 OBIM: A computational model to estimate brand image from online consumer reviewSatanik Mitra and Mamata Jenamani Journal of Business Research, 2020, vol. 114, issue C, 213-226 Abstract: Brand image is comprehended in consumers’ mind through favourability, strength, and uniqueness of brand associations. In this paper, a model is proposed to quantify Online Brand IMage (OBIM) from consumer reviews. We consider the product aspects as a brand association. Natural language processing techniques are used to extract those associations. Favourability, strength, and uniqueness of the extracted associations are computed using sentiment and co-word network analysis. Finally, the multiplicative sum of these values considers as the OBIM score. It can be used as a measure of consumer perception, which apprehends the relation between the association and their changes over time. The proposed model is demonstrated using a dataset of five mobile phones crawled from Amazon. Two applications of OBIM score, Association Based SWOT analysis and Senti-Concept Mapper technique to discover hidden concepts, are proposed. It shows how these techniques can support the decision-making process of marketers. Keywords: Brand image; Online consumer review; Aspect-based sentiment analysis; Co-Word network analysis; SWOT; Senti-Concept Mapper (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:114:y:2020:i:c:p:213-226 DOI: 10.1016/j.jbusres.2020.04.003 Access Statistics for this article Journal of Business Research is currently edited by A. G. Woodside More articles in Journal of Business Research from Elsevier |
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