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Designn and Implementation Online Customer Reviews Analysis System based on Dependency Network Model

종속성 네트워크 기반의 온라인 고객리뷰 분석시스템 설계 및 구현

  • 김근형 (제주대학교 경영정보학과)
  • Received : 2010.08.13
  • Accepted : 2010.10.26
  • Published : 2010.11.28

Abstract

It is very important to analyze online customer reviews, which are small documents of writing opinions or experiences about products or services, for both customers and companies because the customers can get good informations and the companies can establish good marketing strategies. In this paper, we did not propose only dependency network model which is tool for analyzing online customer reviews, but also designed and implemented the system based on the dependency network model. The dependency network model analyzes both subjective and objective sentences, so that it can represent relative importance and relationship between the nouns in the sentences. In the result of implementing, we recognized that relative importance and relationship between the features of products or services, which can not be mined by opinion mining, can be represented by the dependency network model.

Keywords

Online Customer Review;Dependency Network;Frequency;Relationship;Opinion Mining

Acknowledgement

Supported by : 제주대학교

References

  1. Xiaowen Ding, "A Holistic Lexicon-Based Approach to Opinion Mining," Proc. of the international conference on web search and web mining, pp.231-240, 2008.
  2. Jerzy Stefanowski and Dawid Weiss, "Comprehensible and Accurate Cluster Labels in Text Clustering," Conference RIAO 2007, Pittsburgh PA, USA, May, pp.34-45, 2007.
  3. Zamir and Etzioni, Grouper: a dynamic clustering interface to Web search results. Computer Networks, 31, pp.11-16, 1999.
  4. G. Salton, A. Singhal, C. Buckley, and M. Mitra, Automatic Text Decomposition using Text Segments and Text Themes," ACM Conference on Hypertext, pp.56-65, 1996.
  5. B. Boguraev and C. Kennedy, "Salience-Based Content Characterization of Text Documents," Proc. of the ACL'97/EACL'97 Workshop on Intelligent Scalable Text Summarization, pp.76-87, 1997.
  6. B. Liu, M. Hu, and J. Cheng, "Opinion observer: analyzing and comparing opinions on the Web," Proc. of the 14th international conference on WWW, pp.10-14, 2005.
  7. Minqing Hu and Bing Liu, "Mining and Summarizing Customer Reviews," KDD'04,pp.168-177, 2004. https://doi.org/10.1145/1014052.1014073
  8. Christopher Scaffidi, Kevin Bierhoff, Eric Chang, Mikhael Felker, Herman Ng, Chun Jin, "Red Opal: Product-Feature Scoring from Reviews," Proc. of the 8th ACM conference on Electronic commerce, pp.11-15, 2007.
  9. Xiaowen Ding and Bing Lui, "The Utility of Lingusitic Rules in Opinion Mining," SIGR pp.811-812, 2007.
  10. E. Courses and T. Surveys, "Using SentiWordNet for multilingual sentiment analysis," Data Engineering Workshop ICDEW, pp.102-110, 2008.
  11. Xiaowen Ding, Bing Liu and Philip S. Yu, "A Holistic Lexicon-Based Approach to Opinion Mining," WSDM'08, pp.231-239, 2008.
  12. http://nlp.kookmin.ac.kr/HAM/kor/download.html

Cited by

  1. Study on Designing and Implementing Online Customer Analysis System based on Relational and Multi-dimensional Model vol.12, pp.4, 2012, https://doi.org/10.5392/JKCA.2012.12.04.076
  2. Methodology for Identifying Issues of User Reviews from the Perspective of Evaluation Criteria: Focus on a Hotel Information Site vol.22, pp.3, 2016, https://doi.org/10.13088/jiis.2016.22.3.023