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Digging Deeper: Representations for Fine-Grained Affective Text Analysis

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Dr. Gerard de Melo, Rutgers University
Thursday, November 14, 2019, 4PM
SBUS 114

Digging Deeper: Representations for Fine-Grained Affective Text Analysis

How do we identify what kinds of sentiment and emotion a text evokes? While there is a long history of research on sentiment analysis, this talk describes new methods that draw on representation learning and deep learning to provide a more detailed understanding of a text and its affective associations. This encompasses methods that predict the specific emotions associated with a text, considering not only the semantic content but also 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. This also includes methods that better account for the sentence context in which words occur. The talk will conclude with an overview of applications and specific analyses resulting from this work.

Gerard de Melo is an Assistant Professor at Rutgers University, where he serves as the Director of the Deep Data Lab. 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. Prior to joining Rutgers, he was a faculty member at Tsinghua University and a Post-Doctoral Research Scholar at ICSI/UC Berkeley. He received his doctoral degree at the Max Planck Institute for Informatics. Notable research projects include Lexvo.org, FrameBase.org, the Universal WordNet, and the Etymological WordNet. For more information, please consult http://gerard.demelo.org.