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: 2107524
• 负责人: Junyi Li
• 金额: $ 45.23万
• 依托单位: University of Texas at Austin
• 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=2107524&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.

历史上有很多这样的情况，即同一事件被不同的群体完全不同的解释。通过OJ辛普森案、COVID-19危机和乔治弗洛伊德被谋杀等事件，我们观察到社区领袖、警察部门、政策制定者和普通公民对事件的不同反应。该项目的目的是开始识别危机的社会、情感和语言标记（例如，社会动荡、自然灾害等）预测人们对同一事件的不同反应。这是通过分析社交媒体的语言来实现的，社交媒体是一个快速增长的数据来源，我们可以从中了解大规模的情绪表达和感知，从学术研究到公共政策，都有着深远的潜在用途。了解情绪，这些情绪的背景，以及随后的行为对那些处于危机中的人来说非常有价值，寻求危机信息，或帮助管理应对危机。该项目将发现在不同社交平台上提供全面、细粒度情感分析的机制，并推导出强大、可靠的预测模型。细粒度情感分析旨在：（1）检测文本中的情感表达并表征其强度和极性，（2）识别引起情感的触发因素，以及（3）分析情感偏差（即，人们对同一个触发器所表达的不同情绪）。这项研究将有助于在不同的危机和可推广的模型，配备了深刻的语言理解语境化的情绪分析的社交媒体上表达的情绪的注释数据集。 业界和学术界的合作伙伴将参与评估模型的能力，以工作的情况和数据源不同于那些用来开发模型。这个奖项反映了NSF的法定使命，并已被认为是值得支持的评估使用基金会的知识价值和更广泛的影响审查标准。

项目成果

Counterfactual Probing for the Influence of Affect and Specificity on Intergroup Bias

反事实探究情感和特异性对群体间偏见的影响

• 发表时间: 2023
• 期刊: Findings of the Association for Computational Linguistics: ACL 2023
• 作者: Govindarajan, Venkata Subrahmanyan;Beaver, David;Mahowald, Kyle;Li, Junyi Jessy
• 通讯作者: Li, Junyi Jessy

Political Ideology and Polarization: A Multi-dimensional Approach

• DOI: 10.18653/v1/2022.naacl-main.17
• 发表时间: 2022
• 作者: Barea M. Sinno;Bernardo Oviedo;Katherine Atwell;Malihe Alikhani;J. Li

Emotion analysis and detection during COVID-19

COVID-19 期间的情绪分析和检测

• 发表时间: 2022
• 期刊: Proceedings of the Language Resources and Evaluation Conference
• 作者: Sosea, Tiberiu;Pham, Chau;Tekle, Alexander;Caragea, Cornelia;Li, Junyi Jessy
• 通讯作者: Li, Junyi Jessy

ProtoTEx: Explaining Model Decisions with Prototype Tensors

ProtoTEX：用原型张量解释模型决策

• DOI: 10.18653/v1/2022.acl-long.213
• 发表时间: 2022
• 期刊: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics
• 作者: Das, Anubrata;Gupta, Chitrank;Kovatchev, Venelin;Lease, Matthew;Li, Junyi Jessy
• 通讯作者: Li, Junyi Jessy

CoditT5: Pretraining for Source Code and Natural Language Editing

• DOI: 10.1145/3551349.3556955
• 发表时间: 2022-08
• 期刊: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering
• 作者: Jiyang Zhang;Sheena Panthaplackel;Pengyu Nie;Junyi Jessy Li;Miloš Gligorić