Deep Web Mining and Learning for Advanced Local Search CS8803 Advisor Prof Liu Yu Liu, Dan Hou Zhigang Hua, Xin Sun Yanbing Yu

Competitors Yahoo! Local Yelp CitySearch Google Local Yellow Page How to beat them?

Research Background Deep Web Crawling Sentimental Learning Sentimental Ranking Model Geo-credit Ranking Model Social Network for Businesses

Show Time! Local Biz Space

Architecture Database JDBC Query-based Crawler HTML Parser Sentimental Learner Super Local-Search Apache Server

Tools Open source social network platform Elgg, OpenSocial LAMP Server Linux+Apache+Mysql+PHP Google Map API, eg, Geocode,

Crawling Dynamic Pages

Crawling Dynamic Pages

Parsing Dynamic Pages

Database Design

Sentimental Learning

Sentimental Learning

Sentimental Learning Can we use ONE score to show how good/ bad the store is?

Sentimental Learning Objective Dataset To identify positive and negative opinions of a store Dataset Reviews represented by bag-of-terms Normalized TF-IDF feature (normalized) Two ways of sentiment representation Simply average the scores but “what you think good might be bad for me” Manual labeling 1 to 5 (“least satisfied” to “most satisfied”) consensus based time-accuracy tradeoff

Dimension Reduction High dimensionality Dimension Reduction 6857 tokens Memory limitation Possibly under-fitting Dimension Reduction PCA (Principle Component Analysis) 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 Original Dimension: 6857 Covariance Reserved: 95% Different Granularity Manual Labeling: Score Averaging: Downsampling Rate 40% 20% 4% Result Dimension 231 135 41 Downsampling Rate 40% 20% 4% Result Dimension 344 211 37

Sentimental learning Features used for sentimental learning: Vector Space Model (reviews/comments) 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to sentiments: Positive: good, happy, wonderful, excellent, awesome, great, ok, nice, etc Negative: bad, sad, ugly, outdated, shabby, stupid, wrong, awful, etc Most words unrelated to sentiments: e.g. buy, take, go, iPod, apple, comment, etc… Causing noise for sentimental learning!!

What we do? How to learn sentiments from a large set of features with lots of noise? 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 Average Rating is an alternative labeling for the entity Manual labeling: 1 (least satisfactory) – 5 (most satisfactory) Three persons do labeling, most-vote-adopted

Manual labeling vs. Average rating Machine learning Around 300 entities from local search, 6800 features after stop words removing and stemming Using different SVM kernels Avoiding overfit Leave-one-out estimation Nonlinearity of features Polynomial kernel achieves best performance Manual labeling Training more precise Labeling more consistent Rate averaging Training less precise Rating more random E.g. average(5, 5, 1) = 3

What we learned? Dimensionality reduction is necessary Term Vector Space Model (VSM) is huge in nature 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 Other methods to be used: Unsupervised learning (clustering) Gaussian Mixture Model (an alternative to learn sentiments, while it is difficult to know the # of hidden sentiments)

How to use learned sentiments? 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 Sentimental ranking model (SRM): SentiRank = a*ContentSim + (1-a)*SentiValue Empirically setting the parameter as “0.5”. Similar to PageRank PageRank = b*ContentSim + (1-b)*PageImportance

Geocoding 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 ) ) ) )

Geo-Sentimental Ranking Model (GSRM) Three Measurements Content Similarity -- term-frequency Sentimental Value -- sentimental learning Geo-distance -- Google Map API GSRM Ranking model

Example

Thank You ! QA time