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Tracking dynamics of topic trends using a finite mixture model

10 years 6 months ago
Tracking dynamics of topic trends using a finite mixture model
In a wide range of business areas dealing with text data streams, including CRM, knowledge management, and Web monitoring services, it is an important issue to discover topic trends and analyze their dynamics in real-time. Specifically we consider the following three tasks in topic trend analysis: 1)Topic Structure Identification; identifying what kinds of main topics exist and how important they are, 2)Topic Emergence Detection; detecting the emergence of a new topic and recognizing how it grows, 3)Topic Characterization; identifying the characteristics for each of main topics. For real topic analysis systems, we may require that these three tasks be performed in an on-line fashion rather than in a retrospective way, and be dealt with in a single framework. This paper proposes a new topic analysis framework which satisfies this requirement from a unifying viewpoint that a topic structure is modeled using a finite mixture model and that any change of a topic trend is tracked by learni...
Satoshi Morinaga, Kenji Yamanishi
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2004
Where KDD
Authors Satoshi Morinaga, Kenji Yamanishi
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