Article: Assessing brand equity through customer reviews: a naïve Bayes classifier approach Journal: International Journal of Business Information Systems (IJBIS) 2026 Vol.52 No.5 pp.21 - 42 Abstract: Customer-based brand equity has been critical to gaining a competitive advantage in the marketplace. Despite the usefulness of conventional survey-based measurement, many scholars and practitioners face difficulties in comparing brand equity across brands, sectors, and products owing to the limitations of survey-based measurement. This study proposes a machine learning method to complement traditional survey-based measures. Our approach comprises four major stages: 1) extracting seed phrases from brand equity survey questionnaires; 2) identifying response sentences from customer reviews; 3) assigning scores to brand equity; 4) replicating regression models from previous empirical studies to validate our approach. Our analysis examined 65,057 vacuum cleaner customer reviews from an online e-commerce platform, (e.g., Amazon.com) representing six major brands. This paper complements traditional studies by presenting a consistent and reliable measurement methodology. Inderscience Publishers - linking academia, business and industry through research

Title: Assessing brand equity through customer reviews: a naïve Bayes classifier approach

Authors: Jaesun Yeom; JiYu Kim; Han-Gyun Woo

Addresses: Department of Industrial Management Engineering, Hanbat National University, 125 Dongseo-daero, Daejeon, South Korea ' School of Business Administration, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Ulsan, South Korea ' Graduate School of Management of Technology, Sogang University, 35 Baekbeom-ro, Mapo-gu, Seoul, South Korea

Abstract: Customer-based brand equity has been critical to gaining a competitive advantage in the marketplace. Despite the usefulness of conventional survey-based measurement, many scholars and practitioners face difficulties in comparing brand equity across brands, sectors, and products owing to the limitations of survey-based measurement. This study proposes a machine learning method to complement traditional survey-based measures. Our approach comprises four major stages: 1) extracting seed phrases from brand equity survey questionnaires; 2) identifying response sentences from customer reviews; 3) assigning scores to brand equity; 4) replicating regression models from previous empirical studies to validate our approach. Our analysis examined 65,057 vacuum cleaner customer reviews from an online e-commerce platform, (e.g., Amazon.com) representing six major brands. This paper complements traditional studies by presenting a consistent and reliable measurement methodology.

Keywords: brand equity; machine learning; naïve Bayes classification; customer reviews.

DOI: 10.1504/IJBIS.2026.154480

International Journal of Business Information Systems, 2026 Vol.52 No.5, pp.21 - 42

Published online: 30 Jun 2026 *

Title: Assessing brand equity through customer reviews: a naïve Bayes classifier approach

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