Abstract
For researchers, it is important to continue discovering and understanding key topics on their own fields. However, the analysis is almost depended on their experiences. In order to support for discovering emergent key topics as key terms in given textual datasets, we propose a method based on temporal patterns in several data-driven indices for text mining. The method consists of an automatic term extraction method in given documents, three importance indices, and temporal patterns based on results of clustering and linear trends of their centroids. Empirical studies show that the three importance indices are applied to the titles of two academic conferences about artificial intelligence field as sets of documents. After extracting the temporal patterns of automatically extracted terms, we compared the trends of the technical terms among the titles of the conferences.
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Abe, H., Tsumoto, S. (2010). Discovering Research Key Terms as Temporal Patterns of Importance Indices for Text Mining. In: Setchi, R., Jordanov, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2010. Lecture Notes in Computer Science(), vol 6278. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15393-8_34
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DOI: https://doi.org/10.1007/978-3-642-15393-8_34
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