1 / 12

Introduction to ReviewMiner

Introduction to ReviewMiner. Hongning Wang Department of Computer Science University of Illinois at Urbana-Champaign wang296@Illinois.edu. Introduction. ReviewMiner system is developed based on the work of “Latent Aspect Rating Analysis” published in KDD’10 and KDD’11

ena
Download Presentation

Introduction to ReviewMiner

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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 KDD’10 and KDD’11 • 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/ReviewMiner

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

  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/ReviewMiner

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

  6. Search result page Aspect-weight based user profile Spatial result display Search box (keyword queries) Search result list Personalized recommendation results http://timan100.cs.uiuc.edu:8080/ReviewMiner

  7. Highlight, compare and find similar items Supported analysis functions: compare and find similar items regarding to user’s selection Aspect highlights of the selected item http://timan100.cs.uiuc.edu:8080/ReviewMiner

  8. Review analysis page Aspect-based item highlights Review meta-info: reviewers, date, aspect ratings Helpfulness vote Aspect-segmented review content http://timan100.cs.uiuc.edu:8080/ReviewMiner

  9. Aspect-based opinion summarization Analysis type selection: text summary v.s. graphical chart summary. Text summary of aspects http://timan100.cs.uiuc.edu:8080/ReviewMiner

  10. 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/ReviewMiner

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

  12. 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 suggestions • Feel free to send them to wang296@Illinois.edu • I am looking forward to further discussions and collaborations http://timan100.cs.uiuc.edu:8080/ReviewMiner

More Related