Mining the Text of Online Consumer Reviews to Analyze Brand Image, Brand Positioning and Market Structure
(A2022-107087)

AUTHORS

Miriam Alzate, Universidad Publica de Navarra; Javier Cebollada, Public University of Navarre; Marta Arce, Universidad Publica de Navarra

ABSTRACT

The growth of the Internet has led to a huge availability of online consumer reviews. This research proposes a procedure to explore the textual content of online reviews to study brand image, brand positioning and market structure. The text mining analysis in this research is based on a lexicon-based approach, the Linguistic Inquiry and Word Count (Pennebaker et al., 2007), which allows the researcher to get insights into emotional and psychological brand associations, which are used as inputs to analyze brand positioning and market structure. The analysis of brand positioning is conducted using Principal Component Analysis, while market structure is analyzed through hierarchical clustering. The research data is based on 62,496 online reviews of 131 products and 44 brands, which are all the products and brands available at the category of “blush” cosmetics at the online retailer studied.