Collaborative Research: HCC: Medium: Fine-grained Emotion Analysis in Crises

合作研究：HCC：中：危机中的细粒度情绪分析

• 批准号: 2107487
• 负责人: Robert Sloan
• 金额: $ 34.76万
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2107487&HistoricalAwards=false
• 关键词: Collaborative; Research; HCC; Medium; Fine

History is rich with situations where the same event has been interpreted completely differently by different groups of people. Through events such as the OJ Simpson case, the COVID-19 crisis, and the murder of George Floyd, we have observed disparate reactions to events that community leaders, police departments, policymakers, and everyday citizens fail to anticipate. The purpose of this project is to begin to identify social, emotional, and linguistic markers of crises (e.g., social turmoil, natural disasters, etc.) that predict the various ways people will react to the same events. This is achieved by analyzing the language of social media, a rapidly-growing source of data from which we can understand the expression and perception of emotions at a very large scale, with far-reaching potential uses from academic research to public policy.Understanding emotions, the context surrounding these emotions, and subsequent behaviors are of great value to those in a crisis, seeking information about a crisis, or helping manage responses to a crisis. This project will discover mechanisms to provide comprehensive, fine-grained emotion analysis across different social platforms, and derive robust and reliable predictive models. Fine-grained emotion analysis aims to: (1) detect expressions of emotions in a text and characterize their intensity and polarity, (2) identify the triggers causing the emotions, and (3) analyze emotion deviation (i.e., the varied emotions that people express towards the same trigger). This research will contribute annotated datasets of emotions expressed on social media across distinct crises and generalizable models equipped with deep linguistic understanding for contextualized emotion analysis. Industry and academic partners will participate by evaluating the ability of the models to work on situations and data sources different from those used to develop the models.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

历史丰富的情况充满了不同的事件的解释，不同的人群完全不同。通过诸如OJ Simpson案，Covid-19危机以及乔治·弗洛伊德（George Floyd）的谋杀之类的事件，我们观察到对社区领导人，警察部门，政策制定者和日常公民未能预料到的事件的反应不同。该项目的目的是开始确定危机的社会，情感和语言标志（例如，社会动荡，自然灾害等），以预测人们对同一事件的各种方式。这是通过分析社交媒体的语言来实现的，社交媒体的语言是一种快速增长的数据来源，我们可以从中了解到情绪的表达和感知，从学术研究到公共政策，具有深远的潜在用途。理解情感，围绕这些情绪的环境，以及随后的行为对危机的人来说是极大的价值，可以使人们对危机进行crisis的响应，从而响应crisis crisis，以响应一个响应。该项目将发现机制，以在不同的社交平台上提供全面，细粒度的情绪分析，并得出强大而可靠的预测模型。细粒度的情绪分析的目的是：（1）检测文本中情绪的表达并表征其强度和极性，（2）确定引起情绪的触发因素，（3）分析情绪偏差（即人们向同一触发器表达的各种情绪）。这项研究将贡献在社交媒体上围绕不同的危机和具有深厚语言理解的可推广模型在社交媒体上表达的注释数据集，以进行情境分析。 行业和学术合作伙伴将通过评估模型在与开发模型的情况不同的情况和数据源的能力来参与。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛影响的评估来评估的评估。