Mining correlated bursty topic patterns from coordinated text streams

Previous work on text mining has almost exclusively focused on a single stream. However, we often have available multiple text streams indexed by the same set of time points (called coordinated text streams), which offer new opportunities for text mining. For example, when a major event happens, all the news articles published by different agencies in different languages tend to cover the same event for a certain period, exhibiting a correlated bursty topic pattern in all the news article streams. In general, mining correlated bursty topic patterns from coordinated text streams can reveal interesting latent associations or events behind these streams. In this paper, we define and study this novel text mining problem. We propose a general probabilistic algorithm which can effectively discover correlated bursty patterns and their bursty periods across text streams even if the streams have completely different vocabularies (e.g., English vs Chinese). Evaluation of the proposed method on ...

• Type: Conference
• Year: 2007
• Where: KDD
• Authors: Xuanhui Wang, ChengXiang Zhai, Xiao Hu, Richard Sproat
• Keywords: Data Mining, Database Publication Streams, KDD 2007, Multiple Text Streams, Text Streams