A study of retrospective and on-line event detection
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Introduction to topic detection and tracking
Topic detection and tracking
INFOVIS '00 Proceedings of the IEEE Symposium on Information Vizualization 2000
The Journal of Machine Learning Research
Text classification and named entities for new event detection
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
LDA-based document models for ad-hoc retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A new suffix tree similarity measure for document clustering
Proceedings of the 16th international conference on World Wide Web
Topic sentiment mixture: modeling facets and opinions in weblogs
Proceedings of the 16th international conference on World Wide Web
Topic Detection and Tracking for News Web Pages
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Concept Forest: A New Ontology-assisted Text Document Similarity Measurement Method
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
Opinion integration through semi-supervised topic modeling
Proceedings of the 17th international conference on World Wide Web
Document similarity based on concept tree distance
Proceedings of the nineteenth ACM conference on Hypertext and hypermedia
Modeling hidden topics on document manifold
Proceedings of the 17th ACM conference on Information and knowledge management
Automatic online news topic ranking using media focus and user attention based on aging theory
Proceedings of the 17th ACM conference on Information and knowledge management
Rated aspect summarization of short comments
Proceedings of the 18th international conference on World wide web
Meme-tracking and the dynamics of the news cycle
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Similarity-based Classification: Concepts and Algorithms
The Journal of Machine Learning Research
A mixture model for expert finding
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Temporal expert finding through generalized time topic modeling
Knowledge-Based Systems
PET: a statistical model for popular events tracking in social communities
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning summary content units with topic modeling
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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Online news usually describes various events over multiple topics. Some of them may generate great impact and affection on other events, organizations or people. For example, a bankruptcy news about a big company may generate a great impact on other companies. Detecting this kind of impact helps users better to understand the affection of a specified event and its epidemic. Powerful text mining techniques have been developed to help users to detect topic trends of news articles. However, there is a lack of effective analysis tools that analyze and reveal the news impact in an intuitive approach. In this paper, we introduce Impact Wheel, an explorative visual analysis system for topic driven news impact detection. We describe two unique aspects of Impact Wheel, including 1) topic driven impact analysis and 2) interactive rich context visualization. Experiments on performance evaluation show that our proposed approach outperforms the two baseline methods on topic driven impact analysis. In addition, we demonstrate the power of the Impact Wheel system through a case study, which shows the benefits of this work, especially in support of rich topic data analysis.