Sentiment analysis of online destination image of Hong Kong held by mainland Chinese tourists

References

• Ain, Q. T., Ali, M., Riaz, A., Noureen, A., Kamran, M., Hayat, B., & Rehman, A. (2017). Sentiment analysis using deep learning techniques: A review. International Journal of Advanced Computer Science and Applications, 8(6), 424–433. doi:10.14569/IJACSA.2017.080657
   Web of Science ®Google Scholar
• Alaei, A. R., Becken, S., & Stantic, B. (2019). Sentiment analysis in tourism: Capitalizing on big data. Journal of Travel Research, 58(2), 175–191. https://doi.org/10.1177/0047287517747753
   Web of Science ®Google Scholar
• Andreu, L., Bigne, J., & Cooper, C. (2000). Projected and perceived image of Spain as a tourist destination for British travelers. Journal of Travel and Tourism Marketing, 9(4), 47–68. https://doi.org/10.1300/J073v09n04_03
   Google Scholar
• Asghar, M. Z., Khan, A., Ahmad, S., & Kundi, F. M. (2014). A review of feature extraction in sentiment analysis. Journal of Basic and Applied Scientific Research, 4(3), 181–186.
   Google Scholar
• Baloglu, S., & Brinberg, D. (1997). Affective images of tourism destinations. Journal of Travel Research, 35(4), 11–15. https://doi.org/10.1177/004728759703500402
   Google Scholar
• Baloglu, S., & McCleary, K. W. (1999). A model of destination image formation. Annals of Tourism Research, 26(4), 868–897. https://doi.org/10.1016/S0160-7383(99)00030-4
   Web of Science ®Google Scholar
• Beerli, A., & Martín, J. D. (2004). Factors influencing destination image. Annals of Tourism Research, 31(3), 657–681. https://doi.org/10.1016/j.annals.2004.01.010
   Web of Science ®Google Scholar
• Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1–8. https://doi.org/10.1016/j.jocs.2010.12.007
   Web of Science ®Google Scholar
• Cambria, E., Schuller, B., Xia, Y., & Havasi, C. (2013). New avenues in opinion mining and sentiment analysis. IEEE Intelligent Systems, 28(2), 15–21. https://doi.org/10.1109/MIS.2013.30
   Web of Science ®Google Scholar
• Chan, A., Hsu, C. H. C., & Baum, T. (2015). The impact of tour service performance on tourist satisfaction and behavioral intentions: A study of Chinese tourists in Hong Kong. Journal of Travel and Tourism Marketing, 32(1-2), 18–33. https://doi.org/10.1080/10548408.2014.986010
   Web of Science ®Google Scholar
• Chan, C. S., & Marafa, L. M. (2016). Perceptual content analysis for city image: A case study of Hong Kong. Asia Pacific Journal of Tourism Research, 21(12), 1285–1299. https://doi.org/10.1080/10941665.2016.1141226
   Web of Science ®Google Scholar
• Chan, C. S., Peters, M., & Pikkemaat, B. (2019). Investigating visitors’ perception of smart city dimensions for city branding in Hong Kong. International Journal of Tourism Cities, 5(4), 620–638. https://doi.org/10.1108/IJTC-07-2019-0101
   Web of Science ®Google Scholar
• Chen, P., Sun, Z., Bing, L., & Yang, W. (2017, 7–11 September). Recurrent attention network on memory for aspect sentiment analysis. Proceedings of the 2017 Conference on Empirical Methods In Natural Language Processing (pp. 452–461).
   Google Scholar
• Chiu, W., Zeng, S., & Cheng, P. S-T. (2016), The influence of destination image and tourist satisfaction on tourist loyalty: A case study of Chinese tourists in Korea. International Journal of Culture, Tourism and Hospitality Research, 10(2), 223–234. https://doi.org/10.1108/IJCTHR-07-2015-0080
   Web of Science ®Google Scholar
• Choi, W. M., Chan, A., & Wu, J. (1999). A qualitative and quantitative assessment of Hong Kong’s image as a tourist destination. Tourism Management, 20(3), 361–365. https://doi.org/10.1016/S0261-5177(98)00116-2
   Web of Science ®Google Scholar
• Choi, S., Lehto, X. Y., & Morrison, A. M. (2007). Destination image representation on the web: Content analysis of Macau travel related websites. Tourism Management, 28(1), 118–129. https://doi.org/10.1016/j.tourman.2006.03.002
   Web of Science ®Google Scholar
• Chu, K. (2016, February 28). Hong Kong should extend a warmer welcome to mainland visitors – or suffer a permanent decline in its tourism industry. South China Morning Post. https://www.scmp.com/comment/insight-opinion/article/1917170/hong-kong-should-extend-warmer-welcome-mainland-visitors-or
   Google Scholar
• Dai, B., Jiang, Y., Yang, L., & Ma, Y. (2017). China’s outbound tourism – stages, policies and choices. Tourism Management, 58, 253–258. https://doi.org/10.1016/j.tourman.2016.03.009
   Web of Science ®Google Scholar
• Dhaoui, C., Webster, C. M., & Tan, L. P. (2017). Social media sentiment analysis: Lexicon versus machine learning. Journal of Consumer Marketing, 34(6), 480–488. https://doi.org/10.1108/JCM-03-2017-2141
   Web of Science ®Google Scholar
• Dragouni, M., Filis, G., Gavriilidis, K., & Santamaria, D. (2016). Sentiment, mood and outbound tourism demand. Annals of Tourism Research, 60, 80–96. https://doi.org/10.1016/j.annals.2016.06.004
   Web of Science ®Google Scholar
• Echtner, C. M., & Ritchie, R. B. (1993). The measurement of destination image: An empirical assessment. Journal of Travel Research, 31(4), 3–13. https://doi.org/10.1177/004728759303100402
   Google Scholar
• Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review, 14(4), 532–550. https://doi.org/10.5465/amr.1989.4308385
   Web of Science ®Google Scholar
• Evans, M. R., & Chon, K. S. (1989). Formulating and evaluating tourism policy using importance-performance analysis. Journal of Hospitality and Tourism Research, 13, 203–213. https://doi.org/10.1177/109634808901300320
   Google Scholar
• Farhadloo, M., Patterson, R. A., & Rolland, E. (2016). Modeling customer satisfaction from unstructured data using a Bayesian approach. Decision Support Systems, 90, 1–11. https://doi.org/10.1016/j.dss.2016.06.010
   Web of Science ®Google Scholar
• Fuchs, M., Höpken, W., & Lexhagen, M. (2014). Big data analytics for knowledge generation in tourism destinations – A case from Sweden. Journal of Destination Marketing & Management, 3(4), 198–209. https://doi.org/10.1016/j.jdmm.2014.08.002
   Web of Science ®Google Scholar
• Gallarza, M. G., Saura, I. G., & Garcıća, H. C. (2002). Destination image: Towards a conceptual framework. Annals of Tourism Research, 29(1), 56–78. https://doi.org/10.1016/S0160-7383(01)00031-7
   Web of Science ®Google Scholar
• Gartner, W. C. (1994). Image formation process. Journal of Travel & Tourism Marketing, 2(2–3), 191–216. https://doi.org/10.1300/J073v02n02_12
   Google Scholar
• Geetha, M., Singha, P., & Sinha, S. (2017). Relationship between customer sentiment and online customer ratings for hotels – An empirical analysis. Tourism Management, 61, 43–54. https://doi.org/10.1016/j.tourman.2016.12.022
   Web of Science ®Google Scholar
• Gitto, S., & Mancuso, P. (2017). Improving airport services using sentiment analysis of the websites. Tourism Management Perspectives, 22, 132–136. https://doi.org/10.1016/j.tmp.2017.03.008
   Web of Science ®Google Scholar
• Govada, S. S., & Rodgers, T. (2019). Towards smarter regional development of Hong Kong within the Greater Bay Area. In T. Vinod Kumar (Ed.), Smart metropolitan regional development: Advances in 21st century human settlements (pp. 101–171). Springer.
   Google Scholar
• Gretzel, U., Yuan, Y. L., & Fesenmaier, D. R. (2000). Preparing for the new economy: Advertising strategies and change in destination marketing organizations. Journal of Travel Research, 39(2), 146–156. https://doi.org/10.1177/004728750003900204
   Google Scholar
• Hong Kong Tourism Board (HKTB). (2019). Hong Kong tourism performance in 2018. Retrieved August 20, 2019, from https://www.tourism.gov.hk/english/statistics/statistics_perform.html
   Google Scholar
• Hu, N., Koh, N. S., & Reddy, S. K. (2014). Ratings lead you to the product, reviews help you clinch it? The mediating role of online review sentiments on product sales. Decision Support Systems, 57, 42–53. https://doi.org/10.1016/j.dss.2013.07.009
   Web of Science ®Google Scholar
• Hui, T. K., & Wan, T. W. D. (2003). Singapore’s image as a tourist destination. International Journal of Tourism Research, 5(4), 305–313. https://doi.org/10.1002/jtr.437
   Google Scholar
• Information Services Department (ISD). (2018). The 2018-19 Budget. https://www.budget.gov.hk/2018/eng/budget16.html
   Google Scholar
• Jimenez-Barreto, J., Sthapit, E., Rubio, N., & Campo, S. (2019). Exploring the dimensions of online destination brand experience: Spanish and North American tourists’ perspectives. Tourism Management Perspectives, 31, 348–360. https://doi.org/10.1016/j.tmp.2019.07.005
   Web of Science ®Google Scholar
• Jussila, J., Vuori, V., Okkonen, J., & Helander, N. (2017). Reliability and perceived value of sentiment analysis for Twitter data. In A. Kavoura, D. Sakas, & P. Tomaras (Eds.), 5th international Conference on Strategic Innovative Marketing, at Athens, Greece: September 23–26, 2016 (pp. 43–48). (Springer Proceedings in Business and Economics). Springer. https://doi.org/10.1007/978-3-319-56288-9_7
   Google Scholar
• Kammerer, P. (2018, January 29). In Hong Kong, animosity towards mainland Chinese can’t be overcome without an open mind. South China Morning Post. Retrieve from: https://www.scmp.com/comment/insight-opinion/article/2130985/hong-kong-animosity-towards-mainland-chinese-cant-be
   Google Scholar
• Kim, H., & Chen, J. S. (2016). Destination image formation process: A holistic model. Journal of Vacation Marketing, 22(2), 154–166. https://doi.org/10.1177/1356766715591870
   Web of Science ®Google Scholar
• Kirilenko, A. P., Stepchenkova, S. O., & Hernandez, J. M. (2019). Comparative clustering of destination attractions for different origin markets with network and spatial analyses of online reviews. Tourism Management, 72, 400–410. https://doi.org/10.1016/j.tourman.2019.01.001
   Web of Science ®Google Scholar
• Kladou, S., & Mavragani, E. (2015). Assessing destination image: An online marketing approach and the case of TripAdvisor. Journal of Destination Marketing & Management, 4(3), 187–193. https://doi.org/10.1016/j.jdmm.2015.04.003
   Web of Science ®Google Scholar
• Költringer, C., & Dickinger, A. (2015). Analyzing destination branding and image from online sources: A web content mining approach. Journal of Business Research, 68(9), 1836–1843. https://doi.org/10.1016/j.jbusres.2015.01.011
   Web of Science ®Google Scholar
• Ku, G. C. M., & Mak, A. H. N. (2017). Exploring the discrepancies in perceived destination images from residents’ and tourists’ perspectives: A revised importance-performance analysis approach. Asia Pacific Journal of Tourism Research, 22(11), 1124–1138. https://doi.org/10.1080/10941665.2017.1374294
   Web of Science ®Google Scholar
• Lai, I. K. W., & Hitchcock, M. (2015). Importance–performance analysis in tourism: A framework for researchers. Tourism Management, 48, 242–267. https://doi.org/10.1016/j.tourman.2014.11.008
   Web of Science ®Google Scholar
• Lam, J. M., Ismail, H., & Lee, S. (2020). From desktop to destination: User-generated content platforms, co-created online experiences, destination image and satisfaction. Journal of Destination Marketing & Management, 18, 100490. https://doi.org/10.1016/j.jdmm.2020.100490
   Web of Science ®Google Scholar
• Law, R., & Cheung, S. (2010). The perceived destination image of Hong Kong as revealed in the travel blogs of mainland Chinese tourists. International Journal of Hospitality and Tourism Administration, 11(4), 303–327. https://doi.org/10.1080/15256480.2010.518521
   Google Scholar
• Leung, D., Law, R., & Lee, H. A. (2011). The perceived destination image of Hong Kong on Ctrip.com. International Journal of Tourism Research, 13(2), 124–140. https://doi.org/10.1002/jtr.803
   Web of Science ®Google Scholar
• Li, J., Xu, L., Tang, L., Wang, S., & Li, L. (2018). Big data in tourism research: A literature review. Tourism Management, 68, 301–323. https://doi.org/10.1016/j.tourman.2018.03.009
   Web of Science ®Google Scholar
• Lian, T., & Yu, C. (2017). Representation of online image of tourist destination: A content analysis of Huangshan. Asia Pacific Journal of Tourism Research, 22(10), 1063–1082. https://doi.org/10.1080/10941665.2017.1368678
   Web of Science ®Google Scholar
• Lin, C.-H., Morais, D. B., Kerstetter, D. L., & Hou, J.-S. (2007). Examining the role of cognitive and affective image in predicting choice across natural, developed, and theme-park destinations. Journal of Travel Research, 46(2), 183–194. https://doi.org/10.1177/0047287507304049
   Google Scholar
• Liu, B. (2015). Sentiment analysis: Mining opinions, sentiments, and emotions. Cambridge University Press.
   Google Scholar
• Liu, Y., Huang, K., Bao, J., & Chen, K. (2019). Listen to the voices from home: An analysis of Chinese tourists’ sentiments regarding Australian destinations. Tourism Management, 71, 337–347. https://doi.org/10.1016/j.tourman.2018.10.004
   Web of Science ®Google Scholar
• Lund, N. F., Cohen, S. A., & Scarles, C. (2018). The power of social media storytelling in destination branding. Journal of Destination Marketing & Management, 8, 271–280. https://doi.org/10.1016/j.jdmm.2017.05.003
   Web of Science ®Google Scholar
• Luo, Q., & Zhai, X. (2017). “I will never go to Hong Kong again!” How the secondary crisis communication of “Occupy Central” on Weibo shifted to a tourism boycott. Tourism Management, 62, 159–172. https://doi.org/10.1016/j.tourman.2017.04.007
   PubMed Web of Science ®Google Scholar
• Ma, Y., Peng, H., & Cambria, E. (2018, February). Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. The Thirty-second AAAI Conference on Artificial Intelligence (AAAI-18) (pp. 5876–5883), the Hilton New Orleans Riverside, New Orleans, US.
   Google Scholar
• Mak, A. H. N. (2017). Online destination image: Comparing national tourism organisation’s and tourists’ perspectives. Tourism Management, 60, 280–297. https://doi.org/10.1016/j.tourman.2016.12.012
   Web of Science ®Google Scholar
• Manek, A. S., Shenoy, P. D., Mohan, M. C., & Venugopal, K. R. (2017). Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier. World Wide Web, 20(2), 135–154. https://doi.org/10.1007/s11280-015-0381-x
   Google Scholar
• Mäntylä, M. V., Graziotin, D., & Kuutila, M. (2018). The evolution of sentiment analysis – A review of research topics, venues, and top cited papers. Computer Science Review, 27, 16–32. https://doi.org/10.1016/j.cosrev.2017.10.002
   Web of Science ®Google Scholar
• Marchiori, E., & Cantoni, L. (2011). The online reputation construct: Does it matter for the tourism domain? A literature review on destinations’ online reputation. Information Technology & Tourism, 13(3), 139–159. https://doi.org/10.3727/109830512X13283928066715
   Google Scholar
• Martilla, J. A., & James, J. C. (1977). Importance-performance analysis. Journal of Marketing, 41(1), 77–79. https://doi.org/10.1177/002224297704100112
   Web of Science ®Google Scholar
• Nowacki, M., & Niezgoda, A. (2020). Identifying unique features of the image of selected cities based on reviews by TripAdvisor portal users. Scandinavian Journal of Hospitality and Tourism, 20(5), 503–519. https://doi.org/10.1080/15022250.2020.1833362
   Web of Science ®Google Scholar
• Nyaupane, G. P., Timothy, D. J., & Poudel, S. (2015). Understanding tourists in religious destinations: A social distance perspective. Tourism Management, 48, 343–353. https://doi.org/10.1016/j.tourman.2014.12.009
   Web of Science ®Google Scholar
• Pantano, E., & Di Pietro, L. (2013). From e-tourism to f-tourism: Emerging issues from negative tourists’ online reviews. Journal of Hospitality and Tourism Technology, 4(3), 211–227. https://doi.org/10.1108/JHTT-02-2013-0005
   Google Scholar
• Park, E., Kang, J., Choi, D., & Han, J. (2020). Understanding customers’ hotel revisiting behaviour: A sentiment analysis of online feedback reviews. Current Issues in Tourism, 23(5), 605–611. https://doi.org/10.1080/13683500.2018.1549025
   Web of Science ®Google Scholar
• Philandera, K., & Zhong, Y. Y. (2016). Twitter sentiment analysis: Capturing sentiment from integrated resort tweets. International Journal of Hospitality Management, 55, 16–24. https://doi.org/10.1016/j.ijhm.2016.02.001
   Web of Science ®Google Scholar
• Pikkemaat, B., Peters, M., & Bichler, B. F. (2019). Innovation research in tourism: Research streams and actions for the future. Journal of Hospitality and Tourism Management, 41, 184–196. https://doi.org/10.1016/j.jhtm.2019.10.007
   Web of Science ®Google Scholar
• Pikkemaat, B., Peters, M., & Chan, C. S. (2018). Needs, drivers and barriers of innovation: The case of an alpine community-model destination. Tourism Management Perspectives, 25, 53–63. https://doi.org/10.1016/j.tmp.2017.11.004
   Web of Science ®Google Scholar
• Poria, S., Cambria, E., & Gelbukh, A. (2016). Aspect extraction for opinion mining with a deep convolutional neural network. Knowledge-Based Systems, 108, 42–49. https://doi.org/10.1016/j.knosys.2016.06.009
   Web of Science ®Google Scholar
• Qi, S., & Chen, N. (2019). Understanding Macao’s destination image through user-generated content. Journal of China Tourism Research, 15(4), 503–519. https://doi.org/10.1080/19388160.2019.1577199
   Web of Science ®Google Scholar
• Rahm, E., & Do, H. H. (2000). Data cleaning: Problems and current approaches. IEEE Data Eng. Bull., 23(4), 3–13.
   Google Scholar
• Ren, G., & Hong, T. (2017). Investigating online destination images using a topic-based sentiment analysis approach. Sustainability, 9(10), 1765–1783. https://doi.org/10.3390/su9101765
   Web of Science ®Google Scholar
• Ridder, H.-G. (2017). The theory contribution of case study research designs. Business Research, 10(2), 281–305. https://doi.org/10.1007/s40685-017-0045-z
   Google Scholar
• Russell, J. A., & Pratt, G. (1980). A description of the affective quality attributed to environments. Journal of Personality and Social Psychology, 38(2), 311–322. https://doi.org/10.1037/0022-3514.38.2.311
   Web of Science ®Google Scholar
• Serrano-Guerrero, J., Olivas, J. A., Romero, F. P., & Herrera-Viedma, E. (2015). Sentiment analysis: A review and comparative analysis of web services. Information Sciences, 311, 18–38. https://doi.org/10.1016/j.ins.2015.03.040
   Web of Science ®Google Scholar
• Sever, I. (2015). Importance-performance analysis: A valid management tool? Tourism Management, 48, 43–53. https://doi.org/10.1016/j.tourman.2014.10.022
   Web of Science ®Google Scholar
• Sheela, L. J. (2016). A review of sentiment analysis in Twitter data using Hadoop. International Journal of Database Theory and Application, 9(1), 77–86. https://doi.org/10.14257/ijdta.2016.9.1.07
   Google Scholar
• Shen, H., Li, X., Luo, J. M., & Chau, K. Y. (2017). One country, two strata: Implications of social and cultural conflicts of Chinese outbound tourism to Hong Kong. Journal of Destination Marketing & Management, 6(3), 166–169. https://doi.org/10.1016/j.jdmm.2016.10.003
   Web of Science ®Google Scholar
• Shirdastian, H., Laroche, M., & Richard, M. O. (2019). Using big data analytics to study brand authenticity sentiments: The case of Starbucks on Twitter. International Journal of Information Management, 48, 291–307. https://doi.org/10.1016/j.ijinfomgt.2017.09.007
   Web of Science ®Google Scholar
• Shu, X., Wang, J., Shen, X., & Qu, A. (2017). Word segmentation in Chinese language processing. Statistics and its Interface, 10(2), 165–173. https://doi.org/10.4310/SII.2017.v10.n2.a1
   Web of Science ®Google Scholar
• Sun, X. (2011). A study on the tourists’ satisfaction based on the online reviews [Doctoral dissertation]. Retrieved from: http://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CMFD&filename=1011131968.nh
   Google Scholar
• Sun, B., Ao, C., Wang, J., Mao, B., & Xu, L. (2020). Listen to the voices from tourists: Evaluation of wetland ecotourism satisfaction using an online reviews mining approach. Wetlands. https://doi.org/10.1007/s13157-020-01274-9
   Google Scholar
• Tourism Commission, and Commerce and Economic Development Bureau (TC & CEDB). (2017). Development blueprint for Hong Kong’s tourism industry. Retrieved from: https://www.tourism.gov.hk/popup/files/Development_Blueprint_for_Hong_Kongs_Tourism_Industry_Eng.pdf
   Google Scholar
• Tsao, H.-Y., Chen, M.-Y., Lin, H.-C. K., & Ma, Y.-C. (2019). The asymmetric effect of review valence on numerical rating. Online Information Review, 43(2), 283–300. https://doi.org/10.1108/OIR-11-2017-0307
   Web of Science ®Google Scholar
• UNWTO. (2001). Tourism vision 2020 - volume 2 – Americas. Tourism vison 2020. Retrieved from: http://titania.wtoelibrary.org.ezproxy.library.uq.edu.au
   Google Scholar
• Wang, C., & Hsu, M. (2010). The relationships of destination image, satisfaction, and Behavioral Intentions: An integrated model. Journal of Travel and Tourism Marketing, 27(8), 829–843. https://doi.org/10.1080/10548408.2010.527249
   Web of Science ®Google Scholar
• Wang, M., Li, X., Wei, Z., Zhi, S., & Wang, H. (2018a, 26–28 February). Chinese word segmentation based on deep learning. Proceedings of the 2018 10th international Conference on Machine Learning and Computing (pp. 16–20).
   Google Scholar
• Wang, J. H., Liu, T. W., Luo, X., & Wang, L. (2018b, 4-5 October). An LSTM approach to short text sentiment classification with word embeddings. Proceedings of the 30th Conference on Computational Linguistics and Speech Processing (ROCLING 2018) (pp. 214–223).
   Google Scholar
• Wang, X., Wang, M., & Zhang, Q. (2017, July). Realization of Chinese word segmentation based on deep learning method. AIP Conference Proceedings (Vol. 1864, No. 1, p. 020150). AIP Publishing LLC.
   Google Scholar
• Wong, C. U. I., & Qi, S. (2017). Tracking the evolution of a destination’s image by text-mining online reviews - the case of Macau. Tourism Management Perspectives, 23, 19–29. https://doi.org/10.1016/j.tmp.2017.03.009
   Web of Science ®Google Scholar
• Xiang, Z. (2010). Role of social media in online travel information search. Tourism Management, 31(2), 179–188. https://doi.org/10.1016/j.tourman.2009.02.016
   Web of Science ®Google Scholar
• Xiang, Z., Du, Q., Ma, Y., & Fan, W. (2017). A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism. Tourism Management, 58, 51–65. https://doi.org/10.1016/j.tourman.2016.10.001
   Web of Science ®Google Scholar
• Xu, J. (., Hui, C. K. T., & Chan, E. A. H. (2015). Hong Kong’s destination image in the eyes of residents. Journal of China Tourism Research, 11(4), 440–460. https://doi.org/10.1080/19388160.2015.1111182
   Web of Science ®Google Scholar
• Ye, Q., Zhang, Z., & Law, R. (2009). Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Systems with Applications, 36(3), 6527–6535. https://doi.org/10.1016/j.eswa.2008.07.035
   Web of Science ®Google Scholar
• Yeung, S., & Leung, C. (2007). Perception and attitude of Hong Kong hotel guest-contact employees towards tourists from mainland China. International Journal of Tourism Research, 9(6), 395–407. https://doi.org/10.1002/jtr.611
   Google Scholar
• Zhang, S., & Chan, C. S. (2016). Nature-based tourism development in Hong Kong: Importance–performance perceptions of local residents and tourists. Tourism Management Perspectives, 20, 38–46. https://doi.org/10.1016/j.tmp.2016.07.002
   Web of Science ®Google Scholar
• Zhang, L., Wang, S., & Liu, B. (2018a). Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), e1253. https://doi.org/10.1002/widm.1253
   Web of Science ®Google Scholar
• Zhang, M., Zhang, Y., & Fu, G. (2018b). Transition-based neural word segmentation using word-level features. Journal of Artificial Intelligence Research, 63, 923–953. https://doi.org/10.1613/jair.1.11266
   Web of Science ®Google Scholar
• Zheng, X., Chen, H., & Xu, T. (2013, 18-21 October). Deep learning for Chinese word segmentation and POS tagging. Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (pp. 647–657).
   Google Scholar