IDEAS home Printed from https://ideas.repec.org/a/eee/infome/v10y2016i3p762-775.html
   My bibliography  Save this article

Finding a representative subset from large-scale documents

Author

Listed:
  • Zhang, Jin
  • Liu, Guannan
  • Ren, Ming

Abstract

Large-scale information, especially in the form of documents, is potentially useful for decision-making but intensifies the information overload problem. To cope with this problem, this paper proposes a method named RepExtract to extract a representative subset from large-scale documents. The extracted representative subset possesses three desirable features: high coverage of the content of the original document set, low redundancy within the extracted subset, and consistent distribution with the original set. Extensive experiments were conducted on benchmark datasets, demonstrating the superiority of RepExtract over the benchmark methods in terms of the three features above. A user study was also conducted by collecting human evaluations of different methods, and the results indicate that users can gain an understanding of large-scale documents precisely and efficiently through a representative subset extracted by the proposed method.

Suggested Citation

  • Zhang, Jin & Liu, Guannan & Ren, Ming, 2016. "Finding a representative subset from large-scale documents," Journal of Informetrics, Elsevier, vol. 10(3), pages 762-775.
    • Handle: RePEc:eee:infome:v:10:y:2016:i:3:p:762-775
    DOI: 10.1016/j.joi.2016.05.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1751157716300566
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.joi.2016.05.003?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2011. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Management Science, INFORMS, vol. 57(8), pages 1485-1509, August.
    2. Pan, Yue & Zhang, Jason Q., 2011. "Born Unequal: A Study of the Helpfulness of User-Generated Product Reviews," Journal of Retailing, Elsevier, vol. 87(4), pages 598-612.
    3. Chen, Dar-Zen & Huang, Mu-Hsuan & Hsieh, Hui-Chen & Lin, Chang-Pin, 2011. "Identifying missing relevant patent citation links by using bibliographic coupling in LED illuminating technology," Journal of Informetrics, Elsevier, vol. 5(3), pages 400-412.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dominik Gutt & Jürgen Neumann & Steffen Zimmermann & Dennis Kundisch & Jianqing Chen, 2018. "Design of Review Systems - A Strategic Instrument to shape Online Review Behavior and Economic Outcomes," Working Papers Dissertations 42, Paderborn University, Faculty of Business Administration and Economics.
    2. Guha Majumder, Madhumita & Dutta Gupta, Sangita & Paul, Justin, 2022. "Perceived usefulness of online customer reviews: A review mining approach using machine learning & exploratory data analysis," Journal of Business Research, Elsevier, vol. 150(C), pages 147-164.
    3. Bin Guo & Shasha Zhou, 2017. "What makes population perception of review helpfulness: an information processing perspective," Electronic Commerce Research, Springer, vol. 17(4), pages 585-608, December.
    4. Zhuolan Bao & Wenwen Li & Pengzhen Yin & Michael Chau, 2021. "Examining the impact of review tag function on product evaluation and information perception of popular products," Information Systems and e-Business Management, Springer, vol. 19(2), pages 517-539, June.
    5. Sangjae Lee & Joon Yeon Choeh, 2020. "Using the Social Influence of Electronic Word-of-Mouth for Predicting Product Sales: The Moderating Effect of Review or Reviewer Helpfulness and Product Type," Sustainability, MDPI, vol. 12(19), pages 1-18, September.
    6. Juan Feng & Xin Li & Xiaoquan (Michael) Zhang, 2019. "Online Product Reviews-Triggered Dynamic Pricing: Theory and Evidence," Information Systems Research, INFORMS, vol. 30(4), pages 1107-1123, December.
    7. King, Robert Allen & Racherla, Pradeep & Bush, Victoria D., 2014. "What We Know and Don't Know About Online Word-of-Mouth: A Review and Synthesis of the Literature," Journal of Interactive Marketing, Elsevier, vol. 28(3), pages 167-183.
    8. Warut Khern-am-nuai & Karthik Kannan & Hossein Ghasemkhani, 2018. "Extrinsic versus Intrinsic Rewards for Contributing Reviews in an Online Platform," Information Systems Research, INFORMS, vol. 29(4), pages 871-892, December.
    9. Li, Yiming & Li, Gang & Tayi, Giri Kumar & Cheng, T.C.E., 2019. "Omni-channel retailing: Do offline retailers benefit from online reviews?," International Journal of Production Economics, Elsevier, vol. 218(C), pages 43-61.
    10. Srikanth Parameswaran & Pubali Mukherjee & Rohit Valecha, 2023. "I Like My Anonymity: An Empirical Investigation of the Effect of Multidimensional Review Text and Role Anonymity on Helpfulness of Employer Reviews," Information Systems Frontiers, Springer, vol. 25(2), pages 853-870, April.
    11. Lutz, Bernhard & Pröllochs, Nicolas & Neumann, Dirk, 2022. "Are longer reviews always more helpful? Disentangling the interplay between review length and line of argumentation," Journal of Business Research, Elsevier, vol. 144(C), pages 888-901.
    12. Lorenz Graf-Vlachy & Tarun Goyal & Yannick Ouardi & Andreas König, 2021. "Reviews Left and Right: The Link Between Reviewers’ Political Ideology and Online Review Language," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(4), pages 403-417, August.
    13. Konstantin Bauman & Alexander Tuzhilin, 2022. "Know Thy Context: Parsing Contextual Information from User Reviews for Recommendation Purposes," Information Systems Research, INFORMS, vol. 33(1), pages 179-202, March.
    14. Guan-Can Yang & Gang Li & Chun-Ya Li & Yun-Hua Zhao & Jing Zhang & Tong Liu & Dar-Zen Chen & Mu-Hsuan Huang, 2015. "Using the comprehensive patent citation network (CPC) to evaluate patent value," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1319-1346, December.
    15. Rosillo-Díaz, Elena & Muñoz-Rosas, Juan Francisco & Blanco-Encomienda, Francisco Javier, 2024. "Impact of heuristic–systematic cues on the purchase intention of the electronic commerce consumer through the perception of product quality," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).
    16. Joe Cox & Daniel Kaimann, 2013. "The Signaling Effect of Critics - Evidence from a Market for Experience Goods," Working Papers CIE 68, Paderborn University, CIE Center for International Economics.
    17. Baxendale, Shane & Macdonald, Emma K. & Wilson, Hugh N., 2015. "The Impact of Different Touchpoints on Brand Consideration," Journal of Retailing, Elsevier, vol. 91(2), pages 235-253.
    18. Chung-Yi Lin & Shu-Yi Liaw & Chao-Chun Chen & Mao-Yuan Pai & Yuh-Min Chen, 2017. "A computer-based approach for analyzing consumer demands in electronic word-of-mouth," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(3), pages 225-242, August.
    19. Hanyang Luo & Sijia Cheng & Wanhua Zhou & Sumin Yu & Xudong Lin, 2021. "A Study on the Impact of Linguistic Persuasive Styles on the Sales Volume of Live Streaming Products in Social E-Commerce Environment," Mathematics, MDPI, vol. 9(13), pages 1-21, July.
    20. Wu, Xingli & Liao, Huchang, 2021. "Modeling personalized cognition of customers in online shopping," Omega, Elsevier, vol. 104(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:infome:v:10:y:2016:i:3:p:762-775. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/joi .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.