DERIV: distributed brand perception tracking framework

Abstract

Abstract Determining user’s perception of a brand in short periods of time has become crucial for business. Distilling brand perception directly from people’s comments in social media has promise. Current techniques for determining brand perception, such as surveys of handpicked users by mail, in person, phone or online, are time consuming and increasingly inadequate. The DERIV system distills storylines from open data representing direct consumer voice into a brand perception. The framework summarizes perception of a brand in comparison to peer brands with in-memory distributed algorithms utilizing supervised machine learning techniques. Experiments performed with open data and models built with storylines of known peer brands show the technique as highly scalable and accurate in capturing brand perception from vast amounts of social data compared to sentiment analysis.

Keywords

• Big data
• Distributed learning
• Brand perception
• In-memory distribution