Paper: Topic Models and Metadata for Visualizing Text Corpora

ACL ID N13-3002
Title Topic Models and Metadata for Visualizing Text Corpora
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session System Demonstration
Year 2013
Authors

Effectively exploring and analyzing large text corpora requires visualizations that provide a high level summary. Past work has relied on faceted browsing of document metadata or on natural language processing of document text. In this paper, we present a new web-based tool that integrates topics learned from an unsuper- vised topic model in a faceted browsing expe- rience. The user can manage topics, filter doc- uments by topic and summarize views with metadata and topic graphs. We report a user study of the usefulness of topics in our tool.