Abstract
With the development of Internet, an increasing number of people choose to travel during the holidays and post travel information on the tourism products and services through the smart devices anytime and anywhere. Because tourism network opinions have a significant impact on tourism activities and the whole tourist trade, they have attracted the attention of tourism management department. Through analyzing the tourism User-Generated Content data obtained from Mafengwo, which is one of the influential tourism social networking sites, this paper studies and designs a visual analytic system—for tourism network opinion—VisTravel. The VisTravel system includes three main views: the interactive filtering view can select travel notes and comments, the content view is used to express comments and tourists’ emotion changes, and the pop-up information view shows social relationships of tourist and tag cloud of comments. In this paper, tourists’ hierarchical structure is put forward to explore the tourist’s social networking relationships, and the stacked group is used to analyze tourists’ sentiment changes. Experimental results show that the proposed VisTravel system can effectively analyze tourists’ regional tendency and emotional changes. It can also help the tourism management department more thoroughly understand the tourism network opinion in time.
Graphical abstract
A screenshot of VisTravel. The system includes eight views. (a Temporal histogram, filtering subset of travel notes. b Map notes view, filtering subset of travel comments. c Travel notes view, selecting one of subset of travel notes. d Sentiment analyzer view, indicating the changes of tourists’ sentiment. e Comment list view, displaying the raw comment data. f Hierarchical structure view, showing tourists’ geographical relationships in comments. g Tag cloud, showing key words of the comments.)
Similar content being viewed by others
References
Banerjee S, Ramanathan K, Gupta A (2007) Clustering short text using Wikipedia. In: Proceedings of the 30th annual international ACM SIGIR conference. Amsterdam, pp 787–788, doi:10.1145/1277741.1277909
Bigné JE, Andreu L (2004) Emotions in segmentation: an empirical study. Ann Tour Res 31(3):682–696. doi:10.1016/j.annals.2003.12.018
Cai WW, Wu YC, Liu SX, et al (2010) Context preserving dynamic word cloud visualization. In: Proceedings of IEEE pacific visualization symposium (PacificVis 2010), Taipei, pp 42–53. doi:10.1109/MCG.2010.102
Caschera MC, Ferri F, Grifoni P, et al (2009) Multidimensional visualization system for travel social networks. In: Proceedings of six international conference on information technology: new generations, ITNG 2009, Las Vegas, pp 27–29. doi:10.1109/ITNG.2009.236
Feng N, Li JY, Zhang GJ (2013) A study of the structure of China’s mainstream online tourism information network based on SNA. In: Proceedings of international symposium, GRMSE 2013, Wuhan, pp 541–552. doi:10.1007/978-3-642-41908-9_55
Francalanci C, Hussain A (2015) A visual analysis of social influencers and influence in the tourism domain. In: Proceedings of the international conference, Lugano, pp 19–32. doi:10.1007/978-3-319-14343-9_2
Fredrickson BL, Losada MF (2005) Positive affect and the complex dynamics of human flourishing. Am Psychol 60(7):678–686. doi:10.1037/0003-066X.60.7.678
Garcia E, Garcia A, Hilera JR (2010) Turisbook: social network of tourism with geographical information. In: Proceedings of third world submit on the knowledge society (WSKS 2013), pp 172–179. doi:10.1007/978-3-642-16318-0_20
Goossens C (2000) Tourism information and pleasure motivation. Ann Tour Res 27(2):301–321. doi:10.1016/S0160-7383(99)00067-5
Hu X, Sun N, Zhang C, et al (2009) Exploiting internal and external semantics for the clustering of short texts using world knowledge. In: Proceedings of the 18th ACM conference on Information and knowledge management, New York, pp. 919–928. doi:10.1145/1645953.1646071
Kumar S, Barbier G, Abbasi MA, et al (2011) TweetTracker: an analysis tool for humanitarian and disaster relief. In: Proceedings of the 5th international AAAI conference on weblogs and social media, note: (ICWSM-11)
Laros FJM, Steenkamp J-BEM (2005) Emotions in consumer behavior: a hierarchical approach. J Bus Res 58(10):1437–1445. doi:10.1016/j.jbusres.2003.09.013
Liao Q, Shi L (2013) She gets a sports car from our donation: rumor transmission in a Chinese microblogging community. In: Proceedings CSCW 2013, San Antonio, pp 587–598. doi:10.1145/2441776.2441842
MacEachren AM, Jaiswal A, Robinson AC, et al (2011) Senseplace2: Geotwitter analytics support for situational awareness. In: IEEE conference on visual analytics science and technology (VAST), pp 181–190. doi:10.1109/VAST.2011.6102456
Marcus A, Bernstein MS, Badar O, et al (2011) TwitInfo: aggregating and visualizing microblogs for event exploration. In: Proceedings of the SIGCHI conference on human factors in computing systems, pp 227–236. doi:10.1145/1978942.1978975
Mihalcea R, Tarau P (2004). TextRank: bringing order into texts. Department of Computer Science University of North Texas. In: Proceedings of the conference on empirical methods in natural language processing (EMNLP 2004), Barcelona, pp 404–411
Nakatoh T, Yin C, Matsuura H, et al (2011) Visualization of tourism information using WordNet. In: Proceeding of 3rd international conference on awareness science and technology(iCAST), Dalian, pp 412–417. doi:10.1109/ICAwST.2011.6163110
Nawijin J (2011) Determinants of daily happiness on vacation. J Travel Res 50(5):559–566. doi:10.110.1177/0047287510379164
Qu Y, Huang C, Zhang P, et al (2011) Microblogging after a major disaster in china: a case study of the 2010 yushu earthquake. In: Proceedings of the ACM conference on computer supported cooperative work, pp 25–34. doi:10.1145/1958824.1958830
Ren D, Zhang X, Wang Z, et al (2014). WeiboEvents: a crowd sourcing weibo visual analytic system. In: Proceedings of IEEE pacific visualization symposium (PacificVis 2014), Yokohama, pp 330–334. doi:10.1109/PacificVis.2014.38
Sankaranarayanan J, Samet H, Teitler BE, et al (2009) TwitterStand: news in tweets. In: Proceedings ACM GIS’09, Seattle, pp 42–51. doi:10.1145/1653771.1653781
Sriram B, Fuhry D, Demir E, et al (2010) Short text classfication in twitter to improve information filtering. In: Proceedings of the 33rd international ACM SIGIR conference on research and development in information retrieval. ACM, New York, pp 841–842. doi:10.1145/1835449.1835643
Thom D, Bosch H, Koch S, et al (2012) Spatiotemporal anomaly detection through visual analysis of geolocated twitter messages. In: Proceedings of IEEE pacific visualization symposium, pp 41–48. doi:10.1109/PacificVis.2012.6183572
Wirjono AA, Lincoln ZSR, William, et al (2011) Information visualization for tourist and travelling in Indonesia. In: Proceedings of first international conference on advances in computing and communications, Kochi, pp 130–137. doi:10.1007/978-3-642-22714-1_14
Yu L, Asur S, Huberman BA (2012) Artificial inflation: the true story of trends in SinaWeibo. http://arxiv.org/abs/1202.0327
Acknowledgments
The authors would like to thank the leaders of the Mianyang Municipal Tourism Administration for participating this project as domain experts. This work is partially supported by National Natural Science Foundation of China (Grant No. 61303127), Project of Science and Technology Department of Sichuan Province (Grant Nos. 2014SZ0223, 2014GZ0100, 2015GZ0212), Key Program of Education Department of Sichuan Province (Grant Nos. 11ZA130, 13ZA0169), and Seedling Project Fund Project in Sichuan Province (Grant No. 2014-043).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Li, Q., Wu, Y., Wang, S. et al. VisTravel: visualizing tourism network opinion from the user generated content. J Vis 19, 489–502 (2016). https://doi.org/10.1007/s12650-015-0330-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12650-015-0330-x