1 / 12

Mining comparative opinions from customer reviews for Competitive Intelligence

Mining comparative opinions from customer reviews for Competitive Intelligence. Presenter: Tsai Tzung Ruei Authors: Kaiquan Xu , Stephen Shaoyi Liao, Jiexun Li, Yuxia Song . 國立雲林科技大學 National Yunlin University of Science and Technology. DSS 2010. Outline. Motivation Objective

lawson
Télécharger la présentation

Mining comparative opinions from customer reviews for Competitive Intelligence

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Mining comparative opinions from customer reviews for Competitive Intelligence Presenter: Tsai TzungRuei Authors: KaiquanXu, Stephen Shaoyi Liao, Jiexun Li, Yuxia Song 國立雲林科技大學 National Yunlin University of Science and Technology DSS 2010

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

  3. Motivation • In the past, studies mainly focussed on identifying customers' sentiment polarities toward products. The most important problem in CI—i.e., collecting and analyzing the competitors' information to identify potential risks as early as possible and plan appropriate strategies—has not been well studied. • Users usually prefer to compare several competitive products with similar functions.

  4. Objective • To propose a novel approach to extracting product comparative relations from customer reviews, and display the results as comparative relation maps for decision support in enterprise risk management. V.S. iPhone beats the curve in both function and looks.

  5. Methodology R (P1; P2; A; S) Product Sentiment Attribute Sentiment Attribute Product The iPhone has better looks, but a much higher price than the BBCurve “BlackBerry 8320” is written as “BB 8320” and “8320”.

  6. Experiments(1/4)

  7. Experiments(2/4)

  8. Experiments(3/4)

  9. Experiments(4/4)

  10. Conclusion(1/2) • MAJOR CONTRIBUTION • The proposed graphical model can achieve better performance for relation extraction by modeling the unfixed interdependencies among relations, which is not covered by the existing methods. • To the best of our knowledge, this is the first work on using comparison opinion as information sources in CI for enterprise risk management. • The empirical evaluation shows that the performance of the comparative relation extraction is quite promising, and it implies the feasibility of mining the comparison opinions for CI. • FUTURE WORK • To conduct an empirical evaluation of the proposed model on a larger scale with other product types.

  11. Conclusion(1/2) • FUTURE WORK • To extend the model to jointly recognize the comparative relations and entities so as to reduce the errors accumulated in the pipeline process.

  12. Comments • Advantage • This paper describes very clearly. • Application • CRM

More Related