A News Analysis and Tracking System

Continuous monitoring of web-based news sources has emerged as a key intelligence task particularly for Homeland Security. We propose a system for web-based news tracking and alerting. Unlike subscription-based alerts, alerting is implemented as a personalized service where the system is trained to recognize potentially important news based on user preferences. Preferences are expressed as combinations of topics and can change dynamically. The system employs Latent Dirichlet Allocation (LDA) for topic discovery and Latent Semantic Indexing (LSI) for alerting.