学术报告-Topic Derivation in Twitter-杨坚

发布时间:2016-04-13浏览次数:238

报告时间:2016419日(周二)下午1:00

报告地点:计算机学院140

报告题目:Topic Derivation in Twitter


作者简介:

Dr. Jian Yang is a full professor at Department of Computing, Macquarie University. She received her PhD from The Australian National University in 1995. Before she joined Macquarie University,she worked as an associate professor at Tilburg University, Netherlands(2000-2003), a senior research scientist at the Division of Mathematical and Information Science, CSIRO, Australia (1998-2000), and as an assistant professor at The Australian Defence Force Academy, University of New SouthWales (1993-1998).

Dr. Yang has published about 200 papers in the international journals and conferences such as IEEE transactions, Information Systems, Data & Knowledge Engineering, CACM,VLDB, ICDCS, CAiSE, CoopIS, CIKM, etc. She is the co-founder of theInternational Conference on Service Oriented Computing and now serving as asteering committee member. She has served as program committee co-chairs andgeneral chair of in various international conferences. She is also a regular reviewer for journals such as IEEE Transactions on Knowledge & Data Engineering, Data & Knowledge Engineering, VLDB Journal, IEEE InternetComputing, etc.

Her main research interests are: web service technology; business process management;interoperability, trust and security issues in digital libraries ande-commerce; social network.


讲座摘要:

As one of the most popularsocial media, Twitter has attracted interests of business and academics toderive topics and apply the outcomes in a wide range of applications such asemergency management, business advertisements, and corporate/governmentcommunication. Since tweets are short messages, topic derivation from tweetsbecomes a big challnege in the area. Most of existing works use theTwitter content as the only source in the topic derivation. Recently, tweetinteractions have been considered additionally for improving the quality oftopic derivation. In this talk, we introduce a method that incorporatessocial interactions such as mention, retweet, etc intotwitter content to derive topics.  Experimental results show that the proposed method with the inclusion of temporal features resultsin a significant improvement in the quality of topic derivation comparingto existing baseline methods.

In this talk, we will explain the general idea of  Matrix Factorisation and how it is appliedin topic derivation, the experiment set up, and experiment results analysis.