Mining Product Reputations On the Web

Agenda
Introduction
Reputation mining system
Opinion extraction
Reputation analysis
Experiments
Concluding remarks

Introduction
Knowing the reputation of your own and/or competitors’ products is important.
Problems:
Handling the large volume of open answer by manually
Gather the large volume of high quality survey data.
Solution:
New framework for automatically collecting and analyzing opinions on the internet.
Combining the opinion extraction technique and text mining methodologies.
Previously employed in Survey Analyzer (SA. Is a trademark of NEC corporation in Japan.)
Text mining focus on open answer
Text classification through close answer or manual labeling

Introduction (cont.)
Opinion extraction
Collects people’s opinions about products from the internet and attaches three labels:
The name of product referred to
The positive/negative nature of opinion
opinion-likeliness (a numerical value the degree of system confidence that the extracted statement is.)
Labeled opinions put into an opinion database.
Reputation analysis
Rule analysis (Extracting characteristic words)
“ monochrome” and “inexpensive”, “lightweight” and “convenient”
Stochastic complexity
Co-occurrence analysis
Typical sentence analysis
Correspondence analysis
Two-dimensional positioning map
Display the corresponding relationships among the target categories.

Reputation mining system

Opinion extraction
Web page collection module
Use a crawler to collect web pages relevant to input product names.
Positive/negative determining module
Checked with a previously prepared “evaluation-expression dictionary”
“ fast”, “good”, “light” are positive expression
“ heavy”, “easily broken”, “noisy” are negative expression

Opinion extraction (cont.)
Opinion-likeliness calculation module
Calculate its opinion-likeliness score
A real value ranging from 1 to 5
The higher score, the higher likelihood
Using syntactic property rules
Learned manually from training examples or
Standard machine learning

Reputation analysis
Rule Analysis (Characteristic-Word Extraction)
Training
Resemble decision tree generation
Use stochastic complexity as a criterion
Text classification rules & association rules
Ordered sequences of IF-THEN-ELSE rules
Extract keywords indicative of a specified category
Stochastic complexity formula
Score(w) represents information gain

Rule Analysis (cont.)

Co-occurrence analysis
Extract a list of words that co-occur with characteristic words

Typical sentence analysis & Correspondence analysis
Typical sentence analysis
Give user a simple overview of tendencies
Scores are calculated on the basis of the naïve Bayesian theory (posterior probability )
Create a two-dimensional position map.
An extension of principal component analysis (PCA)

Experiments – Cellular Phone

Experiments - PDAs

Experiments – Internet Service Providers

Concluding remarks
Purpose a framework for mining product reputation on the web.
Four fundamental tasks:
Characteristic word extraction
Co-occurring word extraction
Typical sentence extraction
The key to combining two parts is opinion labeling
This framework could applied to mining reputation far beyond industrial products. i.e., events, services, companies, governments, etc.

Mining Product Reputations On the Web

by feiwin on Jun 14, 2007

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Mining Product Reputations On the Web — Presentation Transcript