opinion seer: interactive visualization of hotel customer...
TRANSCRIPT
![Page 1: Opinion Seer: Interactive Visualization of Hotel Customer ...vega.cs.kent.edu/~mikhail/classes/seminar.f11/Presentations/xiao.pdf · Step 1 :Opinion mining Feature-based opinion mining(i.e](https://reader036.vdocuments.us/reader036/viewer/2022070713/5ed029e174dd591ad628df51/html5/thumbnails/1.jpg)
Opinion Seer: Interactive Visualization of Hotel
Customer Feedback
Yingcai Wu, Furu Wei, Shixia Liu, Norman Au, Weiwei Cui, Hong Zhou, and Huamin Qu, Member, IEEE
Published by the IEEE Computer Society
Presented by Xiao Zhu
Kent state university
Nov-9-2011
Email: [email protected]
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![Page 2: Opinion Seer: Interactive Visualization of Hotel Customer ...vega.cs.kent.edu/~mikhail/classes/seminar.f11/Presentations/xiao.pdf · Step 1 :Opinion mining Feature-based opinion mining(i.e](https://reader036.vdocuments.us/reader036/viewer/2022070713/5ed029e174dd591ad628df51/html5/thumbnails/2.jpg)
Outline
Overview of Opinion Seer
Opinion mining
Feature-based opinion mining
Uncertainty Modeling
Subjective Logic
Opinion visualization
Opinion Triangle
Opinion Rings
Experiments
Conclusion
References
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![Page 3: Opinion Seer: Interactive Visualization of Hotel Customer ...vega.cs.kent.edu/~mikhail/classes/seminar.f11/Presentations/xiao.pdf · Step 1 :Opinion mining Feature-based opinion mining(i.e](https://reader036.vdocuments.us/reader036/viewer/2022070713/5ed029e174dd591ad628df51/html5/thumbnails/3.jpg)
Outline
Overview of Opinion Seer
Opinion mining
Feature-based opinion mining
Uncertainty Modeling
Subjective Logic
Opinion visualization
Opinion Triangle
Opinion Rings
Experiments
Conclusion
References
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![Page 4: Opinion Seer: Interactive Visualization of Hotel Customer ...vega.cs.kent.edu/~mikhail/classes/seminar.f11/Presentations/xiao.pdf · Step 1 :Opinion mining Feature-based opinion mining(i.e](https://reader036.vdocuments.us/reader036/viewer/2022070713/5ed029e174dd591ad628df51/html5/thumbnails/4.jpg)
Overview of Opinion Seer
Opinion Seer: An interactive visualization system that could visually analyze a large
collection of online hotel customer reviews
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![Page 5: Opinion Seer: Interactive Visualization of Hotel Customer ...vega.cs.kent.edu/~mikhail/classes/seminar.f11/Presentations/xiao.pdf · Step 1 :Opinion mining Feature-based opinion mining(i.e](https://reader036.vdocuments.us/reader036/viewer/2022070713/5ed029e174dd591ad628df51/html5/thumbnails/5.jpg)
Outline
Overview of Opinion Seer
Opinion mining
Feature-based opinion mining
Uncertainty Modeling
Subjective Logic
Opinion visualization
Opinion Triangle
Opinion Rings
Experiments
Conclusion
References
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![Page 6: Opinion Seer: Interactive Visualization of Hotel Customer ...vega.cs.kent.edu/~mikhail/classes/seminar.f11/Presentations/xiao.pdf · Step 1 :Opinion mining Feature-based opinion mining(i.e](https://reader036.vdocuments.us/reader036/viewer/2022070713/5ed029e174dd591ad628df51/html5/thumbnails/6.jpg)
Step 1 :Opinion mining
Feature-based opinion mining(i.e. room, location, cleanliness, service and hotel)
Split document into a collection of sentences
Use a opinion keyword dictionary to decide the feature and opinion score
i.e.“The service is perfect.”
For each sentence, counting the number of positive and negative keywords
Get the overall opinion about the hotel by grouping the opinion of features
Uncertainty Modeling
i.e. “The room sure is tiny, yet very clean and comfy.”
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![Page 7: Opinion Seer: Interactive Visualization of Hotel Customer ...vega.cs.kent.edu/~mikhail/classes/seminar.f11/Presentations/xiao.pdf · Step 1 :Opinion mining Feature-based opinion mining(i.e](https://reader036.vdocuments.us/reader036/viewer/2022070713/5ed029e174dd591ad628df51/html5/thumbnails/7.jpg)
Outline
Overview of Opinion Seer
Opinion mining
Feature-based opinion mining
Uncertainty Modeling
Subjective Logic
Opinion visualization
Opinion Triangle
Opinion Rings
Experiments
Conclusion
References
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![Page 8: Opinion Seer: Interactive Visualization of Hotel Customer ...vega.cs.kent.edu/~mikhail/classes/seminar.f11/Presentations/xiao.pdf · Step 1 :Opinion mining Feature-based opinion mining(i.e](https://reader036.vdocuments.us/reader036/viewer/2022070713/5ed029e174dd591ad628df51/html5/thumbnails/8.jpg)
Step 2: Subjective Logic
An opinion vector<p, n, u> ( p + n + u=1)
AND operator:
combine the opinions of a customer on different features (at feature level)
FUSION operator:
combine of different customers on the same feature(at the hotel level)
Use AND operator to get multiple overall opinions from different customers,
then use FUSION operator to get the average opinion of the customers
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![Page 9: Opinion Seer: Interactive Visualization of Hotel Customer ...vega.cs.kent.edu/~mikhail/classes/seminar.f11/Presentations/xiao.pdf · Step 1 :Opinion mining Feature-based opinion mining(i.e](https://reader036.vdocuments.us/reader036/viewer/2022070713/5ed029e174dd591ad628df51/html5/thumbnails/9.jpg)
Outline
Overview of Opinion Seer
Opinion mining
Feature-based opinion mining
Uncertainty Modeling
Subjective Logic
Opinion visualization
Opinion Triangle
Opinion Rings
Experiments
Conclusion
References
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![Page 10: Opinion Seer: Interactive Visualization of Hotel Customer ...vega.cs.kent.edu/~mikhail/classes/seminar.f11/Presentations/xiao.pdf · Step 1 :Opinion mining Feature-based opinion mining(i.e](https://reader036.vdocuments.us/reader036/viewer/2022070713/5ed029e174dd591ad628df51/html5/thumbnails/10.jpg)
Step 3. Opinion visualization
Opinion Triangle
p + n + u = 1
Dp +Dn + Du = 1
Opinion Rings
Color: the different dimension
Size: the number of customers in a
particular dimension
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![Page 11: Opinion Seer: Interactive Visualization of Hotel Customer ...vega.cs.kent.edu/~mikhail/classes/seminar.f11/Presentations/xiao.pdf · Step 1 :Opinion mining Feature-based opinion mining(i.e](https://reader036.vdocuments.us/reader036/viewer/2022070713/5ed029e174dd591ad628df51/html5/thumbnails/11.jpg)
Outline
Overview of Opinion Seer
Opinion mining
Feature-based opinion mining
Uncertainty Modeling
Subjective Logic
Opinion visualization
Opinion Triangle
Opinion Rings
Experiments
Conclusion
References
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![Page 12: Opinion Seer: Interactive Visualization of Hotel Customer ...vega.cs.kent.edu/~mikhail/classes/seminar.f11/Presentations/xiao.pdf · Step 1 :Opinion mining Feature-based opinion mining(i.e](https://reader036.vdocuments.us/reader036/viewer/2022070713/5ed029e174dd591ad628df51/html5/thumbnails/12.jpg)
Experiment
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Demonstrate the usefulness of the uncertainty modeling
![Page 13: Opinion Seer: Interactive Visualization of Hotel Customer ...vega.cs.kent.edu/~mikhail/classes/seminar.f11/Presentations/xiao.pdf · Step 1 :Opinion mining Feature-based opinion mining(i.e](https://reader036.vdocuments.us/reader036/viewer/2022070713/5ed029e174dd591ad628df51/html5/thumbnails/13.jpg)
Outline
Overview of Opinion Seer
Opinion mining
Feature-based opinion mining
Uncertainty Modeling
Subjective Logic
Opinion visualization
Opinion Triangle
Opinion Rings
Experiments
Conclusion
References
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![Page 14: Opinion Seer: Interactive Visualization of Hotel Customer ...vega.cs.kent.edu/~mikhail/classes/seminar.f11/Presentations/xiao.pdf · Step 1 :Opinion mining Feature-based opinion mining(i.e](https://reader036.vdocuments.us/reader036/viewer/2022070713/5ed029e174dd591ad628df51/html5/thumbnails/14.jpg)
Conclusion
Based on user feedback, 80-90% of the user think Opinion Seer is very smart and useful
Not just hotel customer feedback, Opinion Seer is useful for other products and services
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![Page 15: Opinion Seer: Interactive Visualization of Hotel Customer ...vega.cs.kent.edu/~mikhail/classes/seminar.f11/Presentations/xiao.pdf · Step 1 :Opinion mining Feature-based opinion mining(i.e](https://reader036.vdocuments.us/reader036/viewer/2022070713/5ed029e174dd591ad628df51/html5/thumbnails/15.jpg)
Outline
Overview of Opinion Seer
Opinion mining
Feature-based opinion mining
Uncertainty Modeling
Subjective Logic
Opinion visualization
Opinion Triangle
Opinion Rings
Experiments
Conclusion
References
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![Page 16: Opinion Seer: Interactive Visualization of Hotel Customer ...vega.cs.kent.edu/~mikhail/classes/seminar.f11/Presentations/xiao.pdf · Step 1 :Opinion mining Feature-based opinion mining(i.e](https://reader036.vdocuments.us/reader036/viewer/2022070713/5ed029e174dd591ad628df51/html5/thumbnails/16.jpg)
References
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[1] N. Au, D. Buhalis, and R. Law. Complaints on the online environment the case of hong kong hotels. In W. H¨opken, 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-VerlagWien, 2010.
[3] C. Chen, F. Ibekwe-SanJuan, E. SanJuan, and C. Weaver. Visual analysis of conflicting pinions. In IEEE Symposium On Visual Analytics Science And Technology, pages 35 – 42, 2006.
Thank you