The Community for Technology Leaders
RSS Icon
Issue No.06 - November/December (2010 vol.16)
pp: 1109-1118
The rapid development of Web technology has resulted in an increasing number of hotel customers sharing their opinionson the hotel services. Effective visual analysis of online customer opinions is needed, as it has a significant impact on buildinga successful business. In this paper, we present OpinionSeer, an interactive visualization system that could visually analyze alarge collection of online hotel customer reviews. The system is built on a new visualization-centric opinion mining technique thatconsiders uncertainty for faithfully modeling and analyzing customer opinions. A new visual representation is developed to conveycustomer opinions by augmenting well-established scatterplots and radial visualization. To provide multiple-level exploration, weintroduce subjective logic to handle and organize subjective opinions with degrees of uncertainty. Several case studies illustrate theeffectiveness and usefulness of OpinionSeer on analyzing relationships among multiple data dimensions and comparing opinionsof different groups. Aside from data on hotel customer feedback, OpinionSeer could also be applied to visually analyze customeropinions on other products or services.
opinion visualization, radial visualization, uncertainty visualization
Furu Wei, Shixia Liu, Norman Au, Weiwei Cui, Hong Zhou, Huamin Qu, "OpinionSeer: Interactive Visualization of Hotel Customer Feedback", IEEE Transactions on Visualization & Computer Graphics, vol.16, no. 6, pp. 1109-1118, November/December 2010, doi:10.1109/TVCG.2010.183
[1] N. Au, D. Buhalis, and R. Law., Complaints on the online environment the case of hong kong hotels. In W. Hopken, U. Gretzel, and R. Law editors, , Information and Communication Technologies in Tourism 2009, pages 73–85. Springer-Verlag Wien, 2009.
[2] N. Au, R. Law, and D. Buhalis., The impact of culture on ecomplaints: Evidence from the chinese consumers in hospitality organization. In U. Gretzel, R. Law, and M. Fuchs editors, , Information and Communication Technologies in Tourism 2010, pages 285–296. Springer-Verlag Wien, 2010.
[3] C. Chen, F. Ibekwe-SanJuan, E. SanJuan, and C. Weaver, Visual analysis of conflicting opinions. In IEEE Symposium On Visual Analytics Science And Technology, pages 35–42, 2006.
[4] C. D. Correa, Y.-H. Chan, and K.-L. Ma, A framework for uncertainty aware visual analytics. In IEEE Symposium on Visual Analytics Science and Technology, pages 51–58, 2009.
[5] W. Cui, Y. Wu, S. Liu, F. Wei, M. X. Zhou, and H. Qu, Context preserving dynamic word cloud visualization. In IEEE Pacific Visualization Symposium, pages 121–128, 2010.
[6] G. Draper and R. Riesenfeld, Who votes for what? a visual query language for opinion data. IEEE Transactions on Visualization and Computer Graphics, 14 (6): 1197–1204, 2008.
[7] G. M. Draper, Y. Livnat, and R. F. Riesenfeld, A survey of radial methods for information visualization. IEEE Transactions on Visualization and Computer Graphics, 15 (5): 759–776, 2009.
[8] M. Gamon, A. Aue, S. Corston-Oliver, and E. Ringger, Pulse: Mining customer opinions from free text. In International Symposium on Intelligent Data Analysis, pages 121–132, 2005.
[9] M. Gamon, S. Basu, D. Belenko, D. Fisher, M. Hurst, and A. C. Konig, BLEWS: Using blogs to provide context for news articles. In AAAI Conference on Weblogs and Social Media, pages 60–67, 2008.
[10] M. L. Gregory, N. Chinchor, P. Whitney, R. Carter, E. Hetzler, and A. Turner, User-directed sentiment analysis: Visualizing the affective content of documents. In Workshop on Sentiment and Subjectivity in Text, pages 23–30, 2006.
[11] D. Houser and J. Wooders, Reputation in auctions: Theory and evidence from ebay. Journal of Economics & Management Strategy, 15 (2): 353–369, 2006.
[12] M. Hu and B. Liu, Mining and summarizing customer reviews. In ACM SIGKDD international conference on Knowledge discovery and data mining, pages 168–177, 2004.
[13] M. Hu and B. Liu, Mining opinion features in customer reviews. In AAAI'04: Proceedings of the 19th national conference onArtifical intelligence, pages 755–760, 2004.
[14] A. Jøsang, The consensus operator for combining beliefs. Artificial Intelligence, 141 (1): 157–170, 2002.
[15] A. Jøsang, Subjective Logic. draft, available at : http : //persons . unik . no/ josang/papers subjective_logic . pdf, 2009.
[16] K. A. Keng, D. Richmond, and S. Hans, Determinants of consumer complaint behavior: A study of singapore consumers. Journal of International Consumer Marketing, 8 (2): 59–76, 1995.
[17] S.-M. Kim and E. Hovy, Determining the sentiment of opinions. In Proceedings of international conference on Computational Linguistics, pages 1367–373, 2004.
[18] R. Kosara, F. Bendix, and H. Hauser, Parallel Sets: Interactive exploration and visual analysis of categorical data. IEEE Transactions on Visualization and Computer Graphics, 12 (4): 558–568, 2006.
[19] C. C. Lee and C. Hu, Analyzing hotel customers E-complaints from an internet complaint forum. Journal of Travel & Tourism Marketing, 17 (2 &3): 167–181, 2005.
[20] B. Liu, M. Hu, and J. Cheng, Opinion observer: analyzing and comparing opinions on the web. In International Conference on World Wide Web, pages 342–351, 2005.
[21] S. Morinaga, K. Yamanishi, K. Tateishi, and T. Fukushima, Mining product reputations on the web. In ACM SIGKDD international conference on Knowledge discovery and data mining, pages 341–349, 2002.
[22] D. Oelke, M. Hao, C. Rohrdantz, D. A. Keim, U. Dayal, L.-E. Haug, and H. Janetzko, Visual opinion analysis of customer feedback data. In IEEE Symposium On Visual Analytics Science And Technology, pages 187–194, 2009.
[23] B. Pang and L. Lee, Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2 (1-2): 1–135, 2008.
[24] B. Pang, L. Lee, and S. Vaithyanathan, Thumbs up?: sentiment classification using machine learning techniques. In Conference on Empirical methods in natural language processing, pages 79–86, 2002.
[25] A.-M. Popescu and O. Etzioni, Extracting product features and opinions from reviews. In Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pages 339–346, 2005.
[26] B. E. Rogowitz, L. A. Treinish, S. Bryson, How not to lie with visualization. Computers in Physics, 10 (3): 268–273, 1996.
[27] I. E. Vermeulen and D. Seegers, Tried and tested: The impact of online hotel reviews on consumer consideration. Tourism Management, 30 (1): 123–127, 2008.
[28] F. B. Viegas, M. Wattenberg, and J. Feinberg, Participatory visualization with wordle. IEEE Transactions on Visualization and Computer Graphics, 15 (6): 1137–1144, 2009.
[29] F. Wanner, C. Rohrdantz, F. Mansmann, and D. A. Keim, Visual sentiment analysis of RSS news feeds featuring the us presidential election in 2008. In Workshop on Visual Interfaces to the Social and the Semantic Web, 2009.
[30] C. Ware, Information Visualization: Perception for Design. Morgan Kaufmann 2nd edition, 2004.
[31] J. Yang, M. O. Ward, and E. A. Rundensteiner, Interring: An interactive tool for visually navigating and manipulating hierarchical structures. In IEEE Symposium on Information Visualization, pages 77–84, 2002.
13 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool