Title: Analysing online reviews of restaurants in Malaysia: a novel approach to descriptive and predictive analytic
Authors: Kok Wei Khong; Shasha Teng; Mohammad Mohsin Butt; Babajide AbuBakr Muritala
Addresses: Faculty of Business and Law, Taylor's University, Lakeside Campus, 1 Jalan Taylor's, 47500 Subang Jaya, Malaysia ' Faculty of Business and Law, Taylor's University, Lakeside Campus, 1 Jalan Taylor's, 47500 Subang Jaya, Malaysia ' School of Business Studies, Institute of Business Administration, University Rd, Karachi City, Sindh 75270, Pakistan ' Departament de Gestió d'Empreses, Facultat d'Economia i Empresa, Universitat Rovira i Virgili, Spain
Abstract: This paper aims to develop a model of restaurant products and services quality based on consumer sentiments shared on social networks. We applied term frequency-inverse document frequency (TF-IDF) weighted algorithm to generate empirical entities. These entities were incorporated into hypothetically defined constructs which reflect their thematic and sentimental nature, to test our predictive model using variance-based structural equation modelling. The results suggest that consumers have a positive attitude toward Malaysian restaurants regarding price, hospitality, location, waiting time, food variety, and restaurant atmosphere. Restaurant managers are advised to prioritise their restaurant attributes and manage key attributes to sustain and attract customers. By understanding the relative importance of restaurant reviews, restaurant managers are able to create and maintain competitive advantages in the restaurant industry, ultimately achieving customer loyalty and positive brand image.
Keywords: online reviews; text mining; unstructured data; clustering; Bayesian structural equation modelling; SEM.
International Journal of Electronic Business, 2021 Vol.16 No.4, pp.315 - 335
Received: 16 Mar 2020
Accepted: 06 Feb 2021
Published online: 15 Sep 2021 *