Abstract
Information technologies and the recent pandemic have reshaped the tourism sector. For tourism policy evaluation that is traditionally measured by evaluation methods, such as questionnaire and interview, we developed a dictionary-based sentiment analysis system on tourism destinations using review data generated by tourists. The system is based on the questionnaire developed by the Korean Ministry of Culture, Sports, and Tourism. Through a series computational method as well as human rating processes, sentiment lexicons, such as ‘sentimentally polarizable’ and ‘categorically discriminable,’ are extracted. The extraction procedures of the sentiment lexicons are presented in the study. On the basis of the dictionary, Haeundae beach, a famous tourism destination in Korea, is analyzed. Our result is encouraging with respect to reshaping the tourism policy evaluation with the text mining method.
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References
Korean Ministry of Culture, Sports, and Tourism (2018) A comprehensive evaluation report for culture, tourism, and festival, MCST
Kim JH (1995) A study on reliability analysis of questionnaire items. Master’s Thesis, Jeonju University
Parra-Lopez E, Bulchand-Gidumal J, Gutierrez-Tano D, Diaz-Armas R (2004) Intentions to use social media in organizing and taking vacation trips. Comput Hum Behav 27(2):640–654
Kar AK, Dwivedi YK (2020) Theory building with big data-driven research–moving away from the “what” towards the “why”. Int J Inf Manage 54
Korean Ministry of Culture, Sports, and Tourism (2011) A comprehensive evaluation report for culture, tourism, and festival, MCST
Ministry of Culture and Tourism, Tourism Evaluation Report. MCT (2003)
Toboada M, Brooke J, Tofiloski M, Voll M, Stede M (2011) Lexicon-based methods for sentiment analysis. Comput Linguist 37(2):267–307
Mowlaei ME, Abadeh MS, Keshavarz H (2020) Aspect-based sentiment analysis using adaptive aspect-based lexicons. Expert Syst Appl 148(15)
Kim C (2000) A model specification for measuring competitiveness of the tourism industry Korea. Tourism Research Institute
Isojima A (2006) Analysis of a consumer questionnaire pertaining to rice by using text mining. Agric Inf Res 15(1):49–60
TripAdvisor Busan page. https://www.tripadvisor.com/Tourism-g297884-Busan-Vacations.html. Last accessed 1 Oct 2021
Wikipedia Haeundae Beach page. https://en.wikipedia.org/wiki/HaeundaeBeach. Last accessed 1 Feb 2022
Wikipedia Gamcheon Culture Village page. https://en.wikipedia.org/wiki/GamcheonCultureVillage. Last accessed 1 Feb 2022
Wikipedia Beomeosa page. https://en.wikipedia.org/wiki/Beomeosa. Last accessed 1 Feb 2022
Park K, Lee J, Jang S, Jung D (2020) An empirical study of tokenization strategies for various Korean NLP tasks. In: Proceedings of the 1st conference of the Asia- Pacific chapter of the association for computational linguistics and the 10th international joint conference on natural language processing, pp 133–142. Suzhou, China
Han C-H, Palmer M (2004) A morphological tagger for Korean: statistical tagging combined with corpus-based morphological rule application. Mach Transl 18(4):275–297
Park EL, Cho S (2014) KoNLPy: Korean natural language processing in Python. In: Proceedings of the 26th annual conference on human and cognitive language technology. Chuncheon, Korea
Zhang T, Ge SS (2019) An improved TF-IDF algorithm based on class discriminative strength for text categorization on desensitized data. In: Proceedings of the 2019 3rd international conference on innovation in artificial intelligence. pp 39–44 (2019)
Jurafsky D, Martin JH (2021) Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition, 3rd edn. Prentice Hall PTR, Upper Saddle River, NJ, United States
Acknowledgements
This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2018S1A3A2075240).
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Lee, K., Lim, J., Kim, M., Kim, D., Hong, SG. (2023). Building up a Categorical Sentiment Dictionary for Tourism Destination Policy Evaluation. In: Jacob, I.J., Kolandapalayam Shanmugam, S., Izonin, I. (eds) Data Intelligence and Cognitive Informatics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-6004-8_2
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DOI: https://doi.org/10.1007/978-981-19-6004-8_2
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