Volume 10 Number 1 (Jan. 2021)
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IJCCE 2021 Vol.10(1): 17-27 ISSN: 2010-3743
DOI: 10.17706/IJCCE.2021.10.1.17-27

Deep LSTM for Generating Brand Personalities Using Social Media: A Case Study from Higher Education Institutions

Piyumini Wijenayake, Damminda Alahakoon, Daswin de Silva, Saliya Kirigeeganage
Abstract—This paper introduces a novel technique to generate brand personalities for organizations from social media text data using a deep bi-directional Long Short-Term Memory (BiLSTM) neural network model in combination with an accepted brand personality scale model. Brand Personality has evolved into an indispensable element in modern business organizations. Currently brand personality of an organization is generated using traditional techniques such as stakeholder interviews, questionnaires, which are time and resource intensive and limited in efficacy. However, the rise of the internet and social media have provided a platform for stakeholders to frequently and freely express their opinions and experiences related to organizations. Such social media data while now successfully being used for customer analytics have not yet been fully investigated and used to understand brand personalities. Our research investigated how this data can be effectively leveraged by organizations to generate and monitor their brand in near real time. Our technique has been successfully demonstrated on Higher Education Institutes, as Higher Education is an industry that is increasingly being exposed to business competition over the last few decades.

Index Terms—Big data, brand personality, deep learning, neural networks, social media, word embedding.

Piyumini Wijenayake, Damminda Alahakoon, Daswin de Silva, Saliya Kirigeeganage are with Research Centre for Data Analytics and Cognition, La Trobe University, Melbourne, VIC, Australia.

Cite:Piyumini Wijenayake, Damminda Alahakoon, Daswin de Silva, Saliya Kirigeeganage, "Deep LSTM for Generating Brand Personalities Using Social Media: A Case Study from Higher Education Institutions," International Journal of Computer and Communication Engineering vol. 10, no. 1, pp. 17-27, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

ISSN: 2010-3743 (Online)
Abbreviated Title: Int. J. Comput. Commun. Eng.
Frequency: Quarterly
Editor-in-Chief: Dr. Maode Ma
Abstracting/ Indexing: INSPEC, Google Scholar, Crossref, EBSCO, ProQuest, and Electronic Journals Library
E-mail: ijcce@iap.org
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