Incorporating Metadata into Dynamic Topic Analysis

Everyday millions of blogs and micro-blogs are posted on the Internet These posts usually come with useful metadata, such as tags, authors, locations, etc. Much of these data are highly specific or personalized. Tracking the evolution of these data helps us to discover trending topics and users’ interests, which are key factors in recommendation and advertisement placement systems. In this paper, we use topic models to analyze topic evolution in social media corpora with the help of metadata. Specifically, we propose a flexible dynamic topic model which can easily incorporate various type of metadata. Since our model adds negligible computation cost on the top of Latent Dirichlet Allocation, it can be implemented very efficiently. We test our model on both Twitter data and NIPS paper collection. The results show that our approach provides better performance in terms of held-out likelihood, yet still retains good interpretability.