An Investigation of Brand-Related User-Generated Content on Twitter

Abstract

The big data of user-generated content (UGC) on social media are laden with potential value for brand managers. However, there are many obstacles to using big data to answer brand-management questions. This article presents a framework that automatically derives latent brand topics and classifies brand sentiments. It applies text mining with latent Dirichlet allocation (LDA) and sentiment analysis on 1.7 million unique tweets for 20 brands across five industries: fast food, department store, footwear, electronics, and telecommunications. The framework is used to explore four brand-related questions on Twitter. There are three main findings. First, product, service, and promotions are the dominant topics of interest when consumers interact with brands on Twitter. Second, consumer sentiments toward brands vary within and across industries. Third, separate company-specific analyses of positive and negative tweets generate a more accurate understanding of Twitter users' major brand topics and sentiments. Our findings provide brand managers with actionable insights in targeted advertising, social customer relationship management (CRM), and brand management.