Extracting Topics From Weblogs Through Frequency Segments
Mizuki Oka, Hirotake Abe and Kazuhiko Kato
Abstract: In this paper, we present an approach to extracting topics from weblogs by using terms that appear in them. We model a term in terms of frequency segments, i.e., sequential occurrences of the term over time, as the unit of characterization. A notable feature of the model is its approximation of changes in the dynamics of term frequencies; it captures the granularity of frequencies from the very beginning of their occurrence. This approximation also makes a comparison of frequency patterns of terms more effective. We report on the results obtained from weblogs that contained an event of global significance i.e., the London bombings of 2005.
Abstract: In this paper, we present an approach to extracting topics from weblogs by using terms that appear in them. We model a term in terms of frequency segments, i.e., sequential occurrences of the term over time, as the unit of characterization. A notable feature of the model is its approximation of changes in the dynamics of term frequencies; it captures the granularity of frequencies from the very beginning of their occurrence. This approximation also makes a comparison of frequency patterns of terms more effective. We report on the results obtained from weblogs that contained an event of global significance i.e., the London bombings of 2005.
Tagged as: weblogging2006
posted by wwe2006 at
8:35 AM
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