Digging Deeper: Representation Learning for Fine-Grained Sentiment and Emotion Analysis of Text

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.