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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
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    References listed on IDEAS

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    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.
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    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.

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