We are currently working on a SOM-based method for temporal analysis and visualization of “hot topic” trends in news articles.
Hot topics are extracted from a document collection by applying PCA to term frequency bag-of-words vectors. Evaluative experiments
on three data sets, the largest expands across ten years, show that SBSOM induces a sequential analysis and that the use of
label confidence mitigates the performance loss.