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OBIM: A computational model to estimate brand image from online consumer review

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  • Mitra, Satanik
  • Jenamani, Mamata

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.

Suggested Citation

  • Mitra, Satanik & Jenamani, Mamata, 2020. "OBIM: A computational model to estimate brand image from online consumer review," Journal of Business Research, Elsevier, vol. 114(C), pages 213-226.
    • Handle: RePEc:eee:jbrese:v:114:y:2020:i:c:p:213-226
    DOI: 10.1016/j.jbusres.2020.04.003
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