Hiearchie: Visualization for Hierarchical Topic Models

Existing algorithms for understanding large collections of documents often produce output that is nearly as difficult and time consuming to interpret as reading each of the documents themselves. Topic modeling is a text understanding algorithm that discovers the “topics” or themes within a collection of documents. Tools based on topic modeling become increasingly complex as the number of topics required to best represent the collection increases. In this work, we present Hiérarchie, an interactive visualization that adds structure to large topic models, making them approachable and useful to an end user. Additionally, we demonstrate Hiérarchie’s ability to analyze a diverse document set regarding a trending news topic.