. Expert Systems with Applications, 36(2), 2107-2115.
There are 44 citations in total.

Details

Primary Language Turkish
Subjects Strategy, Management and Organisational Behaviour (Other)
Journal Section Articles
Authors

Yılmaz Ağca 0000-0002-5912-0977

Cemil Gündüz 0000-0002-0222-2497

Publication Date June 15, 2023
Published in Issue Year 2023

Cite

APA Ağca, Y., & Gündüz, C. (2023). Türkiye’deki Otel Konuk Yorumları ve Puanlarının Metin Madenciliği ile Analizi. Journal of Management and Economics, 30(2), 397-411. https://doi.org/10.18657/yonveek.1063592