COMPARATIVE ANALYSIS OF EMOTION ANALYSIS METHODS WITHIN THE SCOPE OF HOTEL USERS TURKISH AND ENGLISH COMMENTS

Öz

The aim of the research is to compare the sentiment analysis methods used to reveal and classify the emotional tendencies in Turkish and English comments of hotel users. Within purpose, classification algorithms such as Decision Tree and Random Forest from machine learning methods were used. The data was obtained from Tripadvisor tourism portal with web scraping/mining technique within the scope of this study, which shows quantitative research feature. A purposeful sampling method was used in this study. Emotion analysis, which is one of the text mining applications, was used to analyze the data. KNIME Analytics Platform was used in the data analysis process. As a result of the research, it was seen that the machine learning algorithms performed more effective classification than dictionary-based analysis. In addition, the machine learning algorithms produced more successful results in the Turkish language comments at the classification stage.

Anahtar Kelimeler

• User reviews
• data mining
• text mining
• emotion analysis
• TripAdvisor

TRIPADVISOR KULLANICILARININ TÜRKÇE VE İNGİLİZCE YORUMLARI KAPSAMINDA DUYGU ANALİZİ YÖNTEMLERİNİN KARŞILAŞTIRMALI ANALİZİ

Öz

Araştırmanın amacı, TripAdvisor kullanıcılarının Türkçe ve İngilizce yorumlarındaki duygusal eğilimlerin ortaya çıkarılması ve sınıflandırılmasında kullanılan duygu analizi yöntemlerini karşılaştırmaktır. Amaç kapsamında makine öğrenme yöntemlerinden Decision Tree, Random Forest gibi sınıflandırma algoritmaları kullanılmıştır. Nicel araştırma özelliği gösteren bu çalışma kapsamında veriler, TripAdvisor turizm portalından web kazıma tekniği ile elde edilmiştir. Amaçsal örnekleme yönteminin benimsendiği bu çalışmada verilerin analiz edilmesi sürecinde duygu analizi yöntemi kullanılmıştır. Veri analiz sürecinde açık kaynak kodlu KNİME veri madenciliği programından yararlanılmıştır. Araştırma neticesinde makine öğrenme algortimalarının sözlük tabanlı analize göre daha etkin sınıflandırma gerçekleştirdiği görülmüştür. Ayrıca makine öğrenme algortimaları sınıflandırma aşamasında Türkçe dilindeki yorumlarda daha başarılı sonuçlar üretmiştir.

Anahtar Kelimeler

• Kullanıcı yorumları
• veri madenciliği
• metin madenciliği
• duygu analizi
• TripAdvisor

Kaynakça

1. Ağca, Y. (2019), Çevrimiçi Seyahat Acentalarında Oda Fiyatlarına Etki Eden Faktörlerin Araştırılması (Yayınlanmamış Doktora Tezi), Erzurum: Atatürk Üniversitesi, Sosyal Bilimler Enstitüsü
2. Ağca, Y. (2021), Otel Oda Fiyatlarını Açıklamada Makine Öğrenmesi Algoritmalarının Kıyaslanması, İşletme Araştırmaları Dergisi, 13(1), s.450-463.
3. Alpar, R. (2010), Uygulamalı İstatistik ve Geçerlik-Güvenirlik: Spor, Sağlık ve Eğitim Bilimlerinden Örneklerle, Ankara: Detay Yayıncılık.
4. Asani, E., Vahdat-Nejad, H. ve Sadri, J. (2021), Restaurant Recommender System Based on Sentiment Analysis, Machine Learning with Applications, 6, s.100-114. https://doi.org/10.1016/J.MLWA.2021.100114
5. Ballantine, P. W., Lin, Y. ve Veer, E. (2015), The İnfluence of User Comments on Perceptions of Facebook Relationship Status Updates, Computers in Human Behavior, 49, s.50–55. https://doi.org/10.1016/J.CHB.2015.02.055
6. Berthold, M. R., Cebron, N., Dill, F., Gabriel, T. R., Kotter, T., Meinl, T., Ohl, P., Thiel, K. ve Wiswedel, B. (2009), KNIME-The Konstanz Information Miner Version 2.0 and Beyond, 11(1), s.26-31. https://doi.org/10.1145/1656274.1656280
7. Chen, Y., Liu, D., Liu, Y., Zheng, Y., Wang, B. ve Zhou, Y. (2022), Research on User Generated Content in Qvea System and Online Comments Based on Text Mining, Alexandria Engineering Journal, 61(10), s.7659–7668. https://doi.org/10.1016/J.AEJ.2022.01.020
8. Chatterjee, S. (2019), Explaining customer ratings and recommendations by combining qualitative and quantitative user generated contents, Decision Support Systems, 119, s. 14–22. https://doi.org/10.1016/J.DSS.2019.02.008

9. 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), s.131-156.
10. Dandıl, E. ve Karakurt, B. (2019), Sosyal Medya Uygulamalarında Kullanıcı Yorumlarının Metin Madenciliği ile Sınıflandırılması, International Congress on HumanComputer Interaction, Optimization and Robotic Applications, s.203–207.
11. Dean, J. (2014), Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners, New Jersey: John Wiley ve Sons.
12. Dehkharghani, R., Yanikoglu, B., Saygin, Y. ve Oflazer, K. (2016), Sentiment Analysis in Turkish at Different Granularity Levels, Natural Language Engineering, 23(4), s.535–559. https://doi.org/10.1017/S1351324916000309
13. Deng, S., Sinha, A. P. ve Zhao, H. (2017), Adapting Sentiment Lexicons to Domain-Specific Social Media Texts, Decision Support Systems, 94, s.65–76. https://doi.org/10.1016/J.DSS.2016.11.001
14. Dhar, S., & Bose, I. (2022), Walking on Air or Hopping Mad? Understanding the Impact of Emotions, Sentiments and Reactions on Ratings in Online Customer Reviews of Mobile Apps, Decision Support Systems. https://doi.org/10.1016/J.DSS.2022.113769
15. Flach, P. (2019). Performance Evaluation in Machine Learning: The Good, the Bad, the Ugly, and the Way Forward, The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), 33(1), s.9808–9814.
16. Ghimire, B., Shanaev, S., & Lin, Z. (2022), Effects of Official versus Online Review Ratings, Annals of Tourism Research, 92. https://doi.org/10.1016/J.ANNALS.2021.103247
17. Han, J., Kamber, M. ve Pei, J. (2012), Data Mining: Concepts and Techniques, Waltham: Morgan Kaufmann Publishers.
18. 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), s.58-80.
19. 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), s.445-472. doi:10.20491/isarder.2016.227
20. Köse, İ. (2018), Veri madenciliği: Teori, uygulama ve felsefesi, İstanbul: Papatya Yayın Eğitim.
21. Kuhzady, S. ve Ghasemi, V. (2019), Factors Influencing Customers' Satisfaction and Dissatisfaction with Hotels: A Text-Mining Approach, Tourism Analysis, 24(1), s.69-79.
22. Kulkarni, A., Chong, D. ve Batarseh, F. A. (2020), Foundations of Data İmbalance and Solutions for a Data Democracy, (Ed. Feras Batarseh ve Ruiib Yang), Data Democracy: At the Nexus of Artificial Intelligence, Software Development, and Knowledge Engineering, s.83-105, London:Academi Press.
23. Lau, K.-N., Lee, K.-H. ve Ho, Y. (2015), Text Mining for the Hotel Industry, Cornell Hotel and Restaurant Administration Quarterly, 46(3), s.344-362.
24. Li, H., Chen, Q., Zhong, Z., Gong, R. ve Han, G. (2022), E-word of Mouth Sentiment Analysis for User Behavior Studies, Information Processing ve Management, 59(1), s.1-12. https://doi.org/10.1016/J.IPM.2021.102784
25. Lin, H. C. K., Wang, T. H., Lin, G. C., Cheng, S. C., Chen, H. R. ve Huang, Y. M. (2020), Applying Sentiment Analysis to Automatically Classify Consumer Comments Concerning Marketing 4Cs Aspects, Applied Soft Computing, 97, s.1-9. https://doi.org/10.1016/J.ASOC.2020.106755
26. Li, X., Liu, H., & Zhu, B. (2020), Evolutive Preference Analysis with Online Consumer Ratings, Information Sciences, 541, 332–344. https://doi.org/10.1016/J.INS.2020.06.048
27. Linoff, G. S. ve Berry, M. J. (2011), Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management. Indiana, US: Wiley Publishing.
28. Medhat, W., Hassan, A. ve Korashy, H. (2014), Sentiment Analysis Algorithms and Applications: A survey, Ain Shams Engineering Journal, 5(4), s.1093–1113.
29. 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), s.274-283.
30. 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.
31. Oğuzlar, A. ve Kızılkaya, M. (2019), Metin Madenciliğinde Duygu Analizi: R Uygulamalı, Bursa: Dora Basım-Yayım.
32. Öğündür, G. (2019), Doğruluk (accuracy) , kesinlik (precision) , duyarlılık (recall) ya da F1 score ?, https://medium.com/@gulcanogundur/doğruluk-accuracy-kesinlik-precision-duyarlılık-recall-ya-da-f1-score-300c925feb38 (Erişim Tarihi: 17.02.2022)
33. Öğüt, H. ve Taş, B. K. (2012), The İnfluence of İnternet Customer Reviews on the Online Sales and Prices in Hotel İndustry, The Service Industries Journal, 32(2), s.197-214.
34. Polat, H. ve Öcal, D. (2020), Çoklu Medyada Ayak İzlerini Sürmek: Büyük Veri ve Yönetimi. (Ed. Derya Öcal ve Hıdır Polat), Dijital Reklamcılık, s. 99–130, Ankara: Nobel Akademik Yayıncılık.
35. 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), s.1-24.
36. Somprasertsri, G. ve Lalitrojwong, P. (2010), Mining Feature-Opinion in Online Customer Reviews for Opinion Summarization, Journal of Universal Computer Science, 16(6), s.938-955.
37. Sirisuriya, S. (2015), A Comparative Study on Web Scraping, Proceedings of 8th International Research Conference, s.135-140.
38. Taş, B. (2019), Roc Eğrisi ve Eğri Altında Kalan Alan (Auc), https://bernatas.medium.com/roc-eğrisi-ve-eğri-altında-kalan-alan-auc-97b058e8e0cf (Erişim Tarihi: 17.02.2022)
39. Tian, G., Lu, L., & McIntosh, C. (2021), What Factors Affect Consumers’ Dining Sentiments and Their Ratings: Evidence from Restaurant Online Review Data, Food Quality and Preference, 88. https://doi.org/10.1016/J.FOODQUAL.2020.104060
40. Trenz, M. ve Berger, B. (2013), Analyzing Online Customer Reviews - An Interdisciplinary Literature Review And Research Agenda, 21st European Conference on Information Systems (ECIS), Utrecht:Netherlands
41. Tyagi, N. (2021), Top 7 Text Mining Techniques, https://www.analyticssteps.com/blogs/top-7-text-mining-techniques, (Erişim Tarihi: 15.02.2022)
42. Wei, P.-S. ve Lu, H.-P. (2013), An Examination of the Celebrity Endorsements and Online Customer Reviews İnfluence Female Consumers’ Shopping Behavior, Computers in Human Behavior, 29(1), s.193-201.
43. Xiong, Z., Yan, Z., Yao, H., Moreno, J. G., Xiong, Z., Yan, Z., Yao, H. ve Liang, S. (2022). Design Demand Trend Acquisition Method Based on Short Text Mining of User Comments in Shopping Websites, Information 2022, 13(3), s.1-16. https://doi.org/10.3390/INFO13030110
44. 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, s.57-69.
45. 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), s.2107-2115.
46. Zhao, B. (2017), Web Scraping, https://www.researchgate.net/profile/Bo-Zhao-3/publication/317177787_Web_Scraping/links/5c293f85a6fdccfc7073192f/Web-Scraping.pdf (Erişim Tarihi: 13.02.2022).