Using big data analytics to study brand authenticity sentiments: The case of Starbucks on Twitter. (October 2019)

• Record Type: Journal Article
• Authors: Shirdastian, Hamid; Laroche, Michel; Richard, Marie-Odile
• Is Part Of: International journal of information management. Volume 48(2019)
• Page Start: 291
• Page End: 307
• Publication Date: 2019-10
• Subjects: Brand sentiment analysis -- Brand authenticity -- Social media -- Big data analytics -- Support Vector Machine (SVM) -- Latent semantic analysis (LSA); Social sciences -- Information services -- Periodicals; Social sciences -- Research -- Periodicals; Information science -- Periodicals; Management information systems -- Periodicals; Knowledge management -- Periodicals; Sciences sociales -- Documentation, Services de -- Périodiques; Sciences sociales -- Recherche -- Périodiques; Sciences de l'information -- Périodiques; Systèmes d'information de gestion -- Périodiques; Information science; Management information systems; Social sciences -- Information services; Social sciences -- Research; Periodicals; Electronic journals; 025.52068
• DOI: 10.1016/j.ijinfomgt.2017.09.007
• Languages: English
• ISSNs: 0268-4012

Abstract

Highlights: A new analytics procedure for studying branding issues in the big data era. Tweets are classified under the quality commitment, heritage, uniqueness, and symbolism categories. Latent semantic analysis (LSA) extract the common words in each category. Results from the support vector machine (SVM) illustrate the effectiveness of the proposed procedure. High accuracy for both the brand authenticity dimensions' prediction and its sentiment polarity. Abstract: There is a strong interest among academics and practitioners in studying branding issues in the big data era. In this article, we examine the sentiments toward a brand, via brand authenticity, to identify the reasons for positive or negative sentiments on social media. Moreover, in order to increase precision, we investigate sentiment polarity on a five-point scale. From a database containing 2, 282, 912 English tweets with the keyword 'Starbucks', we use a set of 2204 coded tweets both for analyzing brand authenticity and sentiment polarity. First, we examine the tweets qualitatively to gain insights about brand authenticity sentiments. Then we analyze the data quantitatively to establish a framework in which we predict both the brand authenticity dimensions and their sentiment polarity. Through three qualitative studies, we discuss several tweets from the dataset that can be classified under the quality commitment, heritage, uniqueness, and symbolism categories. Using latent semantic analysis (LSA), we extract the common words in each category. We verify the robustness of previous findings with an in-lab experiment. Results from the support vector machine (SVM), as the quantitative research method, illustrate the effectiveness of the proposed procedure of brand authenticity sentiment analysis. It shows high accuracy for both the brand authenticity dimensions' predictions and their sentiment polarity. We then discuss the theoretical and managerial implications of the studies.