Mindset metrics, the measurement of consumers' perceptions, attitudes, and intentions, have a long tradition in marketing, particularly in advertising and branding. Some of the most usual mindset metrics are brand awareness, brand image, personality traits, and attribute importance. Brand awareness and other mindset measures have the form of texts (bag of words). And, a natural methodology for analyzing these variables is topic modeling and the popular Latent Dirichlet allocation (LDA) model. The LDA methodology assumes that brands or concepts are represented by clusters of brands in consumers' minds. This study proposes an extension/modification of the LDA model for brand awareness and other mindset variables that incorporate Bernoulli observations instead of the Multinomial specification present in the usual LDA specification. This extension is relevant since, unlike words in texts, brands and mindset concepts are not repeated within a document and have a dichotomous form, present or absent. The proposed model is applied to two brand awareness datasets. The results show significant gains in both managerial insights in analyzing brand clusters and consumers' profiles.