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

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. 國立雲林科技大學 National Yunlin University of Science and Technology. KDD 2009. Outline. Motivation Objective Methodology

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

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  1. 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 國立雲林科技大學 National Yunlin University of Science and Technology KDD 2009

  2. Outline • Motivation • Objective • Methodology • Experiments • Conclusion • Comments

  3. 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.

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

  5. Methods - Overview

  6. Methods – Definition of entity

  7. Methods – Tag

  8. Methods – Tag (Con.) 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>

  9. Methods – Maximum Likelihood Estimation(MLE)

  10. Methods – Information Propagation

  11. Methods – Bootstrapping

  12. Experiments

  13. Experiments

  14. Conclusion 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.

  15. Comments • 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

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