Topic Similarity Networks: Visual Analytics for Large Document Sets
Abstract
We investigate ways in which to improve the interpretability of LDA topic models by better analyzing and visualizing their outputs. We focus on examining what we refer to as topic similarity networks: graphs in which nodes represent latent topics in text collections and links represent similarity among topics. We describe efficient and effective approaches to both building and labeling such networks. Visualizations of topic models based on these networks are shown to be a powerful means of exploring, characterizing, and summarizing large collections of unstructured text documents. They help to tease out non-obvious connections among different sets of documents and provide insights into how topics form larger themes. We demonstrate the efficacy and practicality of these approaches through two case studies: 1) NSF grants for basic research spanning a 14 year period and 2) the entire English portion of Wikipedia.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 14, 2014
- Accession Number
- AD1123718
Entities
People
- Arun S. Maiya
- Robert M. Rolfe
Organizations
- Institute for Defense Analyses