Abstract
Local, State and National parks are a major source of natural beauty, fresh air, and calming environs that are being used more and more by visitors to achieve mental and physical wellbeing. Given the popularity of social networks and availability of smartphones with user-friendly apps, these patrons are recording their visit experiences in the form of online reviews and blogs. The availability of this voluminous data provides an excellent opportunity for facility management to improve their service operation by cherishing the positive compliments and identifying and addressing the inherent concerns. This data however, lacks structure, is voluminous and is not easily amenable to manual analysis necessitating the use of Big Data approaches. We designed, developed, and implemented software systems that can download, organize, and analyze the text from these online reviews, analyze them using Natural Language Processing algorithms to perform sentiment analysis and topic modeling and provide facility managers actionable insights to improve visitor experience.
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References
I. Blal, M.C. Sturman, The differential effects of the quality and quantity of online reviews on hotel room sales. Cornell Hosp. Q. 55(4), 365–375 (2014)
D. Blei, J. Lafferty J, Topic models, in Text Mining: Classification, Clustering, and Applications, ed, by A. Srivastava, M. Sahami (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series, 2009), pp. 71–94
S. Driml, M. Common, Economic and financial benefits of tourism in major protected areas. Aust. J. Enviro. Manage. 2(2), 19–39 (1995)
W. Duan, B. Gu, A.B. Whinston, The dynamics of online word-of-mouth and product sales—An empirical investigation of the movie industry. J. Retail. 84(2), 233–242 (2008)
P.F.J. Eagles, Trends in park tourism: economics, finance, and management. J. Sustain. Tourism 10(2), 132–153 (2002)
C.M. Fleming, M. Bowden, Web-based surveys as an alternative to traditional mail methods. J. Environ. Manage. 90(4), 284–292 (2009)
A.R. Graefe, J.D. Absher, R.C. Burns, Monitoring visitor satisfaction: a comparison of comment cards and more in-depth surveys, in Proceedings of the 2000 Northeastern Recreation Research Symposium (USDA Forest Service General Technical Report NE-276, 2000), pp. 265–69
M.J.B. Green, J. Paine, State of the world’s protected areas at the end of the twentieth century. Paper presented at protected areas in the 21st century: from islands to networks. World Commission on Protected Areas, Albany, WA, Australia (1997)
B. Gruen, K. Hornik, Topic models: an R package for fitting topic models. J. Stat. Softw. 40(13), 1–30 (2011). http://www.jstatsoft.org/v40/i13/
N. Hu, N.S. Koh, S.K. Reddy, Ratings lead you to the product, reviews help you clinch it? The mediating role of online review sentiments on product sales. Decis. Support Syst. 57, 42–53 (2014)
R. Kosala, H. Blockeel, Web mining research: a survey. ACM SIGKDD Explor. Newslett. 2(1), 1–15 (2000)
S.E. Levy, W. Duan, S. Boo, An analysis of one-star online reviews and responses in the Washington, D.C., lodging market. Cornell Hosp. Q. 54(1), 49–63 (2013)
K. Lindberg, Economic aspects of ecotourism, in Ecotourism: A Guide for Planners and Managers, vol. 2., ed. by M. Epler Wood, K. Lindberg (Ecotourism Society, North Bennington, VT, 1998)
B. Liu, Sentiment analysis and subjectivity, in Handbook of Natural Language Processing, 2nd edn., ed. by N. Indurkhya, F.J. Damerau (Chapman & Hall/CRC Machine Learning & Pattern Recognition, 2010), pp. 627–666
T. Loughran, B. McDonald, When is a liability not a liability? Textual analysis, dictionaries, and 10-ks. J. Finan. 66(1), 35–65 (2011)
S. Mankad, H. Han, J. Goh, S. Gavirneni, Understanding online hotel reviews through automated text analysis. Serv. Sci. 8(2), 124–138 (2016)
S.M. Mudambi, D. Schuff, What makes a helpful online review? A study of customer reviews on Amazon.com. MIS Q. 34(1), 185–200 (2010)
F.A. Nielsen, A new ANEW: evaluation of a word list for sentiment analysis in microblogs, in Proceedings of the ESCW2011 Workshop on ‘Making Sense of Microposts’: Big Things Come in Small Packages (2011), pp. 93–98
R Core Team, R: a language and environment for statistical computing, in R Foundation for Statistical Computing (Vienna, Austria, 2014). http://www.R-project.org/
S.A. Rhoades, The Herfindahl-Hirschman index. Fed. Reserve Bull. March, 188–189
B.A. Sparks, V. Browning, The impact of online reviews on hotel booking intentions and perception of trust. Tourism Manage. 32(6), 1310–1323 (2011)
H.P. Udyapuram, S. Gavirneni, Automated analysis of online reviews to improve visitor experience in New York State Parks. WRI Final Report (2014). https://wri.cals.cornell.edu/sites/wri.cals.cornell.edu/files/shared/documents/2014_Gavirneni_Final.pdf
M.P. Wells, Economic Perspectives on Nature Tourism, Conservation and Development (Environment Department Paper No. 55, Pollution and Environmental Economics Division, World Bank, Washington, DC, 1997)
Acknowledgements
This research was conducted at the New York State Water Resources Institute and funded by the Hudson River Estuary Program, a program of the NYS Department of Environmental Conservation. We are grateful for the support we received from those two organizations on the intellectual, educational, technological, motivational, and financial dimensions.
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Udyapuram, H.P., Gavirneni, S. (2019). Big Data Improves Visitor Experience at Local, State, and National Parks—Natural Language Processing Applied to Customer Feedback. In: Emrouznejad, A., Charles, V. (eds) Big Data for the Greater Good. Studies in Big Data, vol 42. Springer, Cham. https://doi.org/10.1007/978-3-319-93061-9_8
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DOI: https://doi.org/10.1007/978-3-319-93061-9_8
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