Introduction to ReviewMiner

Hongning Wang Department of Computer Science

University of Illinois at Urbana-Champaignwang296@Illinois.edu

http://timan100.cs.uiuc.edu:8080/test-app

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/test-app

Latent Aspect Rating Analysis

Reviews + overall ratings Aspect segmentslocation:1amazing:1walk:1anywhere:1

0.11.70.13.9

nice:1accommodating:1smile:1friendliness:1attentiveness:1

Term Weights Aspect Rating

0.02.90.10.9

room:1nicely:1appointed:1comfortable:1

2.11.21.72.20.6

Aspect Segmentation

Latent Rating Regression

3.9

4.8

5.8

Aspect Weight

0.2

0.2

0.6

Boot-stripping method

+

Latent!

http://timan100.cs.uiuc.edu:8080/test-app

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

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

Search result page

Search result list Personalized recommendation results

Search box (keyword queries)

http://timan100.cs.uiuc.edu:8080/test-app

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

Enable aspect-based analysis

• Move mouse over the displayed item title

Aspect-based review analysis Aspect-based item comparison (use check box to select more than one item)

Aspect-based similar hotel findingAspect-based item highlight (click the image)

http://timan100.cs.uiuc.edu:8080/test-app

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

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

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.edu• I am looking forward to further discussions and collaborations