Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

12-2012

Abstract

Many event monitoring systems rely on counting known keywords in streaming text data to detect sudden spikes in frequency. But the dynamic and conversational nature of Twitter makes it hard to select known keywords for monitoring. Here we consider a method of automatically finding noun phrases (NPs) as keywords for event monitoring in Twitter. Finding NPs has two aspects, identifying the boundaries for the subsequence of words which represent the NP, and classifying the NP to a specific broad category such as politics, sports, etc. To classify an NP, we define the feature vector for the NP using not just the words but also the author's behavior and social activities. Our results show that we can classify many NPs by using a sample of training data from a knowledge-base. © 2012 ACM.

Keywords

Named entities, Noun phrases, Social media, Twitter

Discipline

Computer Sciences | Social Media

Publication

CIKM '12 Proceedings of the 21st ACM international conference on Information and knowledge management

First Page

1702

Last Page

1706

ISBN

9781450311564

Identifier

10.1145/2396761.2398501

Publisher

ACM

City or Country

New York, USA

Copyright Owner and License

LARC

Additional URL

http://doi.org/10.1145/2396761.2398501

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