Aaron Sun

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Modeling and detecting bursts in data streams is an important area of research with a wide range of applications. In this paper, we present a novel method to analyze and identify correlated burst patterns by considering multiple data streams that co-evolve over time. The main technical contribution of our research is the use of a dynamic probabilistic(More)
Many machine learning, statistical, and computational linguistic methods have been developed to identify sentiment of sentences in documents, yielding promising results. However, most of state-of-the-art methods focus on individual sentences and ignore the impact of context on the meaning of a sentence. In this paper, we propose a method based on(More)
Studying information diusion through social networks has become an active research topic with important implications in viral marketing applications. One of the fundamental algorithmic problems related to viral marketing is the Inuence Maximization (IM) problem: given an social network, which set of nodes should be considered by the viral marketer as the(More)
Social media is becoming a major and popular technological platform that allows users to express personal opinions toward the subjects with shared interests. Identifying the sentiments of these social media data can help users make informed decisions. Existing research mainly focus on developing algorithms by mining textual information in social media.(More)
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