Bursty and Hierarchical Structure in Streams
Data Mining and Knowledge Discovery
The Journal of Machine Learning Research
Incorporating non-local information into information extraction systems by Gibbs sampling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
In-depth behavior understanding and use: The behavior informatics approach
Information Sciences: an International Journal
Proceedings of the 2012 international workshop on Socially-aware multimedia
Detecting real-time burst topics in microblog streams: how sentiment can help
Proceedings of the 22nd international conference on World Wide Web companion
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A convergence of emotions among people in social networks is potentially resulted by the occurrence of an unprecedented event in real world. E.g., a majority of bloggers would react angrily at the September 11 terrorist attacks. Based on this observation, we introduce a sentiment index, computed from the current mood tags in a collection of blog posts utilizing an affective lexicon, potentially revealing subtle events discussed in the blogosphere. We then develop a method for extracting events based on this index and its distribution. Our second contribution is establishment of a new bursty structure in text streams termed a sentiment burst. We employ a stochastic model to detect bursty periods of moods and the events associated. Our results on a dataset of more than 12 million mood-tagged blog posts over a 4-year period have shown that our sentiment-based bursty events are indeed meaningful, in several ways.