Abstract
In temporal text mining, some importance indices such as simple appearance frequency, tf-idf, and differences of some indices play the key role to identify remarkable trends of terms in sets of documents. However, most of conventional methods have treated their remarkable trends as discrete statuses for each time-point or fixed period. In order to find their trends as continuous statuses, we have considered the values of importance indices of the terms in each time-point as temporal behaviors of the terms. In this paper, we describe the method to identify the temporal behaviors of terms on several importance indices by using the linear trends. Then, we show a comparison between visualizations on each time-point by using composed indices with PCA and the trends of the emergent terms, which are detected the burst word detection method.
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Abe, H., Tsumoto, S. (2009). Comparing Temporal Behavior of Phrases on Multiple Indexes with a Burst Word Detection Method. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., Ślęzak, D., Zhu, W. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2009. Lecture Notes in Computer Science(), vol 5908. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10646-0_61
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DOI: https://doi.org/10.1007/978-3-642-10646-0_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10645-3
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