Title: NLP technologies for analysing user generated Twitter data to identify the reputation of universities in the Valencian Community, Spain

Authors: Paula Núñez Milán; Manuel Palomar Sanz; Yoan Gutiérrez Vázquez

Addresses: Department of Software and Computing Systems, University of Alicante, Alicante, Spain ' Department of Software and Computing Systems, University of Alicante, Alicante, Spain ' Department of Software and Computing Systems, University of Alicante, Alicante, Spain

Abstract: The measurement of a university's reputation is currently based on rankings published nationally and internationally. These rankings are based on different criteria directly related to research and teaching, thereby creating a reputation. However, the proliferation of digital media has made it is possible to know the real opinion of stakeholders on a range of issues, including brand perception. Therefore, analysing datasets from social networks, blogs and the comment sections of websites has become an increasingly worthwhile task to getting a thorough understanding of an organisation's reputation. One way to approach this task is to use natural language processing (NLP) technologies as part of the process of conceiving the social reputation of institutional brands by interpreting a large amount of comments from social users. NLP technologies are useful for identifying and quantifying positive, neutral, negative and other information posted on social networks, i.e., Twitter. Through this research, we intend to monitor the situation of universities in the Valencian Community in terms of audience opinions, based on a justified selection of human language technologies that provide the necessary data from tweets, to create different classifications such as reputation, audience, among others.

Keywords: universities; natural language processing; NLP; technologies; social media; reputation; sentiment analysis; opinion mining; audience; public opinion; human language technologies; Spain.

DOI: 10.1504/IJEMR.2022.121829

International Journal of Electronic Marketing and Retailing, 2022 Vol.13 No.2, pp.242 - 258

Received: 31 Jul 2020
Accepted: 02 Jan 2021
Published online: 07 Apr 2022 *

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