Visualizing dynamics of the hot topics using sequence-based self-organizing maps

Authors:
Ken-ichi Fukui;Kazumi Saito;Masahiro Kimura;Masayuki Numao
Affiliations:
Dept. of Information Science and Technology, Osaka University, Japan;NTT Communication Science Laboratories, Japan;Department of Electronics and Informatics, Ryukoku University, Japan;The Institute of Scientific and Industrial Research, Osaka University, Japan
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
Year:
2005

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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.