A study of retrospective and on-line event detection
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On-line new event detection and tracking
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A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Extracting significant time varying features from text
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Automatic generation of overview timelines
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Text classification using ESC-based stochastic decision lists
Information Processing and Management: an International Journal
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Topic-conditioned novelty detection
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ThemeRiver: Visualizing Theme Changes over Time
INFOVIS '00 Proceedings of the IEEE Symposium on Information Vizualization 2000
Topic analysis using a finite mixture model
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A decision-theoretic extension of stochastic complexity and its applications to learning
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Key semantics extraction by dependency tree mining
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Parameter free bursty events detection in text streams
VLDB '05 Proceedings of the 31st international conference on Very large data bases
A probabilistic approach to spatiotemporal theme pattern mining on weblogs
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MONIC: modeling and monitoring cluster transitions
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Topic evolution and social interactions: how authors effect research
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Delineating the citation impact of scientific discoveries
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Time-dependent event hierarchy construction
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Short communication: Variable space hidden Markov model for topic detection and analysis
Knowledge-Based Systems
Storyline-based summarization for news topic retrospection
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Incorporating topic transition in topic detection and tracking algorithms
Expert Systems with Applications: An International Journal
A framework for WWW user activity analysis based on user interest
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A sentence level probabilistic model for evolutionary theme pattern mining from news corpora
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Detecting topic evolution in scientific literature: how can citations help?
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Tracing conceptual and geospatial diffusion of knowledge
OCSC'07 Proceedings of the 2nd international conference on Online communities and social computing
Hierarchical clustering for topic analysis based on variable feature selection
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 7
Topic dynamics: an alternative model of bursts in streams of topics
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The web of topics: discovering the topology of topic evolution in a corpus
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Following the social media: aspect evolution of online discussion
SBP'11 Proceedings of the 4th international conference on Social computing, behavioral-cultural modeling and prediction
Online heterogeneous mixture modeling with marginal and copula selection
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Web text clustering with dynamic themes
WISM'11 Proceedings of the 2011 international conference on Web information systems and mining - Volume Part II
Topics modeling based on selective Zipf distribution
Expert Systems with Applications: An International Journal
Indices of novelty for emerging topic detection
Information Processing and Management: an International Journal
Discovering emerging topics in unlabelled text collections
ADBIS'06 Proceedings of the 10th East European conference on Advances in Databases and Information Systems
HotDigg: finding recent hot topics from digg
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Comparative document summarization via discriminative sentence selection
ACM Transactions on Knowledge Discovery from Data (TKDD)
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DEXA'07 Proceedings of the 18th international conference on Database and Expert Systems Applications
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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 learning the finite mixture model dynamically. In this framework we propose the usage of a time-stamp based discounting learning algorithm in order to realize real-time topic structure identification. This enables tracking the topic structure adaptively by forgetting out-of-date statistics. Further we apply the theory of dynamic model selection to detecting changes of main components in the finite mixture model in order to realize topic emergence detection. We demonstrate the effectiveness of our framework using real data collected at a help desk to show that we are able to track dynamics of topic trends in a timely fashion.