報告時間：2022年6月10日（星期五）9:00-10:30
報告平臺：騰訊會議 ID:854-240-604、密碼:1515
報 告 人：Weiguo (Patrick) Fan 教授
工作單位：University of Iowa
舉辦單位：管理學(xué)院
報告簡介：
Most existing research on mining online reviews focus on issues such as the impact of reviews on sales, helpfulness of reviews, and customers’ participation in reviews. Few research studies, however, seek to identify and extract innovation ideas for products from online reviews. This type of information is particularly important for product functionality improvement and new feature development. In this paper, we propose a deep learning-based approach to identify sentences that contain innovation ideas from online reviews. Specifically, we develop a novel ensemble embedding method to generate semantic and contextual representations of the words in review sentences. The resultant representations in each sentence are then used in a long short-term memory (LSTM) model for innovation-sentence identification. Moreover, we adopt a focal loss function in our model to address the class imbalance problem.
報告人簡介：
Dr. Weiguo (Patrick) Fan is a Henry B. Tippie Excellence Chair Professor in Business Analytics at the University of Iowa. His research interests focus on the design and development of novel information technologies — information retrieval, data mining, text analytics, social media analytics, business intelligence techniques — to support better business information management and decision making. His research has appeared in many premier IT/IS/OM journals such as MIS Quarterly, Information Systems Research, Journal of Management Information Systems, Productions and Operations Management, IEEE Transactions on Knowledge and Data Engineering, etc.
