1. Understanding User Intentions by Computational Techniques
Hongning Wang
Department of Computer Science University of Illinois at Urbana-Champaign Urbana IL, USA

2. Research Summary Latent Aspect Rating Analysis [KDD’10, 11]
Great hotel = price<$60, location in downtown
Good MP3 player = large memory, long batter life
Relevant news = most recent report
Latent Aspect Rating Analysis [KDD’10, 11]
Online Forum Discussion Structure Modeling [SIGIR’11]
Latent Topical Structure Modeling [ACL’11]
4/19/2019

3. Information buried in text content
4/19/2019

4. Latent Aspect Rating Analysis [KDD’10, 11]
Entity
Review
Aspects
Aspect Rating
Aspect Weight
Location
Excellent location in walking distance to Tiananmen Square and shopping streets. That’s the best part of this hotel! The rooms are getting really old. Bathroom was nasty. The fixtures were falling off, lots of cracks and everything looked dirty. I don’t think it worth the price. Service was the most disappointing part, especially the door men. this is not how you treat guests, this is not hospitality.
location
amazing
walk
anywhere
0.86
0.04
0.10
Room
room
dirty
appointed
smelly
Service
terrible
front-desk
smile
unhelpful
4/19/2019

5. LARA Applications I User rating emphasis analysis
Reviewers emphasize ‘value’ aspect would prefer ‘cheap’ hotels
City
AvgPrice
Group
Val/Loc
Val/Rm
Val/Ser
Amsterdam
241.6
top-10
190.7
214.9
221.1
bot-10
270.8
333.9
236.2
San Francisco
261.3
214.5
249.0
225.3
321.1
311.1
311.4
Florence
272.1
269.4
248.9
220.3
298.9
293.4
292.6
It would be interesting to analyze the user’s rating emphasis via the identified aspect weights. We rank the hotels according to weight ratio between value and other aspects, and calculate the price for the top 10 and bottom 10 hotels according to this ranking. We could find that hotels with higher emphasis on value aspect usually possess lower price than then average, and vice versa.
4/19/2019

6. LARA Applications II User rating behavior analysis
Reviewers focus differently on ‘expensive’ and ‘cheap’ hotels
Expensive Hotel
Cheap Hotel
5 Stars
3 Stars
1 Star
Value
0.134
0.148
0.171
0.093
Room
0.098
0.162
0.126
0.121
Location
0.074
0.161
0.082
Cleanliness
0.081
0.163
0.116
0.294
Service
0.251
0.101
0.049
Besides the performance evaluations, we also want to demonstrate the various applications enabled by the proposed LRR model. We divide the hotels into expensive and cheap hotels according to their actual price. We could find that people give expensive hotels 5 stars mainly because of their excellent service while cleanliness is the bottle neck. And for the cheap hotels, value is the most important factor for higher ratings, at the mean time cleanliness is still the major concerns for the lower ratings.

7. Oops, time is limited... Probabilistic model with rich features
Online Forum Discussion Structure Modeling [SIGIR’11]
Latent Topical Structure Modeling [ACL’11]
Probabilistic model with rich features
Topical transition structure
Transition probability
Emission probability
Initial topic
Content topic proportion
4/19/2019

8. Future Direction Mining rich user-generated-data
Clicks, sharing, like
Analyzing social interactions
Friendship, following
4/19/2019

9. Thank you!
Q&A
4/19/2019

10. Information Hidden in Structures
Structure is not always visible
Flat View
Threaded View
v.s.
4/19/2019

11. Online Forum Discussion Structure Modeling
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12. Online Forum Discussion Structure Modeling
Probabilistic model with rich features
Post attributes
User interactions
4/19/2019

13. Recognizing and modeling document structure
Languages are intrinsically cohesive and coherent
4/19/2019

14. Latent Topical Structure Modeling
Transition probability
Emission probability
Initial topic
Content topic proportion
4/19/2019

15. LARA Applications I Corpus specific word sentimental orientation
Uncover sentimental information directly from the data
Value
Rooms
Location
Cleanliness
resort 22.80
view 28.05
restaurant 24.47
clean 55.35
value 19.64
comfortable 23.15
walk 18.89
smell 14.38
excellent 19.54
modern 15.82
bus 14.32
linen 14.25
worth 19.20
quiet 15.37
beach 14.11
maintain 13.51
bad
carpet -9.88
wall
smelly -0.53
money
smell -8.83
bad -5.40
urine -0.43
terrible
dirty -7.85
road -2.90
filthy -0.42
overprice -9.06
stain -5.85
website -1.67
dingy -0.38

16. Learned Topical Structure
4/19/2019