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

SoCS：利用海量在线社交数据研究情绪的可计算性

• 批准号: 1110970
• 负责人: James Wang
• 金额: $ 72.08万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1110970&HistoricalAwards=false
• 关键词: SoCS; Studying; Computability; Emotions; Harnessing

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.

大量人类评级和评论的视觉数据的出现为探索人工智能中超越地平线的基本问题开辟了道路。 该项目解决了自动推断视觉美学和情感的挑战，并发明了新的系统，以帮助公众的创造性和决策活动。 一个在视觉建模、数据挖掘、心理学和计算科学方面具有专业知识的跨学科团队将构建工具，从视觉、文本和数字数据的组合中提取信息。 视觉特征，选择的基础上发表的文献和咨询领域专家，将被提取用于区分类型的情绪。 由此产生的系统可以选择和排名视觉信息的基础上美学和emotions.Intellectual优点：这个项目将使计算机科学家获得下一代计算机视觉美学和情感评估系统的理解。 美学和情感评估中的内容、情境和主观性之间的复杂相互关系使得相应的学习问题特别具有挑战性，这可能会引发机器学习和统计建模的创新。这些能力将从根本上改变视觉信息的分析、处理和管理方式。 该项目将促进我们对情感可计算性的理解，并导致新的应用程序，可以在各种设置中使用。更广泛的影响：该研究将在信息检索，人机交互，信息处理，消费电子和设计领域产生变革性的影响。该技术还可用于改进作为教育资源的多媒体内容。 该项目将传播研究成果，生成新的软件实现和收集的数据集，并提供可供研究人员，教育工作者和行业使用的在线服务。教育工作包括制定跨学科课程，培训跨学科科学家，并让代表性不足的群体参与研究。