Deep Web Mining and
Learning for Advanced
Local Search
CS8803 Advisor Prof Liu
Yu Liu, Dan Hou
Zhigang Hua, Xin Sun
Yanbing Yu
Competitors
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Yahoo! Local
Yelp
CitySearch
Google Local
Yellow Page
How to beat them?
Research Background
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Deep Web Crawling
Sentimental Learning
Sentimental Ranking Model
Geo-credit Ranking Model
Social Network for Businesses
Show Time!
Local Biz Space
Architecture
Query-based
Crawler
Sentimental
Learner
Super
Local-Search
HTML Parser
Apache
Server
JDBC
Database
Tools
Open source social network platform
Elgg, OpenSocial
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LAMP Server
Linux+Apache+Mysql+PHP
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Google Map API,
eg, Geocode,
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Crawling Dynamic Pages
Crawling Dynamic Pages
Yahoo!Local Keyword Trend
16000
Numer of Keywords
14000
12000
10000
8000
6000
4000
2000
0
1
2
3
4
5
6
7
8
9
10
11
Time (min)
12
13
14
15
16
17
18
19
20
Parsing Dynamic Pages
Database Design
Reviewer
PK
Category
Reviewer_ID
Name
URL
Business
PK
PK,FK1
PK
Business_ID
Category
Business_ID
Name
Phone
Address
Description
Rating
Source
Latitude
Longtitude
Review
FK1
FK2
Business_ID
Reviewer_ID
Date
Rating
Content
Term
PK
PK,FK1
PK
Term
Business_ID
Source
Frequency
Norm_Frequency
Sentimental Learning
Sentimental Learning
Sentimental Learning
Can we use ONE score
to show how good/ bad the store is?
Sentimental Learning
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Objective
• To identify positive and negative opinions of a store
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Dataset
• Reviews represented by bag-of-terms
• Normalized TF-IDF feature (normalized)
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Two ways of sentiment representation
• Simply average the scores
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but “what you think good might be bad for me”
• Manual labeling
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1 to 5 (“least satisfied” to “most satisfied”)
consensus based
time-accuracy tradeoff
Dimension Reduction
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High dimensionality
• 6857 tokens
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Memory limitation
Possibly under-fitting
Dimension Reduction
• PCA (Principle Component Analysis)
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an orthogonal linear transformation
transforms the data to a new coordinate system
retains the characteristics of the data set that
contribute most to its variance
• Get the most important features without losing
generality
Principle Component Analysis
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Original Dimension: 6857
Covariance Reserved: 95%
Different Granularity
• Manual Labeling:
Downsampling Rate
Result Dimension
40%
231
20%
4%
135
41
20%
211
4%
37
• Score Averaging:
Downsampling Rate
Result Dimension
40%
344
Sentimental learning
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Features used for sentimental learning:
• Vector Space Model (reviews/comments)
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Some keywords related to sentiments:
• Positive: good, happy, wonderful,
excellent, awesome, great, ok, nice, etc
• Negative: bad, sad, ugly, outdated,
shabby, stupid, wrong, awful, etc
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Most words unrelated to sentiments:
• e.g. buy, take, go, iPod, apple, comment,
etc…
• Causing noise for sentimental learning!!
What we do?
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How to learn sentiments from a large set
of features with lots of noise?
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Vector Space Model: MXN (Entity-Term, e.g.
6,000X20,000)
Dimensionality reduction (PCA)
Using supervised learning for sentimental learning
Human labeling vs. Average rating
• An online entity always includes many reviews
with each review containing a rating
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Average Rating is an alternative labeling for the
entity
• Manual labeling:
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1 (least satisfactory) – 5 (most satisfactory)
Three persons do labeling, most-vote-adopted
Manual labeling vs. Average rating
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Machine learning
Sentimental learning - manual labeling
• Around 300 entities from
local search, 6800 features
after stop words removing
and stemming
• Using different SVM kernels
• Avoiding overfit
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Leave-one-out estimation
• Nonlinearity of features
• Polynomial kernel achieves
best performance
Sentimental learning - average rating
80%
90%
Linear-kernel
RBF-kernel
Poly-kernel
80%
70%
60%
50%
40%
30%
20%
10%
0%
High-dimension
1
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60%
50%
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40%
20%
10%
0%
1
PCA-37
2
PCA-135
3
PCA-231
4
Manual labeling
Rate averaging
•
•
•
30%
High-dimension
2
• Training more precise
• Labeling more consistent
Linear-kernel
RBF-kernel
Poly-kernel
70%
PCA-41
PCA-211
3
PCA-344
4
Training less precise
Rating more random
E.g. average(5, 5, 1) = 3
What we learned?
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Dimensionality reduction is necessary
• Term Vector Space Model (VSM) is huge in
nature
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Human labeling is necessary
• Sentimental learning involved subjective judge
instead of objective judge.
• Human rating is very random because it is not
consistent across different people
• More labeling data is needed
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Other methods to be used:
• Unsupervised learning (clustering)
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Gaussian Mixture Model (an alternative to learn
sentiments, while it is difficult to know the # of
hidden sentiments)
How to use learned sentiments?
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Sentimental learning can be used to
improve ranking of local search
• Because sentimental value represents an
important metrics to evaluate the rank of an
entity
• Local search is influenced by the sentiment
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Sentimental ranking model (SRM):
• SentiRank = a*ContentSim + (1-a)*SentiValue
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Empirically setting the parameter as “0.5”.
• Similar to PageRank
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PageRank = b*ContentSim + (1-b)*PageImportance
Geocoding
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Geocoding of Addresses
For example , the geo-center of store AA National Auto Parts
Is located at 3410 Washington St, Phoenix, AZ,85009
Using Geocode, we can get the exact latitude and longtitude
(33.447708, -112.13246)
Haversine Formula of Great-circle distance:
Distance between two pairs of coordinates on sphere
= (3959 * acos( cos( radians(33.448) ) * cos( radians( lat ) ) * cos( radians( lng )
radians(-122) ) + sin( radians(-112.132) ) * sin(radians( lat ) ) ) )
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Geo-Sentimental Ranking Model
(GSRM)
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Three Measurements
1.
Content Similarity
-- term-frequency
3.
Sentimental Value
Geo-distance
-- sentimental learning
-- Google Map API
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GSRM Ranking model
2.
rank 
a*ContentSim + (1-a)*SentiValue
GeoDis tan ce
Example
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Thank You !
QA time