OpinionSeer : Interactive Visualization of Hotel Customer Feedback

1. OpinionSeer: Interactive Visualization of Hotel Customer Feedback Presented, Kane @2013-6-28

2. IEEE Transactions on Visualization and Computer Graphics • Vol. 16, no. 6, 2010 • Monthly since 2012 • About the Authors • Huamin Qu: • http://www.huamin.org/ • Yingcai Wu: • http://research.microsoft.com/en-us/um/people/ycwu/ • Newest: Visualizing Flow of Uncertainty through Analytical Processes, IEEE InfoVis 2012

3. Content • Free Style to present to paper • Structure • General view • Systematic thinking • What have I learnt from reading the paper? • Conclusions • Recommended Websites

4. General View of the Paper

5. Structure of the Paper • Introduction • Related Works • Data and Task Abstraction • Advanced and throughout the rest of paper • System View • Mining Opinion From Online Hotel Reviews • Opinion Visualization • Experiments and Discussion • Conclusions and Future Works

6. Significance of the Visualization • Help improve customer satisfaction and build customer trust and loyalty over time. • End Users • Hospitality researchers • Hotel managers

7. Introduction - Current Problems 1) Most of current Researches are visualizations based on the final mining results, not the process of visually examining and analyzing the feedbacks. 2) Current opinion visualization tools are not from multiple aspects which is essential for analysts to make a quick decision. 3) Cannot support the complex opinion visualization. 4) Count not the uncertainty or inaccuracy, which lead to wrong conclusions.

8. Introduction – cont. • Focus • Visual analysis of online hotel customer feedback • Diverse cultural backgrounds • Why challenging: • High-dimensional data • Ambiguity of language • Difficult to distinguish the positive and negative opinion

9. Data • Data Source • TripAdvisor, popular tourism cyber-intermediaries website. • Data Obtained • Hotel Data • Customer Data • Review Data Basic Information Free-text Comments

10. Conditional Methods • Nvivo2 • Classification • complaints • Opinions • Problems • Hard to work out with such large-scale data • Hard to understand

11. Task Abstraction Q.1 How is the deviation of a group of opinions from the average? Q.2 How could several groups of opinions be compared effectively? Q.3 How do people’s backgrounds affect their opinions on a hotel or a certain group of hotels? Q.4 What are the differences in the cultural background of two groups of customers who hold similar or different opinions? Q.5 Is there any conflict between free-text comments and the score ratings, e.g., a good review with low ratings? Q.6 Are there any localization or geography patterns regarding user opinions on a hotel or a certain group of hotels? Q.7 Are there any temporal patterns regarding the users opinions?

12. System View • an opinion mining component • a subjective logic component • an opinion visualization component

13. Feature Based Opinion Mining • Opinion Mining • Known as Sentiment Analysis • Document, Sentence Level • Feature Level, find comments on individual features of the product • OpinionSeer • Feature Level • Consider the uncertainty • Focus on visualization of the results

14. Uncertainty Modeling • Define uncertainty with Gaussian distribution • Section 5.2, • “The room sure is tiny, yet very clean and comfy”

15. Opinion Combination Based on Subjective Logic • Every extracted opinion contains positive, negative, and uncertainty scores for each feature. • < b , d , u , a > • b: positive opinion scores • d: negative opinion scores • u: uncertainty • a: priori probability in the absence of evidence • b+d+u=1 • AND & FUSION operators

16. Opinion Visualization • Includes • the opinion wheel • the tag cloud spreadsheet • a set of tailored user interactions • Design Principles • Effectiveness • Intuitiveness • Attraction • To convey the results of the opinion mining, from simple to complex, while keeping its intuitiveness

17. Opinion Wheel – Opinion Triangle b+d+u=1; an equilateral triangle.

18. Opinion Wheel – Opinion Rings

19. Tag Clouds: Detailed Visualization of Customer Opin-ion Data

20. Tag Clouds – cont.

21. Temporal and geographic rings • Parallel Sets [18] [18] R. Kosara, F. Bendix, and H. Hauser. Parallel Sets: Interactive exploration and visual analysis of categorical data. IEEE Transactions on Visualiza- tion and Computer Graphics, 12(4):558–568, 2006.

22. User Interactions • Moving Projection Center

23. Experiments

24. What have I learnt from reading the paper? • Visualization Design Principles • Can design for two end users, one may know more about techniques • Vector images are definitely more suitable for circular representation • Effectiveness, Intuitiveness, Attraction • Visualization • For multidimensional data with linear relationship • Circular Layout, as region statistics • Visualization for comments • Writing Skills

25. Conclusion • Learn how to scan a visualization paper • Try to focus on visualization pictures in paper • Keep some methods down • Figure out something that you may use in your own design • Thoroughly read a paper in a long time • Keep some sentences down, for later writing • Know how to use something across the whole paper

26. Recommended websites • Blog About Infographics and Data Visualization - Cool Infographics • http://www.coolinfographics.com/ • Alltop - Top Infographics News • http://infographics.alltop.com/ • Daily Infographic • http://dailyinfographic.com/ • Fast Company | Business + Innovation • http://www.fastcompany.com/ • FlowingData | Data Visualization, Infographics, and Statistics • http://flowingdata.com/

27. Recommended websites – cont. • Infographics | GOOD • http://www.good.is/infographics • Flickr: Info Graphics • http://www.flickr.com/groups/16135094@N00 • Information Is Beautiful • http://www.informationisbeautiful.net/ • Pinterest / Search results for infographics • http://pinterest.com/search/pins/?q=infographics • Infographics &amp; Data Visualization | Visual.ly • http://visual.ly/