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Tweets mining using Wikipedia and impurity cluster measurement
Abstract Twitter is one of the fastest growing onliTwitter is one of the fastest growing online social networking services. Tweets can be categorized into trends, and are related with tags and follower/following social relationships. The categorization is neither accurate nor effective due to the short length of tweet messages and noisy data corpus. In this paper, we attempt to overcome these challenges with an extended feature vector along with a semi-supervised clustering technique. In order to achieve this goal, the training set is expanded with Wikipedia topic search result, and the feature set is extended. When building the clustering model and doing the classification, impurity measurement is introduced into our classifier platform. Our experiment results show that the proposed techniques outperform other classifiers with reasonable precision and recall.iers with reasonable precision and recall.
Abstractsub Twitter is one of the fastest growing onliTwitter is one of the fastest growing online social networking services. Tweets can be categorized into trends, and are related with tags and follower/following social relationships. The categorization is neither accurate nor effective due to the short length of tweet messages and noisy data corpus. In this paper, we attempt to overcome these challenges with an extended feature vector along with a semi-supervised clustering technique. In order to achieve this goal, the training set is expanded with Wikipedia topic search result, and the feature set is extended. When building the clustering model and doing the classification, impurity measurement is introduced into our classifier platform. Our experiment results show that the proposed techniques outperform other classifiers with reasonable precision and recall.iers with reasonable precision and recall.
Bibtextype inproceedings  +
Doi 10.1109/ISI.2010.5484758  +
Has author Qingcai Chen + , Shipper T. + , Khan L. +
Has extra keyword Classification + , Clustering model + , Feature sets + , Feature vectors + , Impurity clusters + , Noisy data + , Precision and recall + , Search results + , Semi-supervised Clustering + , Social networking + , Social relationships + , Training sets + , Tweet mining + , Wikipedia + , Information science + , Probabilistic logics + , Social networking (online) + , Classifiers +
Has keyword Extended features + , Tweet mining + , Wikipedia +
Isbn 9781424464609  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 141–143  +
Published in ISI 2010 - 2010 IEEE International Conference on Intelligence and Security Informatics: Public Safety and Security +
Title Tweets mining using Wikipedia and impurity cluster measurement +
Type conference paper  +
Year 2010 +
Creation dateThis property is a special property in this wiki. 8 November 2014 07:24:13  +
Categories Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 8 November 2014 07:24:13  +
DateThis property is a special property in this wiki. 2010  +
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