Toward Label-Efficient Emotion and Sentiment Analysis

IF 23.2 1区 计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Proceedings of the IEEE Pub Date : 2023-09-15 DOI:10.1109/JPROC.2023.3309299
Sicheng Zhao;Xiaopeng Hong;Jufeng Yang;Yanyan Zhao;Guiguang Ding
{"title":"Toward Label-Efficient Emotion and Sentiment Analysis","authors":"Sicheng Zhao;Xiaopeng Hong;Jufeng Yang;Yanyan Zhao;Guiguang Ding","doi":"10.1109/JPROC.2023.3309299","DOIUrl":null,"url":null,"abstract":"Emotion and sentiment play a central role in various human activities, such as perception, decision-making, social interaction, and logical reasoning. Developing artificial emotional intelligence (AEI) for machines is becoming a bottleneck in human–computer interaction. The first step of AEI is to recognize the emotion and sentiment that are conveyed in different affective signals. Traditional supervised emotion and sentiment analysis (ESA) methods, especially deep learning-based ones, usually require large-scale labeled training data. However, due to the essential subjectivity, complexity, uncertainty and ambiguity, and subtlety, collecting such annotations is expensive, time-consuming, and difficult in practice. In this article, we introduce label-efficient ESA from the computational perspective. First, we present a hierarchical taxonomy for label-efficient learning based on the availability of sample labels, emotion categories, and data domains during training. Second, for each of the seven paradigms, i.e., unsupervised, semisupervised, weakly supervised, low-shot, incremental, domain-adaptive, and domain-generalizable ESA, we give the definition, summarize existing methods, and present our views on the quantitative and qualitative comparison. Finally, we provide several promising real-world applications, followed by unsolved challenges and potential future directions.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"111 10","pages":"1159-1197"},"PeriodicalIF":23.2000,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the IEEE","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10253654/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

Abstract

Emotion and sentiment play a central role in various human activities, such as perception, decision-making, social interaction, and logical reasoning. Developing artificial emotional intelligence (AEI) for machines is becoming a bottleneck in human–computer interaction. The first step of AEI is to recognize the emotion and sentiment that are conveyed in different affective signals. Traditional supervised emotion and sentiment analysis (ESA) methods, especially deep learning-based ones, usually require large-scale labeled training data. However, due to the essential subjectivity, complexity, uncertainty and ambiguity, and subtlety, collecting such annotations is expensive, time-consuming, and difficult in practice. In this article, we introduce label-efficient ESA from the computational perspective. First, we present a hierarchical taxonomy for label-efficient learning based on the availability of sample labels, emotion categories, and data domains during training. Second, for each of the seven paradigms, i.e., unsupervised, semisupervised, weakly supervised, low-shot, incremental, domain-adaptive, and domain-generalizable ESA, we give the definition, summarize existing methods, and present our views on the quantitative and qualitative comparison. Finally, we provide several promising real-world applications, followed by unsolved challenges and potential future directions.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
标签有效情感与情感分析
情感在人类的各种活动中起着核心作用,如感知、决策、社交和逻辑推理。为机器开发人工情感智能(AEI)正成为人机交互的瓶颈。AEI的第一步是识别不同情感信号中传达的情感和情绪。传统的监督情绪分析(ESA)方法,尤其是基于深度学习的方法,通常需要大规模的标记训练数据。然而,由于本质上的主观性、复杂性、不确定性和模糊性以及微妙性,收集此类注释在实践中是昂贵、耗时和困难的。在这篇文章中,我们从计算的角度介绍了标签有效的ESA。首先,我们基于训练过程中样本标签、情绪类别和数据域的可用性,提出了一种用于标签高效学习的分层分类法。其次,对于无监督、半监督、弱监督、低镜头、增量、领域自适应和领域可推广的ESA这七种范式中的每一种,我们给出了定义,总结了现有的方法,并提出了我们对定量和定性比较的看法。最后,我们提供了几个有前景的现实世界应用程序,以及未解决的挑战和潜在的未来方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Proceedings of the IEEE
Proceedings of the IEEE 工程技术-工程:电子与电气
CiteScore
46.40
自引率
1.00%
发文量
160
审稿时长
3-8 weeks
期刊介绍: Proceedings of the IEEE is the leading journal to provide in-depth review, survey, and tutorial coverage of the technical developments in electronics, electrical and computer engineering, and computer science. Consistently ranked as one of the top journals by Impact Factor, Article Influence Score and more, the journal serves as a trusted resource for engineers around the world.
期刊最新文献
Front Cover Table of Contents IEEE Membership Future Special Issues/Special Sections of the Proceedings TechRxiv
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1