Mining the text of online consumer reviews to analyze brand image and brand positioning
Fecha
2022Versión
Acceso abierto / Sarbide irekia
Tipo
Artículo / Artikulua
Versión
Versión publicada / Argitaratu den bertsioa
Identificador del proyecto
Impacto
|
10.1016/j.jretconser.2022.102989
Resumen
The growth of the Internet has led to massive availability of online consumer reviews. So far, papers studying online reviews have mainly analysed how non-textual features, such as ratings and volume, influence different types of consumer behavior, such as information adoption decisions or product choices. However, little attention has been paid to examining the textual aspects of online reviews ...
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The growth of the Internet has led to massive availability of online consumer reviews. So far, papers studying online reviews have mainly analysed how non-textual features, such as ratings and volume, influence different types of consumer behavior, such as information adoption decisions or product choices. However, little attention has been paid to examining the textual aspects of online reviews in order to study brand image and brand positioning. The text analysis of online reviews inevitably raises the concept of 'text mining'; that is, the process of extracting useful and meaningful information from unstructured text. This research proposes an unified, structured and easy-to-implement procedure for the text analysis of online reviews with the ultimate goal of studying brand image and brand positioning. The text mining analysis is based on a lexicon-based approach, the Linguistic Inquiry and Word Count (Pennebaker et al., 2007), which provides the researcher with insights into emotional and psychological brand associations. [--]
Materias
Brand positioning,
eWOM,
Online reviews,
Text mining
Editor
Elsevier
Publicado en
Journal of Reatlaing Consumer Services, 2022, Vol. 67
Departamento
Universidad Pública de Navarra. Departamento de Gestión de Empresas /
Nafarroako Unibertsitate Publikoa. Enpresen Kudeaketa Saila
Versión del editor
Entidades Financiadoras
This work was supported by the Spanish Ministry of Economy, Industry and Competitivity [grant number: ECO2015-65393-R] and by the Government of Spain Ministry of Science, Innovation and Universities Grant numbers: PID2019-108554RB-I00.