RI: Medium: Bringing Sentiment Analysis and Social Network Analysis Together

RI：媒介：将情感分析和社交网络分析结合在一起

• 批准号: 1159679
• 负责人: Christopher Potts
• 金额: $ 100.63万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-06-01 至 2017-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1159679&HistoricalAwards=false
• 关键词: RI; Medium; Bringing; Sentiment; Analysis

Recent years have seen extraordinary breakthroughs in computational sentiment analysis and social network analysis. This research has helped to reveal that robust language understanding --- in dialogue, in text, on the Web --- depends on accurately identifying attitudes, emotions, and social relationships. However, missing from the current scientific and technical picture is a deep understanding of the ways in which sentiment affects, and is affected by, our interpersonal relationships and social networks. The central goal of this project is to fill this gap by developing algorithms, methods, and data sets for modeling sentiment as social and interpersonal. The approach balances fine-grained sociological and linguistic study, computational modeling, and large-scale data-mining of weblogs and other interactive social media.This research aims to reshape the field of sentiment analysis by moving it towards inferential models of emotional language as situated in specific social contexts. It also provides social network analysis with rich features for characterizing social ties, thereby facilitating the identification of new latent structure in social networks. The work addresses a wide variety of social and political issues, including the extent of media polarization, the effects of bias and framing, and the role of emotional content in shaping the flow of information. It can also inform the creation of the next generation of communication tools --- software for virtual meetings and online collaboration that intelligently tracks users' evolving attitudes, social networking platforms that distinguish relationship types, and data-mining tools relevant for legal discovery, intelligent tutoring, and media analytics.

近年来，在计算情感分析和社会网络分析方面取得了非凡的突破。这项研究揭示了强有力的语言理解——在对话、文本和网络中——依赖于对态度、情感和社会关系的准确识别。然而，在当前的科学和技术图景中，缺少对情感影响我们的人际关系和社会网络的方式以及被情感影响的方式的深刻理解。该项目的中心目标是通过开发算法、方法和数据集来填补这一空白，将情感建模为社会和人际关系。该方法平衡了细粒度的社会学和语言学研究、计算建模以及网络日志和其他交互式社会媒体的大规模数据挖掘。本研究旨在重塑情感分析领域，将其推向特定社会背景下的情感语言推理模型。它还为社会网络分析提供了丰富的特征来表征社会关系，从而有助于识别社会网络中新的潜在结构。这项工作涉及各种各样的社会和政治问题，包括媒体两极分化的程度，偏见和框架的影响，以及情感内容在塑造信息流中的作用。它还可以为下一代通信工具的创建提供信息——用于虚拟会议和在线协作的软件，可以智能地跟踪用户不断变化的态度，区分关系类型的社交网络平台，以及与法律发现、智能辅导和媒体分析相关的数据挖掘工具。