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俄勒冈州立大學 朱斌副教授:Gender classification of product reviewers in china: a data driven approach

([西财新闻] 发布于 :2019-11-28 )

光华讲坛——社会名流与企业家论坛第 5633 期

 

主題Gender classification of product reviewers in china: a data driven approach

主講人俄勒冈州立大學 朱斌 副教授

主持人经济信息工程学院 郑海超 教授

時間2019年11月29日(星期五)上午10:00

地點西南財經大學柳林校區格致樓J311B

主辦單位:经济信息工程学院  金融智能与金融工程四川省重点实验室  科研處

 

主講人簡介:

Dr. Bin Zhu is an Associate Professor and the program director of Business Analytics in the College of Business. 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.

朱斌博士是俄勒冈州立大學商学院的副教授和商业分析专业项目主任。在此之前,她曾是波士顿大學的助理教授。她在亚利桑那大學获得了管理信息系统博士学位。目前的研究方向包括商业智能、信息分析、社交网络、人机交互、信息可视化、计算机沟通和知识管理系统。她作为主要作者在 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等期刊上发表过论文。研究成果获得过IBM教员奖。教学兴趣包括商业智能、数据库分析与设计、电信、网络技术、程序设计、数据结构和算法、电子商务、信息安全/保障、管理信息系统。

 

內容提要:

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.

對于企業而言,自動識別産品論壇中參與者的性別至關重要。但是,目前企業在應用性別分類方法過程中仍存在困難,一個很大的因素是分類模型高度情景化,即不同情境中識別性別的特征有很大差異。主要提出並驗證了一個框架,來開發“數據驅動”的、適應情境變化的分類方法。除了優化性別分類方法之外,還可以通過調整其應用于在線産品論壇數據的方式來提高模型的性能。對于任何給定的在線論壇數據和給定的分類方法,分類准確性會隨著輸入數據的大小而變化。存在一個最佳的輸入數據樣本大小來後的最高的准確性。這與我們認爲的“更大的數據大小總是導致更好的分類性能”這一常識不一致。


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