IDEAS home Printed from https://ideas.repec.org/a/spr/infosf/v15y2013i3d10.1007_s10796-012-9400-y.html
   My bibliography  Save this article

Storing and analysing voice of the market data in the corporate data warehouse

Author

Listed:
  • Lisette García-Moya

    (Universitat Jaume I)

  • Shahad Kudama

    (Universitat Jaume I)

  • María José Aramburu

    (Universitat Jaume I)

  • Rafael Berlanga

    (Universitat Jaume I)

Abstract

Web opinion feeds have become one of the most popular information sources users consult before buying products or contracting services. Negative opinions about a product can have a high impact in its sales figures. As a consequence, companies are more and more concerned about how to integrate opinion data in their business intelligence models so that they can predict sales figures or define new strategic goals. After analysing the requirements of this new application, this paper proposes a multidimensional data model to integrate sentiment data extracted from opinion posts in a traditional corporate data warehouse. Then, a new sentiment data extraction method that applies semantic annotation as a means to facilitate the integration of both types of data is presented. In this method, Wikipedia is used as the main knowledge resource, together with some well-known lexicons of opinion words and other corporate data and metadata stores describing the company products like, for example, technical specifications and user manuals. The resulting information system allows users to perform new analysis tasks by using the traditional OLAP-based data warehouse operators. We have developed a case study over a set of real opinions about digital devices which are offered by a wholesale dealer. Over this case study, the quality of the extracted sentiment data is evaluated, and some query examples that illustrate the potential uses of the integrated model are provided.

Suggested Citation

  • Lisette García-Moya & Shahad Kudama & María José Aramburu & Rafael Berlanga, 2013. "Storing and analysing voice of the market data in the corporate data warehouse," Information Systems Frontiers, Springer, vol. 15(3), pages 331-349, July.
    • Handle: RePEc:spr:infosf:v:15:y:2013:i:3:d:10.1007_s10796-012-9400-y
    DOI: 10.1007/s10796-012-9400-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10796-012-9400-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10796-012-9400-y?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, 2007. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Working Papers 07-36, NET Institute.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Youngseok Choi & Habin Lee, 2017. "Data properties and the performance of sentiment classification for electronic commerce applications," Information Systems Frontiers, Springer, vol. 19(5), pages 993-1012, October.
    2. Malu Castellanos & Florian Daniel & Irene Garrigós & Jose-Norberto Mazón, 2013. "Business Intelligence and the Web," Information Systems Frontiers, Springer, vol. 15(3), pages 307-309, July.
    3. Claudia Diamantini & Paolo Lo Giudice & Domenico Potena & Emanuele Storti & Domenico Ursino, 2021. "An Approach to Extracting Topic-guided Views from the Sources of a Data Lake," Information Systems Frontiers, Springer, vol. 23(1), pages 243-262, February.
    4. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2017. "A multidisciplinary perspective of big data in management research," International Journal of Production Economics, Elsevier, vol. 191(C), pages 97-112.
    5. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2019. "Technology in the 21st century: New challenges and opportunities," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 321-335.
    6. Youngseok Choi & Habin Lee, 0. "Data properties and the performance of sentiment classification for electronic commerce applications," Information Systems Frontiers, Springer, vol. 0, pages 1-20.
    7. Claudia Diamantini & Paolo Lo Giudice & Domenico Potena & Emanuele Storti & Domenico Ursino, 0. "An Approach to Extracting Topic-guided Views from the Sources of a Data Lake," Information Systems Frontiers, Springer, vol. 0, pages 1-20.

    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. Michelle Haynes & Steve Thompson, 2012. "The Economic Significance of User-Generated Feedback," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 19(1), pages 153-166, February.
    2. 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.
    3. Yi-Fen Chen & Shi-Han Chang, 2016. "The online framing effect: the moderating role of warning, brand familiarity, and product type," Electronic Commerce Research, Springer, vol. 16(3), pages 355-374, September.
    4. Woo-Jin Jung & Seungjun Yang & Hee-Woong Kim, 2020. "Design of sweepstakes-based social media marketing for online customer engagement," Electronic Commerce Research, Springer, vol. 20(1), pages 119-146, March.
    5. Weijia (Daisy) Dai & Ginger Jin & Jungmin Lee & Michael Luca, 2018. "Aggregation of consumer ratings: an application to Yelp.com," Quantitative Marketing and Economics (QME), Springer, vol. 16(3), pages 289-339, September.
    6. Erick Kauffmann & Jesús Peral & David Gil & Antonio Ferrández & Ricardo Sellers & Higinio Mora, 2019. "Managing Marketing Decision-Making with Sentiment Analysis: An Evaluation of the Main Product Features Using Text Data Mining," Sustainability, MDPI, vol. 11(15), pages 1-19, August.
    7. Octavian Carare, 2012. "The Impact Of Bestseller Rank On Demand: Evidence From The App Market," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(3), pages 717-742, August.
    8. Yili Hong & Pei-yu Chen & Lorin Hitt, 2014. "Measuring Product Type with Dynamics of Online Product Review Variances: A Theoretical Model and the Empirical Applications," Working Papers 14-03, NET Institute.
    9. Philipp Herrmann, 2014. "The impact of the variance of online consumer ratings on pricing and demand – An analytical model," Working Papers Dissertations 07, Paderborn University, Faculty of Business Administration and Economics.
    10. Yue Ma & Guoqing Chen & Qiang Wei, 2017. "Finding users preferences from large-scale online reviews for personalized recommendation," Electronic Commerce Research, Springer, vol. 17(1), pages 3-29, March.
    11. Kun Chen & Peng Luo & Huaiqing Wang, 2017. "Investigating transitive influences on WOM: from the product network perspective," Electronic Commerce Research, Springer, vol. 17(1), pages 149-167, March.
    12. Daniel Kaimann & Joe Cox, 2014. "The Interaction of Signals: A Fuzzy set Analysis of the Video Game Industry," Working Papers Dissertations 13, Paderborn University, Faculty of Business Administration and Economics.
    13. Anindya Ghose & Sha Yang, 2009. "An Empirical Analysis of Search Engine Advertising: Sponsored Search in Electronic Markets," Management Science, INFORMS, vol. 55(10), pages 1605-1622, October.
    14. 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.
    15. 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.
    16. Yabing Jiang & Hong Guo, 2012. "Design of Consumer Review Systems and Product Pricing," Working Papers 12-10, NET Institute.
    17. Fernando Ferri & Alessia D'Andrea & Patrizia Grifoni, 2017. "An Integrated Methodology for Approaching Sentiment Analysis in Business Domain," International Business Research, Canadian Center of Science and Education, vol. 10(9), pages 1-16, September.
    18. Oded Netzer & Ronen Feldman & Jacob Goldenberg & Moshe Fresko, 2012. "Mine Your Own Business: Market-Structure Surveillance Through Text Mining," Marketing Science, INFORMS, vol. 31(3), pages 521-543, May.
    19. Akshay Kangale & S. Krishna Kumar & Mohd Arshad Naeem & Mark Williams & M. K. Tiwari, 2016. "Mining consumer reviews to generate ratings of different product attributes while producing feature-based review-summary," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(13), pages 3272-3286, October.
    20. Decker, Reinhold & Trusov, Michael, 2010. "Estimating aggregate consumer preferences from online product reviews," International Journal of Research in Marketing, Elsevier, vol. 27(4), pages 293-307.

    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:spr:infosf:v:15:y:2013:i:3:d:10.1007_s10796-012-9400-y. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.