single-dr.php

JDR Vol.16 No.1 pp. 24-30
(2021)
doi: 10.20965/jdr.2021.p0024

Paper:

Twitter Sentiment Analysis of Bangkok Tourism During COVID-19 Pandemic Using Support Vector Machine Algorithm

Thanapat Sontayasara*, Sirawit Jariyapongpaiboon*, Arnon Promjun*, Napat Seelpipat*, Kumpol Saengtabtim*, Jing Tang**,***, and Natt Leelawat*,***,†

*Department of Industrial Engineering, Faculty of Engineering, Chulalongkorn University
254 Phayathai Road, Pathumwan, Bangkok 10330, Thailand

Corresponding author

**International School of Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand

***Disaster and Risk Management Information Systems Research Group, Chulalongkorn University, Bangkok, Thailand

Received:
October 30, 2020
Accepted:
December 26, 2020
Published:
January 30, 2021
Keywords:
COVID-19, sentiment analysis, Bangkok, tourism, support vector machine
Abstract

In the year 2020, SARS-CoV-2, the virus behind the coronavirus disease (COVID-19) pandemic, affected many lives and businesses worldwide. COVID-19, which originated in Wuhan City, China, at the end of December 2019, spread over the entire world in approximately four months. By October 2020, approximately 20 million people were infected and millions had died from this disease. Many health organizations such as the World Health Organization and Centers for Disease Control and Prevention made COVID-19 their primary focus. Many industries, especially, the tourism industry, were affected by the pandemic as many flight and hotel reservations were canceled. Thailand, a country considered one of the world’s most popular tourist destinations, suffered much losses because of this pandemic. Many events and travel bookings were canceled and/or postponed. Many people expressed their views and emotions related to this situation over social media, which is considered a powerful media for spreading news and information. In this research, the views of people who were planning to travel to Bangkok, the capital city and most popular destination in Thailand, were retrieved from Twitter for the dates between April 3 and 30, 2020, the period during which the country underwent nationwide lockdown. Sentiment analysis was performed using the support vector machine algorithm. The results showed 71.03% classification accuracy based on three sentiment classifications: positive, negative, and neutral. This study could thus provide an insight into travelers’ opinions and sentiments related to the tourism business. Based on the significant terms in each sentiment extracted, strengths and weaknesses of each tourism issue could be obtained, which could be used for making recommendations to the related tourism organizations.

Cite this article as:
Thanapat Sontayasara, Sirawit Jariyapongpaiboon, Arnon Promjun, Napat Seelpipat, Kumpol Saengtabtim, Jing Tang, and Natt Leelawat, “Twitter Sentiment Analysis of Bangkok Tourism During COVID-19 Pandemic Using Support Vector Machine Algorithm,” J. Disaster Res., Vol.16, No.1, pp. 24-30, 2021.
Data files:
References
  1. [1] S. Chunhakasikarn, “Legal implications of COVID-19 disruption for employers in Thailand,” Bankok Post, https://www.bangkokpost.com/business/1894865/legal-implications-of-covid-19-disruption-for-employers-in-thailand [accessed October 1, 2020].
  2. [2] NESDC, “NESDC Economic Report: Thai Economic Performance in Q2 and Outlook for 2020,” https://www.nesdc.go.th/nesdb_en/article_attach/article_file_20200827153114.pdf [accessed October 1, 2020].
  3. [3] P.-W. Liang and B.-R. Dai, “Opinion mining on social media data,” Proc. of the 2013 IEEE 14th Int. Conf. on Mobile Data Management, Vol.2, pp. 91-96, 2013.
  4. [4] K. Meechang, N. Leelawat, J. Tang, A. Kodaka, and C. Chintanapakdee, “The acceptance of using information technology for disaster risk management: A systematic review,” Engineering J., Vol.24, No.4, pp. 111-132, 2020.
  5. [5] D. Gonzalez-Marron, D. Mejia-Guzman, and A. Enciso-Gonzalez, “Exploiting data of the Twitter social network using sentiment analysis,” Applications for Future Internet, Lecture Notes of the Institute for Computing Sciences, Social Informatics and Telecommunications Engineering, Vol.179, pp. 35-38, 2017.
  6. [6] A. Java, X. Song, T. Finin, and B. Tseng, “Why we twitter: understanding microblogging usage and communities,” Proc. fo the 9th WebKDD and the 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis, pp. 56-65, 2007.
  7. [7] J. Xue, J. Chen, R. Hu, C. Chen, C. Zheng, Y. Su, and T. Zhu, “Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach,” J. Med. Internet Res., Vol.22, No.11, Article No.e20550, 2020.
  8. [8] E. Chen, K. Lerman, and E. Ferrara, “Covid-19: The first public coronavirus twitter dataset,” https://github.com/echen102/COVID-19-TweetIDs [accessed June 30, 2020].
  9. [9] N. Leelawat, J. Tang, K. Saengtabtim, and A. Laosunthara, “Trends of tweets on the Coronavirus Disease-2019 (COVID-19),” J. Disaster Res., Vol.15, No.4, pp. 530-533, doi: 10.20965/jdr.2020.p0530, 2020.
  10. [10] R. T. R. Qiu, J. Park, S. Li, and H. Song, “Social costs of tourism during the COVID-19 pandemic,” Ann. Tour. Res., Vol.84, Article No.102994, 2020.
  11. [11] A. Sharma and J. L. Nicolau, “An open market valuation of the effects of COVID-19 on the travel and tourism industry,” Ann. Tour. Res., Vol.83, Article No.102990, 2020.
  12. [12] Ministry of Tourism and Sport, “International Tourist Arrivals to Thailand 2020 (Jan–Sep),” https://www.mots.go.th/more_news_new.php?cid=599 (in Thai) [accessed October 24, 2020]
  13. [13] L. A. Birnbaum (Ed.), “Machine Learning Proc. 1993: Proc. of the Tenth Int. Conf. on Machine Learning, University of Massachusetts, Amherst, June 27–29, 1993,” Morgan Kaufmann, 1993.
  14. [14] M. Ahmad, S. Aftab, and I. Ali, “Sentiment analysis of tweets using svm,” Int. J. Comput. Appl, Vol.177, No.5, pp. 25-29, 2017.
  15. [15] S. S. Satchidananda and J. B. Simha, “Comparing decision trees with logistic regression for credit risk analysis,” SAS APAUGC 2006 Mumbai, 2006.
  16. [16] X. Zhang, X. liu, Q. Shi, X.-Q. Xu, H.-C. W. Leung, L. N. Harris, J. D. iglehart, A. Miron, J. S. Liu, and W. H. Wong, “Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data,” BMC Bioinform., Vol.7, No.1, 2006.
  17. [17] D. J. Sebald and J. A. Bucklew, “Support vector machine techniques for nonlinear equalization,” IEEE Tran. Signal process., Vol.48, No.11, pp. 3217-3226, 2000.
  18. [18] Y. Zhao and Y. Zhang, “Comparison of decision tree methods for finding active objects,” Adv. Space Res., Vol.41, No.12, pp. 1955-1959, 2008.
  19. [19] S. L. Salzberg, “C4.5: Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993,” Machine Learning, Vol.16, pp. 235-240, 1994.
  20. [20] M. M. Adankon and M. Cheriet, “Model selection for the LS-SVM. Application to handwriting recognition,” Pattern Recognit., Vol.42, No.12, pp. 3264-3270, 2009.
  21. [21] B. M. Wilamowski, “Neural network architectures and learning algorithms,” IEEE Ind. Electron. Mag., Vol.3, No.4, pp. 56-63, 2009.
  22. [22] J. S. Almeida, “Predictive non-linear modeling of complex data by artificial neural networks,” Curr. Opin. Biotechnol, Vol.13, No.1, pp. 72-76, 2002.
  23. [23] M. Hussain, S. K. Wajid, A. Elzaart, and M. Berbar, “A comparison of SVM kernel functions for breast cancer detection,” Proc. of 2011 8th Int. Conf. Computer Graphics, Imaging and Visualization, pp. 145-150, 2011.
  24. [24] A. Sun, E.-P. Lim, and Y. Liu, “On strategies for imbalanced text classification using SVM: A comparative study,” Decis. Support Syst., Vol.48, No.1, pp. 191-201, 2009.
  25. [25] E. Pasolli, F. Melgani, D. Tuia, F. Pacifici, and W. J. Emery, “SVM active learning approach for image classification using spatial information,” IEEE Trans. on Geoscience and Remote Sensing, Vol.52, No.4, pp. 2217-2233, 2013.
  26. [26] A. T. Azar and S. M. El-Metwally, “Decision tree classifiers for automated medical diagnosis,” Neural Comput. Appl., Vol.23, No.7-8, pp. 2387-2403, 2013.
  27. [27] C. Pelletier, G. I. Webb, and F. Petitjean, “Temporal convolutional neural network for the classification of satellite image time series,” Remote Sensing, Vol.11, No.5, doi: 10.3390/rs11050523, 2019.
  28. [28] M. Babaee, D. T. Dinh, and G. Rigoll, “A deep convolutional neural network for video sequence background subtraction,” Pattern Recognit., Vol.76, pp. 635-649, 2018.
  29. [29] L. Nguyen, R. V. Patel, and K. Khorasani, “Neural network architectures for the forward kinematics problem in robotics,” Proc. of 1990 IJCNN Int. Joint Conf. on Neural Networks, pp. 393-399, 1990.
  30. [30] C. Leslie, E. Eskin, and W. S. Noble, “The spectrum kernel: A string kernel for SVM protein classification,” Pac Symp Biocomput., pp. 564-575, 2002.
  31. [31] S. Yiamjanya and K. Wongleedee, “International tourists’ travel motivation by push-pull factors and the decision making for selecting Thailand as destination choice,” Int. J. Soc. Behav. Edu. Econ. Bus. Ind. Eng., Vol.8, No.5, pp. 1348-1353, 2014.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on May. 04, 2021