Advances in Natural Language Processing pp 239-249
Applying a Burst Model to Detect Bursty Topics in a Topic Model
- Cite this paper as:
- Takahashi Y. et al. (2012) Applying a Burst Model to Detect Bursty Topics in a Topic Model. In: Isahara H., Kanzaki K. (eds) Advances in Natural Language Processing. Lecture Notes in Computer Science, vol 7614. Springer, Berlin, Heidelberg
This paper focuses on two types of modeling of information flow in news stream, namely, burst analysis and topic modeling. First, when one wants to detect a kind of topics that are paid much more attention than usual, it is usually necessary for him/her to carefully watch every article in news stream at every moment. In such a situation, it is well known in the field of time series analysis that Kleinberg’s modeling of bursts is quite effective in detecting burst of keywords. Second, topic models such as LDA (latent Dirichlet allocation) are also quite effective in estimating distribution of topics over a document collection such as articles in news stream. However, Kleinberg’s modeling of bursts is usually applied only to bursts of keywords but not to those of topics. Considering this fact, we propose how to apply Kleinberg’s modeling of bursts to topics estimated by a topic model such as LDA and DTM (dynamic topic model).
KeywordsTime Series News Topic Model Kleinberg’s Burst Model
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