Alzate Barricarte, Miriam

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https://academica-e.unavarra.es/handle/2454/48517

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Alzate Barricarte

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Miriam

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Gestión de Empresas

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INARBE. Institute for Advanced Research in Business and Economics

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0000-0003-4414-3030

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750432

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811200

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Subject: Text mining ×

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Now showing 1 - 2 of 2
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    PublicationOpen Access
    Mining the text of online consumer reviews to analyze brand image and brand positioning
    (Elsevier, 2022) Alzate Barricarte, Miriam; Arce Urriza, Marta; Cebollada Calvo, Javier; Gestión de Empresas; Enpresen Kudeaketa
    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.
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    PublicationOpen Access
    What are consumers saying online about your products?: mining the text of online reviews to uncover hidden features
    (Henry Stewart Publications, 2021) Alzate Barricarte, Miriam; Arce Urriza, Marta; Cebollada Calvo, Javier; Gestión de Empresas; Enpresen Kudeaketa
    Thanks to the growth of the internet and the increasing use of social networks, companies can now access huge volumes of online texts in order to understand consumers¿ preferences and needs. This article illustrates some methods to extrapolate information from such texts. The text-mining analysis covers such issues as word frequency, sentiment analysis, paired words, similarities in textual content and the main topics discussed in online reviews. From a practical point of view, brand managers can use the proposed methods to gain consumer insights into products and brands, to be able to improve and adapt their marketing strategies.