Bing Liu
Title: Sentiment Analysis, Lifelong Learning, and Intelligent Personal Assistants

    Abstract:  Sentiment analysis (SA) or opinion mining is the computational study of opinions, sentiments, attitudes, emotions, moods and affects. Due to almost unlimited applications and numerous research challenges, SA is a very active research area in natural language processing and data mining. In this talk, I will first introduce the SA problem and discuss the recent work of using big data and lifelong learning to help solve the problem. I then discuss a consumer application of sentiment analysis. This naturally leads to the interesting topic of intelligent personal assistants such as Siri, Cortana, and Google Now, and Chatbots such as XiaoIce. I believe such systems will be the next big thing of AI and will profoundly change our lives. For these systems to be very useful and widely adopted, they need to perform extensive opinion, sentiment, and emotion analysis, and to converse with humans affectively.  

    Bing Liu is a professor of Computer Science at the University of Illinois at Chicago (UIC). He received his Ph.D. in Artificial Intelligence from the University of Edinburgh. Before joining UIC, he was a faculty member at the National University of Singapore. His current research interests include sentiment analysis and opinion mining, data mining, machine learning, and natural language processing (NLP). He has published extensively in top conferences and journals in these areas. Two of his papers received KDD Test-of-Time awards. He also authored three books: “Sentiment Analysis: Mining Opinions, Sentiments, and Emotions” (Cambridge University Press, 2015), “Sentiment Analysis and Opinion Mining” (Morgan and Claypool, 2012), “Web Data Mining: Exploring Hyperlinks, Contents and Usage Data” (Springer, 2006, 2011). In addition to research impact, his work has made important societal impact. He and his work have been featured extensively in the press, including a front-page article in The New York Times. On professional services, he has served as the program chair of the flagship data mining conferences of ACM, IEEE, and SIAM, i.e., KDD, ICDM and SDM respectively, and of CIKM, WSDM, and PAKDD, as associate editors of several leading data mining journals, e.g., TKDE, TWEB, DMKD, and as area/track chairs or senior program committee members of numerous NLP, data mining, and Web technology conferences. He currently serves as the Chair of ACM SIGKDD and is an IEEE Fellow.