 Emotional Text Mining: Customer profiling in brand managementFrancesca Greco and Alessandro Polli International Journal of Information Management, 2020, vol. 51, issue C 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. Keywords: Emotional Text Mining; Brand management; Twitter; Network analysis; Customer profiling (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ininma:v:51:y:2020:i:c:s0268401218313598 DOI: 10.1016/j.ijinfomgt.2019.04.007 Access Statistics for this article International Journal of Information Management is currently edited by Yogesh K. Dwivedi More articles in International Journal of Information Management from Elsevier |
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