Temporal and spatiotemporal investigation of tourist attraction visit sentiment on Twitter

PLoS ONE

Volumn 13, Issue 6, 2018, Pages

  Padilla, Jose J ^a   Kavak, Hamdi ^a   Lynch, Christopher J ^a   Gore, Ross J ^a   Diallo, Saikou Y ^a  

^a OLD DOMINION UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; HUMAN; HUMAN EXPERIMENT; ILLINOIS; SEASON; SOCIAL MEDIA; SPATIOTEMPORAL ANALYSIS; TOURISM; WEATHER; PSYCHOLOGY; QUESTIONNAIRE; RECREATION; STATISTICS AND NUMERICAL DATA;

HUMANS; RECREATION; SOCIAL MEDIA; SPATIO-TEMPORAL ANALYSIS; SURVEYS AND QUESTIONNAIRES;

EID: 85059295341     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0198857     Document Type: Article

References (48)

1. Tribe J. The economics of recreation, leisure and tourism. Fifth edit ed. New York, NY: Routledge; 2016.
2. McElroy JL. Small island tourist economies across the life cycle. Asia Pacific Viewpoint. 2006; 47 (1):61–77. https://doi.org/10.1111/j.1467-8373.2006.00303.x
3. Gladstone DL. Tourism Urbanization in the United States. Urban Affairs Review. 1998; 34(1):3–27. https://doi.org/10.1177/107808749803400101
4. USBLS;. https://www.bls.gov/regions/west/summary/blssummary_lasvegas.pdf.
5. Yoon Y, Uysal M. An examination of the effects of motivation and satisfaction on destination loyalty: a structural model. Tourism Management. 2005; 26(1):45–56. https://doi.org/10.1016/j.tourman.2003.08. 016
6. Ryan C. Researching tourist satisfaction: issues, concepts, problems. London, UK: Routledge; 1995.
7. Yun D, Joppe M. Are Tourists Who Are Not Satisfied Necessarily Dissatisfied? In: 2012 TTRA International Conference; 2012.
8. Kozak M, Rimmington M. Tourist satisfaction with Mallorca, Spain, as an off-season holiday destination. Journal of Travel Research. 2000; 38(3):260–269. https://doi.org/10.1177/004728750003800308
9. Gallarza MG, Saura IG. Value dimensions, perceived value, satisfaction and loyalty: An investigation of university students’ travel behaviour. Tourism Management. 2006; 27(3):437–452. https://doi.org/10.1016/j.tourman.2004.12.002
10. Kozak M. Comparative analysis of tourist motivations by nationality and destinations. Tourism Management. 2002; 23(3):221–232. https://doi.org/10.1016/S0261-5177(01)00090-5
11. Fennell DA. A tourist space-time budget in the Shetland Islands. Annals of Tourism Research. 1996; 23 (4):811–829. https://doi.org/10.1016/0160-7383(96)00008-4
12. O’Connor A, Zerger A, Itami B. Geo-temporal tracking and analysis of tourist movement. Mathematics and Computers in Simulation. 2005; 69(1-2 SPEC. ISS.):135–150.
13. Shoval N, Isaacson M. Tracking tourists in the digital age. Annals of Tourism Research. 2007; 34 (1):141–159. https://doi.org/10.1016/j.annals.2006.07.007
14. Jurdak R, Zhao K, Liu J, AbouJaoude M, Cameron M, Newth D. Understanding Human Mobility from Twitter. PLoS ONE. 2015; 10(7):e0131469. https://doi.org/10.1371/journal.pone.0131469 PMID: 26154597
15. Barchiesi D, Preis T, Bishop S, Moat HS. Modelling human mobility patterns using photographic data shared online. Royal Society Open Science. 2015; 2(8). https://doi.org/10.1098/rsos.150046 PMID: 26361545
16. Girardin F, Blat J, Calabrese F, Dal Fiore F, Ratti C. Digital footprinting: Uncovering tourists with user-generated content. IEEE Pervasive Computing. 2008; 7(4):36–44. https://doi.org/10.1109/MPRV.2008.71
17. Jankowski P, Andrienko N, Andrienko G, Kisilevich S. Discovering Landmark Preferences and Movement Patterns from Photo Postings. Transactions in GIS. 2010; 14(6):833–852. https://doi.org/10.1111/j.1467-9671.2010.01235.x
18. Popescu A, Grefenstette G, Moëllic PA. Mining tourist information from user-supplied collections; 2009. p. 1713–1716. Available from: http://portal.acm.org/citation.cfm?doid=1645953.1646211.
19. Wood Sa, Guerry AD, Silver JM, Lacayo M. Using social media to quantify nature-based tourism and recreation. Scientific reports. 2013; 3:2976. https://doi.org/10.1038/srep02976 PMID: 24131963
20. Kavak H, Padilla JJ, Lynch CJ, Diallo SY. Big Data, Agents, and Machine Learning: towards A Data-Driven Agent-based Modeling Approach. In: 51st Annual Simulation Symposium (ANSS’18). ACM; 2018.
21. Kavak H, Vernon-Bido D, Padilla JJ. Fine-Scale Prediction of People’s Home Location using Social Media Footprints. In: Bisgin H, Hyder A, Dancy C, Thomson R, editors. Proceedings of the International Conference SBP-BRiMS 2018. LNCS. Springer; 2018.
22. Mahmud J, Nichols J, Drews C. Home Location Identification of Twitter Users. ACM Transactions on Intelligent Systems and Technology. 2014; 5(3):1–21. https://doi.org/10.1145/2528548
23. Pang B, Lee L. Opinion Mining and Sentiment Analysis. Foundations and Trends in Information. 2006; 1(2):91–231.
24. Ranco G, Aleksovski D, Caldarelli G, Grčar M, Mozetič I. The effects of twitter sentiment on stock price returns. PLoS ONE. 2015; 10(9):1–21. https://doi.org/10.1371/journal.pone.0138441
25. Gabrovšek P, Aleksovski D, Mozetič I, Grčar M. Twitter sentiment around the Earnings Announcement events. PLoS ONE. 2017; 12(2):e0173151. https://doi.org/10.1371/journal.pone.0173151 PMID: 28235103
26. Cody EM, Reagan AJ, Mitchell L, Dodds PS, Danforth CM. Climate change sentiment on Twitter: An unsolicited public opinion poll. PLoS ONE. 2015; 10(8):1–18. https://doi.org/10.1371/journal.pone. 0136092
27. Bollen J, Gonçalves B, van de Leemput I, Ruan G. The happiness paradox: your friends are happier than you. EPJ Data Science. 2017; 6(1):4. https://doi.org/10.1140/epjds/s13688-017-0100-1
28. Shi HX, Li XJ. A sentiment analysis model for hotel reviews based on supervised learning. vol. 3; 2011. p. 950–954.
29. Duan W, Cao Q, Yu Y, Levy S. Mining Online User-Generated Content: Using Sentiment Analysis Technique to Study Hotel Service Quality; 2013. p. 3119–3128. Available from: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6480220.
30. Ye Q, Zhang Z, Law R. Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Systems with Applications. 2009; 36(3 PART 2):6527–6535. https://doi.org/10.1016/j.eswa.2008.07.035
31. Claster WB, Cooper M, Sallis P. Thailand—Tourism and conflict. Modeling sentiment from twitter tweets using naive bayes and unsupervised artificial neural nets; 2010. p. 89–94.
32. TripAdvisor;. http://tripadvisor.com.
33. Timeout;. http://timeout.com.
34. Yelp;. http://yelp.com.
35. Boundary identification tool;. http://hamdikavak.com/research-and-projects/demo/maps-api-polygon/.
36. Consuming streaming data;. https://developer.twitter.com/en/docs/tutorials/consuming-streaming-data.
37. Guo D, Chen C. Detecting Non-personal and Spam Users on Geo-tagged Twitter Network. Transactions in GIS. 2014; 18(3):370–384. https://doi.org/10.1111/tgis.12101
38. Bassolas A, Lenormand M, Tugores A, Gonçalves B, Ramasco JJ. Touristic site attractiveness seen through Twitter. EPJ Data Science. 2016; 5(1). https://doi.org/10.1140/epjds/s13688-016-0077-1
39. Thelwall M, Buckley K, Paltoglou G, Cai D, Kappas A. Sentiment Strength Detection in Short Informal Text. Journal of The American Society for Information Science and Technology. 2010; 61(12):2544–2558. https://doi.org/10.1002/asi.21416
40. Bhattacharya S, Srinivasan P, Polgreen P. Engagement with Health Agencies on Twitter. PLoS ONE. 2014; 9(11). https://doi.org/10.1371/journal.pone.0112235
41. Desai T, Shariff A, Shariff A, Kats M, Fang X, Christiano C, et al. Tweeting the meeting: An in-depth analysis of Twitter activity at kidney week 2011. PLoS ONE. 2012; 7(7):1–9. https://doi.org/10.1371/journal.pone.0040253
42. Baylis P, Obradovich N, Kryvasheyeu Y, Chen H, Coviello L, Moro E, et al. Weather impacts expressed sentiment. arXiv preprint arXiv:170900071. 2017;.
43. Thelwall M. Heart and soul: Sentiment strength detection in the social web with sentistrength. vol. 5; 2013. p. 1–14.
44. https://www.ncdc.noaa.gov/news/meteorological-versus-astronomical-seasons;. https://www.ncdc.noaa.gov/news/meteorological-versus-astronomical-seasons.
45. Barth L, Kobourov SG, Pupyrev S. Experimental comparison of semantic word clouds. Lecture Notes in Computer Science. 2014; 8504 LNCS:247–258. https://doi.org/10.1007/978-3-319-07959-2_21
46. Joseph K, Landwehr PM, Carley KM. Two 1%s Don’t Make a Whole: Comparing Simultaneous Samples from Twitter’s Streaming API. In: Kennedy WG, Agarwal N, Yang SJ, editors. Social Computing, Behavioral-Cultural Modeling and Prediction. Cham: Springer International Publishing; 2014. p. 75–83.
47. Gore RJ, Diallo S, Padilla J. You Are What You Tweet: Connecting the Geographic Variation in America’s Obesity Rate to Twitter Content. PLOS ONE. 2015; 10(9):1–16. https://doi.org/10.1371/journal.pone.0133505
48. Choose Chicago Research and Analysis;. http://res.cloudinary.com/simpleview/image/upload/v1497467337/clients/chicago/Tourism_Volume_Update_4_27_17_9ba139a5-ab9e-4999-9169-1fa7921c43bc.pdf.