IDEAS home Printed from https://ideas.repec.org/a/eee/ininma/v51y2020ics0268401218313598.html
   My bibliography  Save this article

Emotional Text Mining: Customer profiling in brand management

Author

Listed:
  • Greco, Francesca
  • Polli, Alessandro

Abstract

The widespread use of the Internet and the constant increase in users of social media platforms has made a large amount of textual data available. This represents a valuable source of information about the changes in people’s opinions and feelings. This paper presents the application of Emotional Text Mining (ETM) in the field of brand management. ETM is an unsupervised procedure aiming to profile social media users. It is based on a bottom-up approach to classify unstructured data for the identification of social media users’ representations and sentiments about a topic. It is a fast and simple procedure to extract meaningful information from a large collection of texts. As customer profiling is relevant for brand management, we illustrate a business application of ETM on Twitter messages concerning a well-known sportswear brand in order to show the potential of this procedure, highlighting the characteristics of Twitter user communities in terms of product preferences, representations, and sentiments.

Suggested Citation

  • Greco, Francesca & Polli, Alessandro, 2020. "Emotional Text Mining: Customer profiling in brand management," International Journal of Information Management, Elsevier, vol. 51(C).
  • Handle: RePEc:eee:ininma:v:51:y:2020:i:c:s0268401218313598
    DOI: 10.1016/j.ijinfomgt.2019.04.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0268401218313598
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ijinfomgt.2019.04.007?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.

    Citations

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


    Cited by:

    1. Setten, Eric & Chen, Steven, 2024. "Playing with emotions: Text analysis of emotional tones in gender-casted Children’s media," Journal of Business Research, Elsevier, vol. 175(C).
    2. Fronzetti Colladon, Andrea & Toschi, Laura & Ughetto, Elisa & Greco, Francesca, 2023. "The language and social behavior of innovators," Journal of Business Research, Elsevier, vol. 154(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:ininma:v:51:y:2020:i:c:s0268401218313598. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: https://www.journals.elsevier.com/international-journal-of-information-management .

    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.