Extracting Topics From Weblogs Through Frequency Segments

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.