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
• 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
Latent Aspect Rating Analysis
Aspect Segmentation
Reviews + overall ratings
+
Latent Rating Regression
Aspect segments
Term Weights
location:1
amazing:1
walk:1
anywhere:1
0.0
2.9
0.1
0.9
0.1
1.7
0.1
3.9
2.1
1.2
1.7
2.2
0.6
room:1
nicely:1
appointed:1
comfortable:1
nice:1
accommodating:1
smile:1
friendliness:1
attentiveness:1
Boot-stripping method
http://timan100.cs.uiuc.edu:8080/ReviewMiner
Aspect Rating
Aspect Weight
3.9
0.2
4.8
0.2
5.8
0.6
Latent!
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
A search-oriented interface
User registration and profile panel
Search vertical selection panel
Search box (keyword queries)
Trending searches
http://timan100.cs.uiuc.edu:8080/ReviewMiner
Search result page
Search box (keyword queries)
Aspect-weight based user profile
Spatial result display
Personalized recommendation results
Search result list
http://timan100.cs.uiuc.edu:8080/ReviewMiner
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
Review analysis page
Aspect-based item highlights
Review meta-info: reviewers, date, aspect ratings
Aspect-segmented review content
Helpfulness vote
http://timan100.cs.uiuc.edu:8080/ReviewMiner
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
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
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
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