Opinionseer interactive visualization of hotel customer feedback
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OpinionSeer : Interactive Visualization of Hotel Customer Feedback. Presented, Kane @2013-6-28. 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:

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Opinionseer interactive visualization of hotel customer feedback

OpinionSeer: Interactive Visualization of Hotel Customer Feedback

Presented, Kane

@2013-6-28


Opinionseer interactive visualization of hotel customer feedback

  • 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


Content

Content

  • Free Style to present to paper

    • Structure

    • General view

    • Systematic thinking

  • What have I learnt from reading the paper?

  • Conclusions

  • Recommended Websites


General view of the paper

General View of the Paper


Structure of the paper

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


Significance of the visualization

Significance of the Visualization

  • Help improve customer satisfaction and build customer trust and loyalty over time.

  • End Users

    • Hospitality researchers

    • Hotel managers


Introduction current problems

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.


Introduction cont

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


Opinionseer interactive visualization of hotel customer feedback

Data

  • Data Source

    • TripAdvisor, popular tourism cyber-intermediaries website.

  • Data Obtained

    • Hotel Data

    • Customer Data

    • Review Data

Basic Information

Free-text Comments


Conditional methods

Conditional Methods

  • Nvivo2

  • Classification

    • complaints

    • Opinions

  • Problems

    • Hard to work out with such large-scale data

    • Hard to understand


Task abstraction

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?


System view

System View

  • an opinion mining component

  • a subjective logic component

  • an opinion visualization component


Feature based opinion mining

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


Uncertainty modeling

Uncertainty Modeling

  • Define uncertainty with Gaussian distribution

    • Section 5.2,

  • “The room sure is tiny, yet very clean and comfy”


Opinion combination based on subjective logic

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


Opinion visualization

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


Opinion wheel opinion triangle

Opinion Wheel – Opinion Triangle

b+d+u=1;

an equilateral triangle.


Opinion wheel opinion rings

Opinion Wheel – Opinion Rings


Tag clouds detailed visualization of customer opin ion data

Tag Clouds: Detailed Visualization of Customer Opin-ion Data


Tag clouds cont

Tag Clouds – cont.


Temporal and geographic rings

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.


User interactions

User Interactions

  • Moving Projection Center


Experiments

Experiments


What have i learnt from reading the paper

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


Conclusion

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


Recommended websites

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/


Recommended websites cont

Recommended websites – cont.

  • Infographics | GOOD

    • http://www.good.is/infographics

  • Flickr: Info Graphics

    • http:[email protected]

  • 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/


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