Twitter Sentiment Analysis: The Good the Bad and the OMG!

Efthymios Kouloumpis, Theresa Wilson, Johanna D. Moore
{"title":"Twitter Sentiment Analysis: The Good the Bad and the OMG!","authors":"Efthymios Kouloumpis, Theresa Wilson, Johanna D. Moore","doi":"10.1609/icwsm.v5i1.14185","DOIUrl":null,"url":null,"abstract":"\n \n In this paper, we investigate the utility of linguistic features for detecting the sentiment of Twitter messages. We evaluate the usefulness of existing lexical resources as well as features that capture information about the informal and creative language used in microblogging. We take a supervied approach to the problem, but leverage existing hashtags in the Twitter data for building training data.\n \n","PeriodicalId":338112,"journal":{"name":"Proceedings of the International AAAI Conference on Web and Social Media","volume":"56 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1332","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the International AAAI Conference on Web and Social Media","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1609/icwsm.v5i1.14185","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1332

Abstract

In this paper, we investigate the utility of linguistic features for detecting the sentiment of Twitter messages. We evaluate the usefulness of existing lexical resources as well as features that capture information about the informal and creative language used in microblogging. We take a supervied approach to the problem, but leverage existing hashtags in the Twitter data for building training data.
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
推特情绪分析:好的,坏的,OMG!
在本文中,我们研究了语言特征在Twitter消息情感检测中的效用。我们评估了现有词汇资源的有用性,以及捕捉微博中使用的非正式和创造性语言信息的功能。我们采用监督的方法来解决这个问题,但利用Twitter数据中的现有标签来构建训练数据。
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
自引率
0.00%
发文量
0
期刊最新文献
Statement of Removal AnnoBERT: Effectively Representing Multiple Annotators’ Label Choices to Improve Hate Speech Detection Just Another Day on Twitter: A Complete 24 Hours of Twitter Data #RoeOverturned: Twitter Dataset on the Abortion Rights Controversy SexWEs: Domain-Aware Word Embeddings via Cross-Lingual Semantic Specialisation for Chinese Sexism Detection in Social Media
×
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