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Emotional Text Mining: Customer profiling in brand management

Author

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  • 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
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    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).

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