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

ACL ID D13-1131
Title This Text Has the Scent of Starbucks: A Laplacian Structured Sparsity Model for Computational Branding Analytics
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013
Authors

We propose a Laplacian structured sparsity model to study computational branding ana- lytics. To do this, we collected customer re- views 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: auto- matic brand identification from raw text, joint brand-satisfaction prediction, and joint brand- gender-satisfaction prediction. This work ex- tends 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 quantita- tive evaluation shows that our approach ...