On this thesis, we have developed a set of methods along with a framework for discovery of bursty events and their relationship from streams of online news articles. Bursty event discovery can be done using the discovered bursty terms which are significantly smaller in size compared to the original feature-set. Moreover, the discovered bursty events are compared in order to discover any potential relational link between any of two. It is the assumption of this work that bursty events and their relationship in time can provide useful information to firms and individuals who their decision making process is significantly affected by news events. The system performed at 64% level of accuracy on a real world dataset. The results show a great promise as do the implicit measures that our proposed framework and methods can be utilized towards real world applications.
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