Visual exploration of topic variants through a hybrid biclustering approach

In large text corpora, analytic journalists need to identify facts, verify them by locating corroborating documents and survey all related viewpoints. This requires them to make sense of document relationships at two levels of granularity : high-level topics and low-level topic variants. We propose a visual analytics software allowing analytic journalists to verify and refine hypotheses without having to read all documents. Our system relies on a hybrid biclustering approach. A new Weighted Topic Map visualization conveys all top-level topics reflecting their importance and their relative similarity. Then, coordinated multiple views allow to drill down into topic variants through an interactive term hierarchy visualization. Hence, the analyst can select, compare and filter out the subtle co-occurrences of terms shared by multiple documents to find interesting facts or stories. The usefulness of the tool is shown through a usage scenario and further assessed through a qualitative evaluation by an expert user.