学术活动发布与订制系统

Sentiment analysis (SA) or opinion mining is the computational study of people’s opinions, sentiments, evaluations, appraisals, and emotions. Due to numerous challenging research problems and a wide range of applications, SA has been an active research area in many CS fields. In recent years, its research has spread from CS to social, management, and health sciences. Its applications have spread from traditional businesses, organizations, and individuals applications to conversational agents such as chatbots and intelligent personal assistants. There are now hundreds of companies in the SA space. In the first part of the tutorial, I will introduce SA and discuss the current state-of-the-art SA techniques. In the second part, I will introduce lifelong machine learning (LML), which follows naturally from the discussion of SA as LML is particularly suitable for solving SA and NLP problems. LML is different from the classic machine learning (ML) paradigm in that classic ML simply runs a learning algorithm on a given data, which we call isolated learning, while LML aims to learn as humans do. It retains and cumulates the knowledge learned in the past and uses it to help future learning and problem solving.

讲演者简介

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, the premier conference of knowledge discovery and data mining. 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 leading data mining conferences of ACM, IEEE, and SIAM: KDD, ICDM, CIKM, WSDM, and SDM, 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. He is an ACM, IEEE and AAAI Fellow.