1. Introduction to ReviewMiner Hongning Wang Department of Computer Science University of Illinois at Urbana-Champaign wang296@Illinois.edu

2. Introduction ReviewMiner system is developed based on the work of Latent Aspect Rating Analysis published in KDD10 and KDD11 Hongning Wang, Yue Lu and Chengxiang Zhai. Latent Aspect Rating Analysis on Review Text Data: A Rating Regression Approach. The 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'2010), p783-792, 2010. Hongning Wang, Yue Lu and ChengXiang Zhai. Latent Aspect Rating Analysis without Aspect Keyword Supervision. The 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'2011), P618-626, 2011. http://timan100.cs.uiuc.edu:8080/test-app

3. Latent Aspect Rating Analysis Reviews + overall ratingsAspect segments location:1 amazing:1 walk:1 anywhere:1 0.1 1.7 0.1 3.9 nice:1 accommodating:1 smile:1 friendliness:1 attentiveness:1 Term WeightsAspect Rating 0.0 2.9 0.1 0.9 room:1 nicely:1 appointed:1 comfortable:1 2.1 1.2 1.7 2.2 0.6 Aspect SegmentationLatent Rating Regression 3.9 4.8 5.8 Aspect Weight 0.2 0.6 Boot-stripping method + Latent! http://timan100.cs.uiuc.edu:8080/test-app

4. Functionalities Keyword-based item retrieval E.g., search hotels by name, location, brand Aspect-based review analysis Segment review content into aspects Predict aspect ratings based on overall ratings and review text content Infer latent aspect weights the reviewer has put over the aspects when generating the review content Aspect-based item comparison Predicted aspect rating/weight based quantitative comparison Text content based qualitative comparsion http://timan100.cs.uiuc.edu:8080/test-app

5. A search-oriented interface User registration and profile panel Search box (keyword queries) Trending searches Search vertical selection panel Aspect-weight based user profile http://timan100.cs.uiuc.edu:8080/test-app

6. Search result page Search result list Personalized recommendation results Search box (keyword queries) http://timan100.cs.uiuc.edu:8080/test-app

7. Review analysis page Review meta-info: reviewers, date, aspect ratings Aspect-based item highlights Aspect-segmented review content http://timan100.cs.uiuc.edu:8080/test-app

8. Enable aspect-based analysis Move mouse over the displayed item title Aspect-based review analysisAspect-based item comparison (use check box to select more than one item) Aspect-based similar hotel finding Aspect-based item highlight (click the image) http://timan100.cs.uiuc.edu:8080/test-app

9. Aspect-based review analysis Analysis type selection: aspect ratings, aspect weights, aspect mentions and aspect summarization. Analysis result display panel (move mouse over the chart to find the text highlights) http://timan100.cs.uiuc.edu:8080/test-app

10. Aspect-based item comparison Analysis result display panel (move mouse over the chart to find the text highlights) Analysis type selection: aspect ratings, aspect weights, aspect mentions and aspect summarization. Aspect selection panel http://timan100.cs.uiuc.edu:8080/test-app

11. Comments More search verticals to be added Our solution of LARA is general and can be easily extended to multiple domains Restaurant reviews from Yelp.com and electric product reviews from amazon.com will be included soon Your valuable comments and suggestion Feel free to send to wang296@Illinois.eduwang296@Illinois.edu I am looking forward to further discussions and collaborations http://timan100.cs.uiuc.edu:8080/test-app