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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
