Knowledge Management Research (지식경영연구)

The Knowledge Management Society of Korea (한국지식경영학회)
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Sentiment Analysis and Star Rating Prediction Based on Big Data Analysis of Online Reviews of Foreign Tourists Visiting Korea

방한 관광객의 온라인 리뷰에 대한 빅데이터 분석 기반의 감성분석 및 평점 예측모형

  • Hong, Taeho (Pusan National University College of Business Administration)
  • Received : 2022.02.24
  • Accepted : 2022.03.18
  • Published : 2022.03.31
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Abstract

Online reviews written by tourists provide important information for the management and operation of the tourism industry. The star rating of online reviews is a simple quantitative evaluation of a product or service, but it is difficult to reflect the sincere attitude of tourists. There is also an issue; the star rating and review content are not matched. In this study, a star rating prediction model based on online review content was proposed to solve the discrepancy problem. We compared the differences in star ratings and sentiment by continent through sentiment analysis on tourist attractions and hotels written by foreign tourists who visited Korea. Variables were selected through TF-IDF vectorization and sentiment analysis results. Logit, artificial neural network, and SVM(Support Vector Machine) were used for the classification model, and artificial neural network and SVR(Support Vector regression) were applied for the rating prediction model. The online review rating prediction model proposed in this study could solve inconsistency problems and also could be applied even if when there is no star rating.

관광객이 작성한 온라인 리뷰는 관광산업의 관리 및 운영에 중요한 정보를 제공한다. 평점은 제품이나 서비스에 대한 정량적인 평가로 간편하지만 관광객의 진실한 태도를 반영하기 어려우며 평점과 리뷰내용에 대한 불일치 문제도 발생하고 있다. 불일치 문제는 잠재고객에게 혼동을 줄 수 있으며 구매의사결정에도 영향을 미칠 수 있다. 본 연구에서는 온라인 리뷰기반의 평점 예측모형을 통해 평점과 리뷰내용의 불일치 문제를 해결하고자 한다. 한국을 방문한 외국인 관광객이 작성한 관광지와 호텔에 대한 리뷰의 감성분석을 통해 평점과 감성의 차이를 비교하고 TF-IDF vectorization과 감성분석 결과로 변수를 선정하였다. 로짓, 인공신경망, SVM(Support Vector Machine)을 적용하여 평점을 분류하고, 인공신경망, SVR(Support Vector Regression)을 통해 평점을 예측하였다. 평점 분류모형과 예측모형 모두 불일치한 리뷰를 제거하고 감성분석을 반영한 모형에서 우수한 성과를 보여주었다. 본 연구에서 제안한 온라인 리뷰 기반의 평점 예측모형은 평점과 리뷰내용에 대한 불일치 문제를 해결하여 신뢰할 수 있는 정보를 제공하였으며 평점이 없는 온라인 리뷰에도 활용할 수 있을 것이다.

Keywords

Acknowledgement

이 논문은 2010년도 부산대학교 인문사회연구기금의 지원을 받아 연구되었음

References

  1. 김은미 (2021). 감성분석을 이용한 뉴스정보와 딥러닝 기반의 암호화폐 수익률 변동 예측을 위한 통합모형. 지식경영연구, 22(2), 19-32. https://doi.org/10.15813/KMR.2021.22.2.002
  2. 문화체육관광부 (2021). 2020 외래관광객조사.
  3. 야오즈옌, 김은미, 홍태호 (2020). 온라인 리뷰의 텍스트 마이닝에 기반한 외국인 관광객의 문화적 특성 연구. 정보시스템연구, 29(4), 171-191.
  4. 야오즈옌, 박지영, 홍태호 (2021). 레스토랑의 온라인 리뷰를 통해 감성과 감정이 리뷰 유용성에 미치는 영향에 관한 연구. 지식경영연구, 22(1), 243-267. https://doi.org/10.15813/KMR.2021.22.1.012
  5. 이주민, 방정혜 (2020). 화장품 회사의 브랜드컨셉 개발 사례분석. 지식경영연구, 21(3), 215-228. https://doi.org/10.15813/KMR.2020.21.3.012
  6. Ahani, A., Nilashi, M., Ibrahim, O., Sanzogni, L., & Weaven, S. (2019). Market segmentation and travel choice prediction in Spa hotels through TripAdvisor's online reviews. International Journal of Hospitality Management, 80, 52-77. https://doi.org/10.1016/j.ijhm.2019.01.003
  7. Al Ajrawi, S., Agrawal, A., Mangal, H., Putluri, K., Reid, B., Hanna, G., & Sarkar, M. (2021). Evaluating business Yelp's star ratings using sentiment analysis. Materials Today: Proceedings.
  8. Bilal, M., Marjani, M., Hashem, I. A. T., Malik, N., Lali, M. I. U., & Gani, A. (2021). Profiling reviewers' social network strength and predicting the "Helpfulness" of online customer reviews. Electronic Commerce Research and Applications, 45, 101026. https://doi.org/10.1016/j.elerap.2020.101026
  9. Furner, C. P., & Zinko, R. A. (2017). The influence of information overload on the development of trust and purchase intention based on online product reviews in a mobile vs. web environment: An empirical investigation. Electronic Markets, 27(3), 211-224. https://doi.org/10.1007/s12525-016-0233-2
  10. Guo, Y., Barnes, S. J., & Jia, Q. (2017). Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation. Tourism Management, 59, 467-483. https://doi.org/10.1016/j.tourman.2016.09.009
  11. Hu, N., Bose, I., Koh, N. S., & Liu, L. (2012). Manipulation of online reviews: An analysis of ratings, readability, and sentiments. Decision Support Systems, 52(3), 674-684. https://doi.org/10.1016/j.dss.2011.11.002
  12. Hu, Y. H., Chen, K., & Lee, P. J. (2017). The effect of user-controllable filters on the prediction of online hotel reviews. Information & Management, 54(6), 728-744. https://doi.org/10.1016/j.im.2016.12.009
  13. Kim, K., Park, O. J., Yun, S., & Yun, H. (2017). What makes tourists feel negatively about tourism destinations? Application of hybrid text mining methodology to smart destination management. Technological Forecasting and Social Change, 123, 362-369. https://doi.org/10.1016/j.techfore.2017.01.001
  14. Krishnamoorthy, S. (2015). Linguistic features for review helpfulness prediction. Expert Systems with Applications, 42(7), 3751-3759. https://doi.org/10.1016/j.eswa.2014.12.044
  15. Lee, P. J., Hu, Y. H., & Lu, K. T. (2018). Assessing the helpfulness of online hotel reviews: A classification-based approach. Telematics and Informatics, 35(2), 436-445. https://doi.org/10.1016/j.tele.2018.01.001
  16. Luo, Y., & Xu, X. (2021). Comparative study of deep learning models for analyzing online restaurant reviews in the era of the COVID-19 pandemic. International Journal of Hospitality Management, 94, 102849. https://doi.org/10.1016/j.ijhm.2020.102849
  17. Mahadzir, N. H., Omar, N. F., NawiM, N. M., Salameh, A. A., & Hussin, K. C. (2021). Sentiment analysis of code-mixed text: A review. Turkish Journal of Computer and Mathematics Education, 12(3), 2469-2478.
  18. Prameswari, P., Surjandari, I., & Laoh, E. (2017, October). Opinion mining from online reviews in Bali tourist area. In 2017 3rd International Conference on Science in Information Technology (ICSITech), IEEE, 226-230.
  19. Quintal, V. A., Lee, J. A., & Soutar, G. N. (2010). Risk, uncertainty and the theory of planned behavior: A tourism example. Tourism Management, 31(6), 797-805. https://doi.org/10.1016/j.tourman.2009.08.006
  20. Ravi, K., & Ravi, V. (2015). A survey on opinion mining and sentiment analysis: Tasks, approaches and applications. Knowledge-based Systems, 89, 14-46. https://doi.org/10.1016/j.knosys.2015.06.015
  21. Sharma, A., Park, S., & Nicolau, J. L. (2020). Testing loss aversion and diminishing sensitivity in review sentiment. Tourism Management, 77, 104020. https://doi.org/10.1016/j.tourman.2019.104020
  22. Sotiridais, M. D., & Van Zyl, C. (2013). Electronic word-of-mouth and online reviews in tourism services: The use of twitter by tourists. Electronic Commerce Research, 13(1), 103-124. https://doi.org/10.1007/s10660-013-9108-1
  23. Swar, B., Hameed, T., & Reychav, I. (2017). Information overload, psychological ill-being, and behavioral intention to continue online healthcare information search. Computers in Human Behavior, 70, 416-425. https://doi.org/10.1016/j.chb.2016.12.068
  24. Tay, F. E. H., & Cao, L. J. (2001). Application of support vector machines in financial time series forecasting. Omega, 29(4), 309-317. https://doi.org/10.1016/S0305-0483(01)00026-3
  25. Tsai, C. F., Chen, K., Hu, Y. H., & Chen, W. K. (2020). Improving text summarization of online hotel reviews with review helpfulness and sentiment. Tourism Management, 80, 104122. https://doi.org/10.1016/j.tourman.2020.104122
  26. Vapnik, V. (1995). The nature of statistical learning theory. New York: Springer-Verlag.
  27. Vapnik, V., Golowich, S., & Smola, A. (1996). Support vector method for function approximation, regression estimation and signal processing. Advances in Neural Information Processing Systems, 9.
  28. Wu, J. J., & Chang, S. T. (2020). Exploring customer sentiment regarding online retail services: A topic-based approach. Journal of Retailing and Consumer Services, 55, 102145.
  29. Xiang, Z., Du, Q., Ma, Y., & Fan, W. (2017). A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism. Tourism Management, 58, 51-65. https://doi.org/10.1016/j.tourman.2016.10.001
  30. Zang, G., Hu, M. Y., Patuwo, B. E., & Indro, D. C. (1999). Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis. European Journal of Operational Research, 116, 16-32. https://doi.org/10.1016/S0377-2217(98)00051-4
  31. Zhang, Y., & Lin, Z. (2018). Predicting the helpfulness of online product reviews: A multilingual approach. Electronic Commerce Research and Applications, 27, 1-10. https://doi.org/10.1016/j.elerap.2017.10.008
  32. Zheng, T., Wu, F., Law, R., Qiu, Q., & Wu, R. (2021). Identifying unreliable online hospitality reviews with biased user-given ratings: A deep learning forecasting approach. International Journal of Hospitality Management, 92, 102658. https://doi.org/10.1016/j.ijhm.2020.102658