Please use this identifier to cite or link to this item: https://ruomo.lib.uom.gr/handle/7000/279
Title: Applying Brand Equity Theory to Understand the Opinion of Consumers in Social Media
Authors: Kalampokis, Evangelos
Karamanou, Areti
Tambouris, Efthimios
Tarabanis, Konstantinos
Type: Article
Subjects: FRASCATI::Natural sciences::Computer and information sciences
FRASCATI::Social sciences::Media and communications
Issue Date: 2016
Source: Journal of Universal Computer Science
Volume: 22
Issue: 5
First Page: 709
Last Page: 734
Abstract: Billions of people everyday use Social Media (SM), such as Facebook and Twitter, to express their opinions and experiences with brands. Companies are highly interested in understanding such SM brand-related content. Consequently, many studies have been conducted and many applications have been developed to analyse this content. For analysis purposes, the main SM metrics used include volume and sentiment. Interestingly, however, brand equity theory proposes different metrics for assessing brand reputation. These include brand image, brand satisfaction and purchase intention (henceforth referred to as marketing metrics). The objective of this paper is to explore the feasibility of applying marketing metrics in Twitter brand-related content. For this purpose, we collect, study and analyse tweets that mention two brands, namely IKEA and Gatorade. The manual analysis suggests that a significant amount of brand tweets is related to brand image, brand satisfaction and purchase intention. We thereafter design an algorithm that classifies tweets into relevant categories to enable automatic marketing metrics computation. We implement the algorithm using statistical learning approaches and prove that its classification accuracy is good. We anticipate that this article will motivate other studies as well as applications’ designers in adopting marketing theories when evaluating brand reputation through SM content.
URI: https://doi.org/10.3217/jucs-022-05-0709
https://ruomo.lib.uom.gr/handle/7000/279
Other Identifiers: 10.3217/jucs-022-05-0709
Appears in Collections:Department of Applied Informatics
Department of Business Administration

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