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.

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Presentation transcript:

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 1

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Outline Motivation Objective Methodology Experiments Conclusion Comments 2

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. 3

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Objective 4 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 5

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methods – Definition of entity 6

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methods – Tag 7

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methods – Tag (Con.) 8 Example:“I love the ease of transferring the pictures to my computer. I love the ease of transferring the pictures to my com puter

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methods – Maximum Likelihood Estimation(MLE) 9

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methods – Information Propagation 10

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methods – Bootstrapping 11

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments 12

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments 13

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Conclusion 14 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 15 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