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Improving product review search experiences on general search engines

Published: 12 August 2009 Publication History

Abstract

In the Web 2.0 era, internet users contribute a large amount of online content. Product review is a good example. Since these phenomena are distributed all over shopping sites, weblogs, forums etc., most people have to rely on general search engines to discover and digest others' comments. While conventional search engines work well in many situations, it's not sufficient for users to gather such information. The reasons include but are not limited to: 1) the ranking strategy does not incorporate product reviews' inherent characteristics, e.g., sentiment orientation; 2) the snippets are neither indicative nor descriptive of user opinions. In this paper, we propose a feasible solution to enhance the experience of product review search. Based on this approach, a system named "Improved Product Review Search (IPRS)" is implemented on the ground of a general search engine. Given a query on a product, our system is capable of: 1) automatically identifying user opinion segments in a whole article; 2) ranking opinions by incorporating both the sentiment orientation and the topics expressed in reviews; 3) generating readable review snippets to indicate user sentiment orientations; 4) easily comparing products based on a visualization of opinions. Both results of a usability study and an automatic evaluation show that our system is able to assist users quickly understand the product reviews within limited time.

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Cited By

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  • (2018)Review on Recent Advances in Information Mining From Big Consumer Opinion Data for Product DesignJournal of Computing and Information Science in Engineering10.1115/1.404108719:1(010801)Online publication date: 17-Sep-2018
  • (2014)Extraction of Usefulness Factors of Reviews by Factor AnalysisLecture Notes on Software Engineering10.7763/LNSE.2014.V2.112(144-149)Online publication date: 2014
  • (2012)ETFProceedings of the fifth ACM international conference on Web search and data mining10.1145/2124295.2124316(163-172)Online publication date: 8-Feb-2012
  • Show More Cited By

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Published In

cover image ACM Other conferences
ICEC '09: Proceedings of the 11th International Conference on Electronic Commerce
August 2009
407 pages
ISBN:9781605585864
DOI:10.1145/1593254
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

  • School of Business, The University of Hong Kong, Hong Kong
  • Sayling Wen Cultural & Educational Foundation
  • Ministry of Education, Taiwan
  • College of Information Science and Technology, Drexel University, USA
  • Weatherhead School of Management, Case Western Reserve University, USA
  • College of Technology Management, National Tsing Hua University, Taiwan
  • National Science Council, Taiwan
  • Chinese Enterprise Resource Planning Society, Taiwan
  • International Center for Electronic Commerce, Korea Advanced Institute of Science & Technology, Korea

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 August 2009

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Author Tags

  1. affinity rank
  2. opinion mining
  3. review search
  4. sentiment classification

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  • Research-article

Conference

ICEC '09
Sponsor:
ICEC '09: International Conference on E-Commerce
August 12 - 15, 2009
Taipei, Taiwan

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Overall Acceptance Rate 150 of 244 submissions, 61%

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Cited By

View all
  • (2018)Review on Recent Advances in Information Mining From Big Consumer Opinion Data for Product DesignJournal of Computing and Information Science in Engineering10.1115/1.404108719:1(010801)Online publication date: 17-Sep-2018
  • (2014)Extraction of Usefulness Factors of Reviews by Factor AnalysisLecture Notes on Software Engineering10.7763/LNSE.2014.V2.112(144-149)Online publication date: 2014
  • (2012)ETFProceedings of the fifth ACM international conference on Web search and data mining10.1145/2124295.2124316(163-172)Online publication date: 8-Feb-2012
  • (2010)BibliographyAn Introduction to Search Engines and Web Navigation10.1002/9780470874233.biblio(424-461)Online publication date: 4-Aug-2010

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