Conference Paper
PDF EndNote BibTex RIS Cite

DUYGU ANALİZİ İLE OTEL MÜŞTERİLERİNİN YILDIZ DEĞERLENDİRMELERİNİN TAHMİNİ

Year 2020, Volume 6, Issue 1, 86 - 95, 15.06.2020

Abstract

Bu çalışmanın üç farklı amacı vardır; oteller ve derecelendirmeleri hakkında bazı bilgileri keşfetmek, müşterilerin eWOM yorumları için duygu analizi ile yıldız derecelendirmelerini tahmin etmek ve bu eWOM yıldız derecelendirmelerini istatistiksel analizlerle söz konusu yorumlara karşılık gelen çevrimiçi yıldız derecelendirmeleriyle karşılaştırmaktır. Farklı gelir seviyesine sahip dört ildeki otel verileri PHP ile yazılmış bir program kullanılarak toplanmıştır. Veriler çeşitli kurallar göz önünde bulundurularak temizlenip, analizlere hazır hale getirilmiştir. Lexalytics aracının akademik demo sürümü, duyarlılık analizi, RStudio ise istatistik analizler için kullanılmıştır. Sonuçlar, otel başına ortalama oda sayısının ve günlük ortalama otel ücretlerinin illerin gelir düzeyi arttıkça arttığını, otellerin yıldız puanlarının ise illerin gelir düzeyi azaldıkça arttığını göstermektedir. Analizler tüm iller için eWOM ve çevrimiçi yıldız derecelendirmeleri arasında anlamlı bir fark olduğunu ve ayrıca tüm iller için bu iki yıldız derecelendirme arasında anlamlı ve orta düzeyde bir pozitif ilişki olduğunu göstermektedir.

References

  • Ahmad, M., Aftab, S., Ali, I., & Hameed, N. (2017). Hybrid tools and techniques for sentiment analysis: a review. Int. J. Multidiscip. Sci. Eng, 8(3).
  • Alaei, A. R., Becken, S., & Stantic, B. (2019). Sentiment analysis in tourism: capitalizing on big data. Journal of Travel Research, 58(2), 175-191.
  • Dave, K., Lawrence, S., & Pennock, D. M. (2003, May). Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proceedings of the 12th International Conference on World Wide Web (pp. 519-528). ACM.
  • Fu, Y., Hao, J. X., Li, X., & Hsu, C. H. (2019). Predictive Accuracy of Sentiment Analytics for Tourism: A Metalearning Perspective on Chinese Travel News. Journal of Travel Research, 58(4), 666-679.
  • García, A., Gaines, S., & Linaza, M. T. (2012). A lexicon based sentiment analysis retrieval system for tourism domain. Expert Syst Appl Int J, 39(10), 9166-9180.
  • Gitto, S., & Mancuso, P. (2017). Improving airport services using sentiment analysis of the websites. Tourism Management Perspectives, 22, 132-136.
  • Hailong, Z., Wenyan, G., & Bo, J. (2014, September). Machine learning and lexicon based methods for sentiment classification: A survey. In 2014 11th Web Information System and Application Conference (pp. 262-265). IEEE.
  • Kirilenko, A. P., Stepchenkova, S. O., Kim, H., & Li, X. (2018). Automated sentiment analysis in tourism: Comparison of approaches. Journal of Travel Research, 57(8), 1012-1025.
  • Lak, P., & Turetken, O. (2014, January). Star ratings versus sentiment analysis--a comparison of explicit and implicit measures of opinions. In 2014 47th Hawaii International Conference on System Sciences (pp. 796-805). IEEE.
  • Lexalytics (2019). Freqeuntly asked questions. http://dev.lexalytics.com/wiki/pmwiki.php?n=Main.FAQ, (29 July 2019) Liu, B. (2009). Handbook chapter: Sentiment analysis and subjectivity. Handbook of Natural Language Processing. Marcel Dekker, Inc. New York, NY, USA.
  • Malandrakis, N., Kazemzadeh, A., Potamianos, A., & Narayanan, S. (2013, June). SAIL: A hybrid approach to sentiment analysis. In Second Joint Conference on Lexical and Computational Semantics (* SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), pp. 438-442.
  • Neviarouskaya, A., Prendinger, H., & Ishizuka, M. (2015). Attitude sensing in text based on a compositional linguistic approach. Computational Intelligence, 31(2), 256-300.
  • Park, E., Kang, J., Choi, D., & Han, J. (2018). Understanding customers' hotel revisiting behaviour: a sentiment analysis of online feedback reviews. Current Issues in Tourism, 1-7.
  • Prabowo, R., & Thelwall, M. (2009). Sentiment analysis: A combined approach. Journal of Informetrics, 3(2), 143-157. Rambocas, M., & Pacheco, B. G. (2018). Online sentiment analysis in marketing research: a review. Journal of Research in Interactive Marketing, 12(2), 146-163.
  • Roy, A., Guria, S., Halder, S., Banerjee, S., & Mandal, S. (2018). Summarizing Opinions with Sentiment Analysis from Multiple Reviews on Travel Destinations. International Journal of Synthetic Emotions (IJSE), 9(2), 111-120.
  • Sommar, F., & Wielondek, M. (2015). Combining Lexicon-and Learning-based Approaches for Improved Performance and Convenience in Sentiment Classification.
  • Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational Linguistics, 37(2), 267-307.
  • Thelwall, M. (2019). Sentiment Analysis for Tourism. In Big Data and Innovation in Tourism, Travel, and Hospitality (pp. 87-104). Springer, Singapore.
  • Vohra, S. M., & Teraiya, J. B. (2013). A comparative study of sentiment analysis techniques. Journal JIKRCE, 2(2), 313-317. WB (2019). Country Classification: World Bank Country and Lending Groups.
  • https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups, (24 July 2019)

PREDICTION OF STAR RATINGS OF HOTEL CUSTOMERS USING SENTIMENT ANALYSIS

Year 2020, Volume 6, Issue 1, 86 - 95, 15.06.2020

Abstract

The aim of this study is three-fold; access some exploratory findings about hotels and their ratings, predict star ratings of customers by sentiment analysis using their eWOM comments, and compare predicted eWOM star ratings of customers with the corresponding online star ratings by statistical analyses. Data of hotels from four cities with different income levels are retrieved using a script written with PHP. The data are cleaned considering various rules to be ready for analyses. Academic demo version of Lexalytics tool is used for sentiment analysis and RStudio for statistical analyses. Results show that, average number of rooms per hotel and their daily average rates increase as the income level of cities increase whereas star ratings of hotels increase as the income level of cities decrease. Analyses show that that for all cities, there is a significant difference between eWOM and online star ratings and also a significant moderate positive relationship between these two star ratings for all cities.

References

  • Ahmad, M., Aftab, S., Ali, I., & Hameed, N. (2017). Hybrid tools and techniques for sentiment analysis: a review. Int. J. Multidiscip. Sci. Eng, 8(3).
  • Alaei, A. R., Becken, S., & Stantic, B. (2019). Sentiment analysis in tourism: capitalizing on big data. Journal of Travel Research, 58(2), 175-191.
  • Dave, K., Lawrence, S., & Pennock, D. M. (2003, May). Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proceedings of the 12th International Conference on World Wide Web (pp. 519-528). ACM.
  • Fu, Y., Hao, J. X., Li, X., & Hsu, C. H. (2019). Predictive Accuracy of Sentiment Analytics for Tourism: A Metalearning Perspective on Chinese Travel News. Journal of Travel Research, 58(4), 666-679.
  • García, A., Gaines, S., & Linaza, M. T. (2012). A lexicon based sentiment analysis retrieval system for tourism domain. Expert Syst Appl Int J, 39(10), 9166-9180.
  • Gitto, S., & Mancuso, P. (2017). Improving airport services using sentiment analysis of the websites. Tourism Management Perspectives, 22, 132-136.
  • Hailong, Z., Wenyan, G., & Bo, J. (2014, September). Machine learning and lexicon based methods for sentiment classification: A survey. In 2014 11th Web Information System and Application Conference (pp. 262-265). IEEE.
  • Kirilenko, A. P., Stepchenkova, S. O., Kim, H., & Li, X. (2018). Automated sentiment analysis in tourism: Comparison of approaches. Journal of Travel Research, 57(8), 1012-1025.
  • Lak, P., & Turetken, O. (2014, January). Star ratings versus sentiment analysis--a comparison of explicit and implicit measures of opinions. In 2014 47th Hawaii International Conference on System Sciences (pp. 796-805). IEEE.
  • Lexalytics (2019). Freqeuntly asked questions. http://dev.lexalytics.com/wiki/pmwiki.php?n=Main.FAQ, (29 July 2019) Liu, B. (2009). Handbook chapter: Sentiment analysis and subjectivity. Handbook of Natural Language Processing. Marcel Dekker, Inc. New York, NY, USA.
  • Malandrakis, N., Kazemzadeh, A., Potamianos, A., & Narayanan, S. (2013, June). SAIL: A hybrid approach to sentiment analysis. In Second Joint Conference on Lexical and Computational Semantics (* SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), pp. 438-442.
  • Neviarouskaya, A., Prendinger, H., & Ishizuka, M. (2015). Attitude sensing in text based on a compositional linguistic approach. Computational Intelligence, 31(2), 256-300.
  • Park, E., Kang, J., Choi, D., & Han, J. (2018). Understanding customers' hotel revisiting behaviour: a sentiment analysis of online feedback reviews. Current Issues in Tourism, 1-7.
  • Prabowo, R., & Thelwall, M. (2009). Sentiment analysis: A combined approach. Journal of Informetrics, 3(2), 143-157. Rambocas, M., & Pacheco, B. G. (2018). Online sentiment analysis in marketing research: a review. Journal of Research in Interactive Marketing, 12(2), 146-163.
  • Roy, A., Guria, S., Halder, S., Banerjee, S., & Mandal, S. (2018). Summarizing Opinions with Sentiment Analysis from Multiple Reviews on Travel Destinations. International Journal of Synthetic Emotions (IJSE), 9(2), 111-120.
  • Sommar, F., & Wielondek, M. (2015). Combining Lexicon-and Learning-based Approaches for Improved Performance and Convenience in Sentiment Classification.
  • Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational Linguistics, 37(2), 267-307.
  • Thelwall, M. (2019). Sentiment Analysis for Tourism. In Big Data and Innovation in Tourism, Travel, and Hospitality (pp. 87-104). Springer, Singapore.
  • Vohra, S. M., & Teraiya, J. B. (2013). A comparative study of sentiment analysis techniques. Journal JIKRCE, 2(2), 313-317. WB (2019). Country Classification: World Bank Country and Lending Groups.
  • https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups, (24 July 2019)

Details

Primary Language English
Subjects Social
Journal Section Articles
Authors

Selçuk KIRAN> (Primary Author)
MARMARA ÜNİVERSİTESİ
0000-0001-6088-2701
Türkiye


Meltem ÖZTURAN>
BOĞAZİÇİ ÜNİVERSİTESİ
Türkiye

Publication Date June 15, 2020
Published in Issue Year 2020, Volume 6, Issue 1

Cite

Bibtex @conference paper { ybs653870, journal = {Yönetim Bilişim Sistemleri Dergisi}, issn = {2630-550X}, eissn = {2630-550X}, address = {}, publisher = {Vahap TECİM}, year = {2020}, volume = {6}, number = {1}, pages = {86 - 95}, title = {PREDICTION OF STAR RATINGS OF HOTEL CUSTOMERS USING SENTIMENT ANALYSIS}, key = {cite}, author = {Kıran, Selçuk and Özturan, Meltem} }
APA Kıran, S. & Özturan, M. (2020). PREDICTION OF STAR RATINGS OF HOTEL CUSTOMERS USING SENTIMENT ANALYSIS . Yönetim Bilişim Sistemleri Dergisi , 6 (1) , 86-95 . Retrieved from https://dergipark.org.tr/en/pub/ybs/issue/54333/653870
MLA Kıran, S. , Özturan, M. "PREDICTION OF STAR RATINGS OF HOTEL CUSTOMERS USING SENTIMENT ANALYSIS" . Yönetim Bilişim Sistemleri Dergisi 6 (2020 ): 86-95 <https://dergipark.org.tr/en/pub/ybs/issue/54333/653870>
Chicago Kıran, S. , Özturan, M. "PREDICTION OF STAR RATINGS OF HOTEL CUSTOMERS USING SENTIMENT ANALYSIS". Yönetim Bilişim Sistemleri Dergisi 6 (2020 ): 86-95
RIS TY - JOUR T1 - PREDICTION OF STAR RATINGS OF HOTEL CUSTOMERS USING SENTIMENT ANALYSIS AU - SelçukKıran, MeltemÖzturan Y1 - 2020 PY - 2020 N1 - DO - T2 - Yönetim Bilişim Sistemleri Dergisi JF - Journal JO - JOR SP - 86 EP - 95 VL - 6 IS - 1 SN - 2630-550X-2630-550X M3 - UR - Y2 - 2019 ER -
EndNote %0 Yönetim Bilişim Sistemleri Dergisi PREDICTION OF STAR RATINGS OF HOTEL CUSTOMERS USING SENTIMENT ANALYSIS %A Selçuk Kıran , Meltem Özturan %T PREDICTION OF STAR RATINGS OF HOTEL CUSTOMERS USING SENTIMENT ANALYSIS %D 2020 %J Yönetim Bilişim Sistemleri Dergisi %P 2630-550X-2630-550X %V 6 %N 1 %R %U
ISNAD Kıran, Selçuk , Özturan, Meltem . "PREDICTION OF STAR RATINGS OF HOTEL CUSTOMERS USING SENTIMENT ANALYSIS". Yönetim Bilişim Sistemleri Dergisi 6 / 1 (June 2020): 86-95 .
AMA Kıran S. , Özturan M. PREDICTION OF STAR RATINGS OF HOTEL CUSTOMERS USING SENTIMENT ANALYSIS. Yönetim Bilişim Sistemleri Dergisi. 2020; 6(1): 86-95.
Vancouver Kıran S. , Özturan M. PREDICTION OF STAR RATINGS OF HOTEL CUSTOMERS USING SENTIMENT ANALYSIS. Yönetim Bilişim Sistemleri Dergisi. 2020; 6(1): 86-95.
IEEE S. Kıran and M. Özturan , "PREDICTION OF STAR RATINGS OF HOTEL CUSTOMERS USING SENTIMENT ANALYSIS", Yönetim Bilişim Sistemleri Dergisi, vol. 6, no. 1, pp. 86-95, Jun. 2020