Keywords for Searching Topical Posts and Negative Covariate Shift
报告题目:Keywords for Searching Topical Posts and
Negative Covariate Shift
报告人：Professor Bing Liu
(Fellows of ACM, AAAI and IEEE ,KDD Chair)
时间：2016年3月23日 上午 10:00
In almost any application of social media content analysis, the user is interested in studying a particular topic. Collecting posts relevant to the topic from a social media data source is a necessary step. Due to the huge size of social media sources (e.g., Twitter, Weibo, and Facebook), the user has to use some keywords to search for relevant posts. However, gathering a set of representative topical keywords is a very tedious and time-consuming task that often involves a lengthy iterative process of searching and manual reading. In this talk, I first discuss this problem and present an algorithm to help the user discover such search keywords. After searching using the keywords, the resulting set of posts can still be quite noisy because many posts containing the keywords may not be relevant. A supervised learning step is needed to filter out those irrelevant posts. Here I discuss a sampling selection bias problem faced by learning, called negative covariate shift, and present an algorithm to deal with it.
Bing Liu is a professor of Computer Science at the University of Illinois at Chicago (UIC). He received his PhD in Artificial Intelligence from the University of Edinburgh. His research interests include sentiment analysis and opinion mining, lifelong machine learning, fake/deceptive opinion detection, data mining, and natural language processing. He has published extensively in top conferences and journals. Two of his papers received 10-year test-of-time awards from KDD. He also authored three books: two on sentiment analysis and one on Web data mining. Some of his work has been widely reported in the press, including a front-page article in The New York Times. On professional services, Liu has served as program chairs of many leading data mining conferences such as KDD, ICDM, CIKM, WSDM, SDM, and PAKDD, as associate editors of leading journals such as TKDE, TWEB, DMKD, and as area chairs of numerous NLP, Web technology, and data mining conferences. Currently, he serves as the Chair of ACM SIGKDD, and is a Fellow of ACM, AAAI and IEEE.