Abstract:
Automatically detecting sentiment, emotion, and stance expressed in tweets, blogs, product reviews, and SMS messages has attracted extensive interest from both academia and industry. It has a number of applications, including: tracking sentiment towards events, politicians, products, and service; detecting happiness and well-being; improving customer relation models; improving automatic dialogue systems, etc.
About the Speaker:
Dr. Xiaodan Zhu is an Assistant Professor of the Department of Electrical and Computer Engineering. He leads the Text Analytics and Machine Learning Lab (TAML). Dr. Zhu received his Ph.D. from the Department of Computer Science at the University of Toronto in 2010 and his Masters of Engineering from the Department of Computer Science and Technology at Tsinghua University in 2000. He was a researcher of National Research Council Canada from 2010 to 2017.
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