This Text Has the Scent of Starbucks: A Laplacian Structured Sparsity Model for Computational Branding Analytics

William Yang Wang, Edward Lin, John Kominek

Abstract: We propose a Laplacian structured sparsity model to study computational branding analytics. To do this, we collected customer reviews from Starbucks, Dunkin’ Donuts, and other coffee shops across 38 major cities in the Midwest and Northeastern regions of USA. We study the brand related language use through these reviews, with focuses on the brand satisfaction and gender factors. In particular, we perform three tasks: automatic brand identification from raw text, joint brand-satisfaction prediction, and joint brandgender-satisfaction prediction. This work extends previous studies in text classification by incorporating the dependency and interaction among local features in the form of structured sparsity in a log-linear model. Our quantitative evaluation shows that our approach which combines the advantages of graphical modeling and sparsity modeling techniques significantly outperforms various standard and stateof-the-art text classification algorithms. In addition, qualitative analysis of our model reveals important features of the language uses associated with the specific brands.