Mining Product Reputations on the Web SIGKDD 02 Edmonton, Alberta, Canada Copyright 2002 ACM Satoshi Morinaga, Kenji Yamanishi NEC Corporation Kenji Tateishi, Toshikazu Fukushima NEC Corporation

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

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

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.

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

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

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 (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

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 ) Correspondence analysis Create a two-dimensional position map. An extension of principal component analysis (PCA)

Typical sentence analysis

Give user a simple overview of tendencies

Scores are calculated on the basis of the naïve Bayesian theory (posterior probability )

Correspondence analysis

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 Correspondence analysis 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.

Purpose a framework for mining product reputation on the web.

Four fundamental tasks:

Characteristic word extraction

Co-occurring word extraction

Typical sentence extraction

Correspondence analysis

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.