Abstract
Covid-19 leads to public fear. People are afraid to exist in crowded places like public transportation, airports, and hotels. Covid-19 has affected the field of tourism negatively. Many factors affect the travelers’ intentions to travel, including safety and security, space accessibility, travel costs, quality issues, sanitation risks, hygiene, and destination trust. The research aims to propose a sentiment classifier model that analyses travelers’ sentiments intention. The TravelerIntention Sentiment model uses three classification techniques: Support Vector Machine, Naïve Bayes and Decision Tree. One thousand sentiments were collected and analyzed. The safety and security factor was the highest important factor based on 326 sentiments. Results have shown that Naïve Bayes has the highest accuracy when using the Term Frequency Inverse Document Frequency feature selection method, and Support Vector Machine has the highest accuracy level when using the Bag of Words feature selection method.
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References
Nikolaos Pappas, N., Glyptou, K.: Accommodation decision-making during the COVID-19 pandemic: complexity insights from Greece. Int. J. Hosp. Manag. 93, 102767 (2021)
SiliangLuan, S., Yang, Q., Jiang, Z., Wang, W.: Exploring the impact of COVID-19 on individual’s travel mode choice in China. Transp. Policy 106, 271–280 (2021)
Muhammad Abdullah, M., Ali, N., Hussain, S., Aslam, A., Javid, M.: Measuring changes in travel behavior pattern due to COVID-19 in a developing country: a case study of Pakistan. Transp. Policy 108, 21–33 (2021)
Rasoolimanesh, M., Seyfi, S., Rastegar, R., Hall, M.: Destination image during the COVID-19 pandemic and future travel behavior: the moderating role of past experience. J. Destination Mark. Manag. 21, 100620 (2021)
Zheng, D., Luo, Q., Ritchie, B.: Afraid to travel after COVID-19? Self-protection, coping and resilience against pandemic ‘travel fear.’ Tour. Manag. 83, 104261 (2021)
Abdullah, M., Dias, C., Deepti Muley, M., Shahin,: Exploring the impacts of COVID-19 on travel behavior and mode preferences. Transp. Res, Interdiscip. Perspect. 8, 100255 (2020)
Asian Development Bank: The Economic Impact of the COVID-19 Outbreak on Developing Asia, vol. 9(2020). https://doi.org/10.22617/BRF200096
Battistini, N., Stoevsky, G.: Alternative scenarios for the impact of the COVID-19 pandemic on economic activity in the EURO Area. Economic Bulletin Boxes March (2020)
Huang, X., Dai, S., Xu, H.: Predicting tourists’ health risk preventative behaviour and travelling satisfaction in Tibet: Combining the theory of planned behaviour and health belief model. Tour. Manag. Perspect. 33, 100589 (2020)
Gursoy, D., Chi, C., Chi, O.: COVID-19 Report for the restaurant and hotel industry - Restaurant and hotel customers’ sentiment analysis: would they come back? If they would, WHEN? Washington DC (2020)
Zhang, Y., Fricker, D.: Quantifying the impact of COVID-19 on non-motorized transportation: a Bayesian structural time series model. Transp. Pol. 103, 11–20 (2021)
Fodoudi, P., Tabaghdehi, S., Marvi, R.: The gloom of the COVID-19 shock in the hospitality industry: a study of consumer risk perception and adaptive belief in the dark cloud of a pandemic. Int. J. Hosp. Manag. 92, 102717 (2021)
Abdi, A., Shamsuddin, S.M., Hasan, S., Piran, J.: Machine learning-based multi documents sentiment-oriented summarization using linguistic treatment. Expert Syst. Appl. 109, 66–85 (2018)
Raut, V.B., Londhe, D.D.: Opinion mining and summarization of hotel reviews. In: Proceedings - 2014 6th International Conference on Computational Intelligence and Communication Networks, pp. 556–559 (2014)
Dehkharghani, R., Yanikoglu, B., Tapucu, D., Saygin, Y.: Adaptation and use of subjectivity lexicons for domain-dependent sentiment classification. In: IEEE 12th International Conference on Data Mining Workshops Adaptation, pp. 669–673 (2012)
Smetana, M., Koncz, P., Smetana, P., Parali, J.: Active learning enhanced semiautomatic annotation tool for aspect-based sentiment analysis. In: IEEE 11th International Symposium on Intelligent Systems and Informatics, pp. 191–194 (2013)
Fancourt, D., Steptoe, A., Wright, L.: The cummings effect: Politics, trust, and behaviours during the COVID-19 pandemic. Lancet 396(10249), 464–465 (2020)
Moreno, C., et al.: How mental health care should change as a consequence of the COVID-19 pandemic. Lancet Psychiatry 7, 813–824 (2020)
Mostafa, L., Abd Elghany, M.: Investigating game developers’ guilt emotions using sentiment analysis. Int. J. Softw. Eng. Appl. (IJSEA) 9(6), 16 (2018)
Mostafa, L.: Machine learning-based sentiment analysis for analyzing the travelers reviews on Egyptian hotels. In: Hassanien, A.-E., Azar, A.T., Gaber, T., Oliva, D., Tolba, F.M. (eds.) Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020), pp. 405–413. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-44289-7_38
Abdelghany, M., Abdelghany, M., Mostafa, L.: The analysis of the perceptions of service facilities and their impact on student satisfaction. IJBR, 19(1) (2019)
Knime. http://www.knime.com/.Accessed 11 Sept 2019
Mostafa, L., Beshir, S.: Job candidate rank approach using machine learning techniques. In: Hassanien, A.-E., Chang, K.-C., Mincong, T. (eds.) Advanced Machine Learning Technologies and Applications: Proceedings of AMLTA 2021, pp. 225–233. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-69717-4_24
Mostafa, L., Beshir, S.: University selection model using machine learning techniques. In: Hassanien, A.E., et al. (eds.) Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021), pp. 680–688. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-76346-6_60
Reza, A., Alaei, A., Becken, S., Bela Stantic, B.: Sentiment analysis in tourism: capitalizing on big data. J. Travel Res. 58(2), 175–191 (2019)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86 (2002)
Chang, C., Lin, C.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011)
Suresh, A.: Sentiment classification using decision tree-based feature selection. Int. J. Control Theory Appl. (2016)
Mostafa, L.:Webpage keyword extraction using term frequency. In: ICIME 2011, Proceedings of 3rd IEEE International Conference on Information Management and Engineering, Zhengzhou, China, 21–22 May (2011)
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Mostafa, L., Beshir, S. (2022). Understating Factors Affecting Traveling During COVID-19 Using Sentiment Analysis. In: Hassanien, A.E., Snášel, V., Chang, KC., Darwish, A., Gaber, T. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2021. AISI 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 100. Springer, Cham. https://doi.org/10.1007/978-3-030-89701-7_10
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