The Statistics Seminar Speaker for Wednesday, Nov. 2, will be Shawn Mankad, an assistant professor in the Johnson School of Management at Cornell University.
Professor Mankad's research focuses on taking a data-driven approach to solving business and policy issues usually in the areas of Information Systems, Operations, and Finance. In particular, his work has explored using text documents to predict economic variables, especially in the context of online reviews. Another major area of research is the modeling of networks to identify communities, influential agents, and to characterize evolutions of the network structure over time. His undergraduate degree is from Carnegie Mellon University and he received his PhD in Statistics from the University of Michigan.
Visit his personal webpage here.
Title: Single Stage Prediction with Online Reviews for Mobile App Development and Management
Abstract: Mobile apps are one of the building blocks of the mobile digital economy. A differentiating feature of mobile apps to traditional enterprise software is online reviews, which are available on app marketplaces and represent a valuable source of consumer feedback on the app. We create a supervised topic modeling approach for app developers to use mobile reviews as useful sources of quality and customer feedback, thereby complementing traditional software testing. The approach is based on a constrained matrix factorization that leverages the relationship between term frequency and a given response variable in addition to co-occurrences between terms to recover topics that are both predictive of consumer sentiment and useful for understanding the underlying textual themes. The factorization can provide guidance on a single app's performance as well as systematically compare different apps over time for benchmarking of features and consumer sentiment. We apply our approach using a dataset of over 81,000 mobile reviews over several years for the two of the most reviewed online travel agent apps from the iOS and Google Play marketplaces.