Digging Deeper: Representation Learning for Fine-Grained Sentiment and Emotion Analysis of Text
Thursday, February 20, 2020 - 3:30pm to 4:30pm
Gerard De Melo, Rutgers University Deep Data Lab
When users see a piece of text, what kinds of sentiment and emotion are evoked? While there is a long history of research on sentiment analysis, this talk describes a series of new techniques that draw on representation learning and deep learning to provide a more detailed understanding of these affective associations. This includes methods that consider how a given word may be perceived as positive in one domain, but negative in another, which we study both for English and for numerous other languages. This also encompasses methods that predict the specific emotions associated with a text, considering the semantic content as well as the way the text is presented. For example, certain fonts and colors are perceived as more exciting, while others are more likely to convey trustworthiness. Overall, these methods open up new opportunities for organizations to pay attention to what is being said about them in different markets, and to make smarter choices when presenting information to consumers. Biography: Gerard de Melo is an Assistant Professor at Rutgers University, where he serves as the Director of the Deep Data Lab. Over the years, he has published over 100 papers on natural language processing and AI, and received Best Paper/Demo awards at WWW 2011, CIKM 2010, ICGL 2008, and the NAACL 2015 Workshop on Vector Space Modeling.