Vers une approche Visual Analytics pour explorer les variantes de sujets d'un corpus
Résumé
Our purpose is to implement a Visual Analytics tool for exploring topic variants in text
corpora. The overlapping bi-clustering methods extract multiple topics from the documents,
but the interpretation of the results remains difficult. We make the assumption that bi-cluster
overlaps are articulation points between high-level topics, and their multiple variants and viewpoints.
We propose to extract and visualize a hierarchical structure of bi-cluster overlaps, allowing
to explore the corpus and to discover unsuspected viewpoints.