Dataset of Tourism Geography Sentiment Evaluation Model in Cities of Greater Bay Area of China(2008-2021)
LIU Yi1,2CHEN Hailong1XIAO Wenjie*1,3BAO Jigang1WU Xuehan1XU Jiali1
1 School of Tourism Management，Sun Yat-sen University，Guangzhou 510275，China2 Key Laboratory of Intelligent Assessment Technology for Sustainable Tourism，Ministry of Culture and Tourism，Zhuhai 519080，China3 School of Tourism，Jishou University，Zhangjiajie 427000，China
DOI:10.3974/geodb.2023.05.06.V1
Published:May 2023
Visitors：5654 Data Files Downloaded：71
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Key Words：
sentiment evaluation,tourism destination,TSE model,reputation
Abstract：
In order to evaluate cities’ tourist sentiment and provide consultant services for decision-making, the authors invented the Tourism Sentiment Evaluation (TSE) model with which the Dataset of Tourism Geography Sentiment Evaluation Model in Cities of Greater Bay Area of China (2008-2021) was developed. This dataset comprises of 15 tables, which include: (1) ranking of attention for 11 cities; (2) ranking of reputation for 11 cities; (3) differences in attention and reputation rankings for cities in the Greater Bay Area; (4) overall sentiment analysis of the Greater Bay Area; (5) sentiment analysis of Hong Kong; (6) sentiment analysis of Macao; (7) sentiment analysis of Guangzhou; (8) sentiment analysis of Shenzhen; (9) sentiment analysis of Zhuhai; (10) sentiment analysis of Foshan; (11) sentiment analysis of Huizhou; (12) sentiment analysis of Dongguan; (13) sentiment analysis of Zhongshan; (14) sentiment analysis of Jiangmen; (15) sentiment analysis of Zhaoqing. The dataset is archived in .xlsx format, and consists of one file with data size of 34 KB. The dataset indicates that (1) the rankings of attention and reputation for each city have stabilized since 2016, after a period of initial confusion; Guangzhou and Hong Kong have consistently been at the forefront in terms of attention and reputation rankings, with Jiangmen and Zhongshan ranking lower in position; (2) Tourist experiences such as sightseeing and playing are the main focus of tourists, and the importance of infrastructure and supporting facilities cannot be ignored.Browse
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Data Citation：
LIU Yi, CHEN Hailong, XIAO Wenjie*, BAO Jigang, WU Xuehan, XU Jiali. Dataset of Tourism Geography Sentiment Evaluation Model in Cities of Greater Bay Area of China(2008-2021)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2023. https://doi.org/10.3974/geodb.2023.05.06.V1.
LIU Yi, CHEN Hailong, XIAO Wenjie, et al. Dataset of tourism geography sentiment evaluation model application in cities of Greater Bay Area of China (2008-2021) [J]. Journal of Global Change Data & Discovery, 2023, 7(1): 102-107.
Related Publication：
[1] Liu, Y., Lin, X. Y., Zhang, T., et al. Report on Sentiment Analysis of Tourists in Guangdong-Hong Kong-Macao Greater Bay Area based on Big Data (2021) [R]. Xu, H. G., Bao, J. G. Blue Book of Guangdong-Hong Kong-Macao Greater Bay Area: Report on Tourist Industry of Guangdong-Hong Kong-Macao Greater Bay Area in China [R]. Beijing: Social Sciences Academic Press, 2022: 168-210.
[2] Liu, Y., Xu, X. J., Zhao, Y. Analysis on characteristics and differences of tourism destination image based on TSE and IPA Model—The case of Guangdong Province [J]. Tourism Forum, 2019, 12(6): 41-49.
References：
[1] Gong, J., Yang, S. Y. Study on tourism destination evaluation based on web reviews——taking 31 provinces in China for example [J]. Journal of central China Normal University (Natural Science), 2018, 52(2): 279-286.
[2] Shi, C. Y., Zhang, J., Shen, Z. P., et al. Review of the studies on the tourism spatial competition and cooperation [J]. Geography and Geo-Information Science, 2005(5): 85-89.
[3] Zhen, F., Wang, B. Rethinking human geography in the age of big data [J]. Geographical Research, 2015, 34(5): 803-811.
[4] Liu, Y., Bao, J. G., Zhu, Y. L. Exploring emotion methods of tourism destination evaluation: A big-data approach [J]. Geographical Research, 2017, 36(6): 1091-1105.
[5] Liu, Y., Meng, L. K., Bao, J. G., et al. A comparative study of sentiment computing methods: will machine learning be overwhelming? [J]. Nankai Business Review, 2021, 24(5): 63-74.
Data Product:
| ID |
Data Name |
Data Size |
Operation |
| 0 | Datapaper_DataSenEvaCitiesGBA_2008-2021.pdf | 516.00kb | DownLoad |
| 1 |
DataSenEvaCitiesGBA_2008-2021.xlsx |
34.01KB |
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