OpinionMiner: A Novel Machine Learning System for Web Opinion Mining and Extraction

Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.

OpinionMiner: A Novel Machine Learning System for Web Opinion Mining and Extraction

Presenter : Jiang-Shan Wang

Authors : Wei Jin, Hung Hay Ho, Rohini K. Srihari

KDD 2009

國立雲林科技大學National Yunlin University of Science and Technology

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Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.Outline

Motivation

Objective

Methodology

Experiments

Conclusion

Comments

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Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.Motivation

Customers’ opinions and hands-on experiences on products are highly valuable to manufacturers, online advertisers and potential customers.

Unfortunately, reading through all customer reviews is difficult, especially for popular items.

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Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.Objective

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This paper aims to design a system that is capable of extracting, learning and classifying product entities and opinion expressions automatically from product reviews.

Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.Methods - Overview

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Intelligent Database Systems Lab

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I. M.Methods – Definition of entity

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Intelligent Database Systems Lab

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I. M.Methods – Tag

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Intelligent Database Systems Lab

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I. M.Methods – Tag (Con.)

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Example:“I love the ease of transferring the pictures to my computer.

<BG>I</BG><OPINION_POS_EXP>love</OPINION_POS_EXP><BG>the</BG><PROD_FEATBOE>ease</PROD_FEAT-BOE><PROD_FEATMOE>of</PROD_FEAT-MOE><PROD_FEATMOE>transferring</PROD_FEAT-MOE><PROD_FEATMOE>the</PROD_FEATMOE><PROD_FEATEOE>pictures</PROD_FEATEOE><BG>to</BG><BG>my</BG><BG>computer</BG>

<BG>I</BG><OPINION_POS_EXP>love</OPINION_POS_EXP><BG>the</BG><PROD_FEAT>ease of transferring the pictures</PROD_FEAT><BG>to</BG><BG>my</BG><BG>computer</BG>

Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.Methods – Maximum Likelihood Estimation(MLE)

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Intelligent Database Systems Lab

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I. M.Methods – Information Propagation

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Intelligent Database Systems Lab

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I. M.Methods – Bootstrapping

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Intelligent Database Systems Lab

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I. M.Experiments

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Intelligent Database Systems Lab

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I. M.Experiments

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Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.Conclusion

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The model naturally integrates multiple linguistic features into automatic learning.

The system can predict new potential product and opinion entities.

Complex expressions or infrequently entities can be effectively and efficiently identified.

The bootstrapping approach can handle a large training set.

Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.Comments

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Advantage Integrating linguistic features into opinion mining.

It is a valuable idea.

Drawback Long opinion will influence the system performance.

It can’t deal with pronoun.

Application Information Retrieval.

E-commerce