Abstract
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. © Springer-Verlag Berlin Heidelberg 2005.
Cite
CITATION STYLE
Fukui, K. I., Saito, K., Kimura, M., & Numao, M. (2005). Visualizing dynamics of the hot topics using sequence-based self-organizing maps. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3684 LNAI, pp. 745–751). Springer Verlag. https://doi.org/10.1007/11554028_104
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