SoCS: Studying the Computability of Emotions by Harnessing Massive Online Social Data

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The emergence of massive human-rated and commented visual data has opened avenues for exploring fundamental questions in artificial intelligence beyond the horizon. This project tackles the challenge of automatically inferring visual aesthetics and emotions and inventing new systems that assist creative and decision-making activities of the general public. An interdisciplinary team, with expertise in visual modeling, data mining, psychology, and computational sciences will build tools to distill information from a combination of visual, textual, and numerical data. Visual features, selected based on published literature and consultation with domain experts, will be extracted for discriminating types of emotions. The resulting systems can select and rank visual information based on aesthetics and emotions.Intellectual Merits: This project will allow computer scientists to gain understanding of next-generation computerized visual aesthetics and emotion assessment systems. The complex inter-relationship among content, context, and subjectivity in aesthetics and emotion assessment makes the corresponding learning problems especially challenging, which is likely to trigger innovation in machine learning and statistical modeling. Such capabilities will fundamentally change the way visual information is analyzed, processed, and managed. The project will advance our understanding of the computability of emotions, and lead to new applications that can be used in a variety of settings.Broader Impacts: The research will have a transformative impact in the fields of information retrieval, human-computer interaction, information processing, consumer electronics, and design. The technology can also be used to refine multimedia content that serves as education resources. The project will disseminate research findings, generate new software implementations and collected datasets, and provide online services that can be used by researchers, educators, and industry. Education efforts include developing an interdisciplinary curriculum, training cross-disciplinary scientists, and involving underrepresented groups in research.