Earticle

초록

영어

In recently years, there has been rapid growth in discussion groups and micro blogging, in which an important characteristic of the entries is their trending topics on some generalized categories. Many researchers have attempted to classify trending topics by using only keywords, trending topics are rarely straightforward; they are normally expressed in a more subtle manner. It is well accepted that using high-dimensional multi-modal language features for tweets content representation and classifier training may achieve more sufficient characterization of the diverse properties of the tweets and further result in higher discrimination power of the classifiers. However, training the classifiers in a high-dimensional multi-modal feature space requires a large number of labeled training tweets, which will further result in the problem of curse of dimensionality. To tackle this problem, a hierarchical feature subset selection algorithm need to be used to enable more accurate tweets classification; where the processes for feature selection and classifier training are seamlessly integrated in a single framework. In this article, we used the LingPipe classifier to accurately classify the Twitter trending topics where it shows a substantial improvement over their state-of-the art trending topics-trained counterparts.

목차

Abstract
 1. Introduction
 2. Related Research
 3. Identifying and Categorizing Trending Topics
 4. Experiments and Results
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Jinan Fiaidhi [ Department of Computer Science Lakehead University ]
• Sabah Mohammed [ Department of Computer Science Lakehead University ]
• Aminul Islam [ Department of Computer Science Lakehead University ]
• Simon Fong [ Faculty of Science and Technology, University of Macau ]
• Tai-hoon Kim [ Department of Computer Engineering, Glocal Campus, Konkuk University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of u- and e- Service, Science and Technology

• 간기

  격월간

• pISSN

  2005-4246

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605