We present LDAvis, a web-based interac-tive visualization of topics estimated using Latent Dirichlet Allocation that is built us-ing a combination of R and D3. Our visu-alization provides a global view of the top-ics (and how they differ from each other), while at the same time allowing for a deep inspection of the terms most highly asso-ciated with each individual topic. First, we propose a novel method for choosing which terms to present to a user to aid in the task of topic interpretation, in which we define the relevance of a term to a topic. Second, we present results from a user study that suggest that ranking terms purely by their probability under a topic is suboptimal for topic interpretation. Last, we describe LDAvis, our visualization system that allows users to flexibly explore topic-term relationships using relevance to better understand a fitted LDA model. 1
Sievert et al. (2014) studied this question.