Nonparametric Bayesian Topic Modelling with the Hierarchical Pitman-Yor Processes
This work addresses topic modeling for social media data, offering improved performance over parametric methods, but it is incremental as it builds on existing nonparametric Bayesian frameworks.
The authors tackled the problem of modeling text from social media, specifically tweets, by proposing a nonparametric Bayesian topic model using hierarchical Pitman-Yor processes, and found that it outperforms existing parametric models in goodness of fit and real-world applications.
The Dirichlet process and its extension, the Pitman-Yor process, are stochastic processes that take probability distributions as a parameter. These processes can be stacked up to form a hierarchical nonparametric Bayesian model. In this article, we present efficient methods for the use of these processes in this hierarchical context, and apply them to latent variable models for text analytics. In particular, we propose a general framework for designing these Bayesian models, which are called topic models in the computer science community. We then propose a specific nonparametric Bayesian topic model for modelling text from social media. We focus on tweets (posts on Twitter) in this article due to their ease of access. We find that our nonparametric model performs better than existing parametric models in both goodness of fit and real world applications.