题目:用数据驱动的方式识别网上产品论坛里的评论者的性别      

演讲人:朱斌，副教授，俄勒冈州立大学

时间:2019年7月3日上午10:00-11:00

地点:同济大厦A楼208教室

讲座摘要:

While it is crucial for organizations to automatically identify the gender of participants in product discussion forums, they may have difficulties adopting existing gender classification methods because the performance of a classification method is highly contextual, given that the discriminative power of gender features used by a classification method varies with context. This paper proposes and validates a framework to develop a classification method that uses a more “data-driven” approach to accommodate the contextual changes. We demonstrated that in addition to optimizing a gender classification method, its performance can also be improved by optimizing the way in which it is applied to the archived data of online product discussion forums. Our study also indicates that for any given online discussion forum data and a given classification method, the classification accuracy varies with the size of input data. And there is an optimal input data size to achieve highest accuracy. This is different from the commonly accepted assumption that larger data size always leads to better classification performance.

演讲嘉宾简介:

Dr. Bin Zhu is an Associate Professor and the program director of Business Analytics in the College of Business at the Oregon State University. Prior to OSU she was an assistant professor at Boston University. She earned her Ph.D. in Management Information Systems from University of Arizona. Her current research interests include business intelligence, information analysis, social network, human-computer interaction, information visualization, computer-mediated communication, and knowledge management systems. She has been a lead author for papers that have appeared in Information Systems Research, Decision Support Systems, Journal of the American Society for Information Science and Technology, IEEE Transaction on Image Processing, and D-Lib Magazine. Her research also received an IBM faculty award.  Her teaching interests are business intelligence; database analysis and design; telecommunication; web technology; business programming; data structure and algorithms; e-commerce; information security/assurance; management information systems.