TaxonoMap: An Interactive System for the Exploration and Explanation of Unsupervised Large-Scale News Classification

Creating analysis reports on events published in open source news data is a tedious task when done manually. Due to the large-scale nature of news data, analysts, such as government officials, often spend unnecessary resources when trying to research news data on a specific topic. In this paper, we present an interactive system for unsupervised classification of news articles in a dynamic set of hierarchical labels. By providing users with explanations in the form of highlighted words, we enable them to quickly assess the relevance of an article to a particular topic. We also provide aggregated visualisations to detect emerging events and include several quality-of-life enhancements such as a source rating mechanism and report generation.