International Journal of Advanced Culture Technology

The International Promotion Agency of Culture Technology (국제문화기술진흥원)
권호 표지
DOI QR코드

DOI QR Code

Prediction Model of Inclination to Visit Jeju Tourist Attractions based on CNN Deep Learning

  • YoungSang Kim (Dept. of Computer Multimedia, Cheju Halla Univ.)
  • Received : 2023.08.02
  • Accepted : 2023.08.29
  • Published : 2023.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

Sentiment analysis can be applied to all texts generated from websites, blogs, messengers, etc. The study fulfills an artificial intelligence sentiment analysis estimating visiting evaluation opinions (reviews) and visitor ratings, and suggests a deep learning model which foretells either an affirmative or a negative inclination for new reviews. This study operates review big data about Jeju tourist attractions which are extracted from Google from October 1st, 2021 to November 30th, 2021. The normalization data used in the propensity prediction modeling of this study were divided into training data and test data at a 7.5:2.5 ratio, and the CNN classification neural network was used for learning. The predictive model of the research indicates an accuracy of approximately 84.72%, which shows that it can upgrade performance in the future as evaluating its error rate and learning precision.

Keywords

References

  1. Jeju Tourism Association, Current status of tourist arrivals in February, http://www.visitjeju.or.kr/main.do , Feb. 2023.
  2. R. Harrill, "Residents' Attitudes toward Tourism Development: A Literature Review with Implications for Tourism Planning," Journal of Planning Literature, Vol. 18, No. 3, pp. 251-266, Feb. 2004. DOI: 0885412203260306  https://doi.org/10.1177/0885412203260306
  3. K.L. Andereck, G.P. Nyaupane, " Exploring the Nature of Tourism and Quality of Life Perceptions among Residents," Journal of Travel Research, Vol. 50, No. 3, pp. 248-260, May 2010. DOI:10.1177/0047287510362918 
  4. M. Chang, The Effects of Community Attachment and the Perceptions of Tourism Impacts on the Intentions of Pro-Environmental Behavior and Support for Tourism Development on Jeju Island, Ph.D. Thesis. Jeju National Univ., KOREA, Feb. 2021. 
  5. Ministry of Culture, Sports and Tourism, https://www.korea.kr/news/visualNewsView.do?newsId=148910670&sectIds=&cateId=&pageIndex=1&repCode_C=, January 7, 2023.
  6. S.S. Won, https://www.headlinejeju.co.kr/news/articleView.html?idxno=508865 , Headlinejeju, March 05, 2023. 
  7. T.S. Ko, B.J. Kim, J.W. Jwa, "Smart Tourism Information System and IoT Data Collection Devices for Location-based Tourism and Tourist Safety Services," International Journal of Advanced Culture Technology, Vol. 10, No. 2, pp. 310-316, 2022. DOI https://doi.org/10.17703/IJACT.2022.10.1.310 
  8. K. Puh, M.B. Babac, "Predicting Sentiment and rating of Tourist Reviews using machine Learning," Journal of Hospitality and Tourism Insights(JHTI), July 2022. DOI 10.1108/JHTI-02-2022-0078 
  9. M. Chu et al.,"Language Interpretation in travel Guidance Platform: Text Mining and Sentiment Analysis of TripAdvisor Reviews," Frontiers in Psychology, pp. 1-8, Oct. 2022. DOI 10.3389/fpsyg.2022.1029945 
  10. T.H. Hong, "Sentiment Analysis and Star Rating Prediction Based on Big Data Analysis of Online Reviews of Foreign Tourists Visting Korea," Knowledge Management Research, Vol. 23, No.1, Mar. 2022. https://doi.org/10.15813/kmr.2022.23.1.010 
  11. Y.J. Park, "A Study on the Sentiment Analysis of Contemporary Pop Musicians and Classical Music Composers," International Journal of Advanced Culture Technology(IJACT), Vol. 10, No. 3, pp. 352-359, Sep. 2022. https://doi.org/10.17703/IJACT.2022.10.3.352 
  12. Y.S. Kim, "A Study on Sentiment Analysis of Jeju Tourist Destination Using Review Big Data with the Machine Learning," in Smart Convergence of Culture Technology Letters(SCCTL 09), pp. 59-61, Jul. 2023. 
  13. H. Taherdoost, M. Madanchian, "Artificial Intelligence and Sentiment Analysis:A Review in Competitive Research," Computers, Vol. 12, No. 37, Feb. 2023. https://doi.org/10.3390/computers12020037 
  14. A. Ahad et al., " An Intelligent Data Mining System for Tweet Opinion Analysis using Combined Cluster based Classification Approach," International Journal of Intelligent Systems and Applications in Engineering(IJISAE), Vol. 10, No. 4, pp. 604-609, Dec. 2022. https://ijisae.org/index.php/IJISAE/article/view/2330 
  15. M.A. Palomino, F.Aider, "Evaluating the Effectiveness of Text Pre-Processing in Sentiment Analysis," Applied Sciencies , Vol. 12. No.17, Aug. 2022. DOI 10.3390/app12178765 
  16. T. Susnjak, "Applying BERT and ChatGPT for Sentiment Analysis of Lyme Diease in Scientific Literature", Computation and Language(cs. CL), Cornell University, Feb. 2023. https://doi.org/10.48550/arXiv.2302.06474 
  17. K. Saitoh, Deep Learning from Scratch, Hanbit Media, 2017. 
  18. Y. Kim, "Convolutional Neural Networks for Sentence Classification," in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing(EMNLP), pp. 1746-1751, Oct. 2014.