利用社交关系进行微博情感分析

Xia Hu, Lei Tang, Jiliang Tang, Huan Liu
{"title":"利用社交关系进行微博情感分析","authors":"Xia Hu, Lei Tang, Jiliang Tang, Huan Liu","doi":"10.1145/2433396.2433465","DOIUrl":null,"url":null,"abstract":"Microblogging, like Twitter and Sina Weibo, has become a popular platform of human expressions, through which users can easily produce content on breaking news, public events, or products. The massive amount of microblogging data is a useful and timely source that carries mass sentiment and opinions on various topics. Existing sentiment analysis approaches often assume that texts are independent and identically distributed (i.i.d.), usually focusing on building a sophisticated feature space to handle noisy and short texts, without taking advantage of the fact that the microblogs are networked data. Inspired by the social sciences findings that sentiment consistency and emotional contagion are observed in social networks, we investigate whether social relations can help sentiment analysis by proposing a Sociological Approach to handling Noisy and short Texts (SANT) for sentiment classification. In particular, we present a mathematical optimization formulation that incorporates the sentiment consistency and emotional contagion theories into the supervised learning process; and utilize sparse learning to tackle noisy texts in microblogging. An empirical study of two real-world Twitter datasets shows the superior performance of our framework in handling noisy and short tweets.","PeriodicalId":324799,"journal":{"name":"Proceedings of the sixth ACM international conference on Web search and data mining","volume":"2022 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"392","resultStr":"{\"title\":\"Exploiting social relations for sentiment analysis in microblogging\",\"authors\":\"Xia Hu, Lei Tang, Jiliang Tang, Huan Liu\",\"doi\":\"10.1145/2433396.2433465\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Microblogging, like Twitter and Sina Weibo, has become a popular platform of human expressions, through which users can easily produce content on breaking news, public events, or products. The massive amount of microblogging data is a useful and timely source that carries mass sentiment and opinions on various topics. Existing sentiment analysis approaches often assume that texts are independent and identically distributed (i.i.d.), usually focusing on building a sophisticated feature space to handle noisy and short texts, without taking advantage of the fact that the microblogs are networked data. Inspired by the social sciences findings that sentiment consistency and emotional contagion are observed in social networks, we investigate whether social relations can help sentiment analysis by proposing a Sociological Approach to handling Noisy and short Texts (SANT) for sentiment classification. In particular, we present a mathematical optimization formulation that incorporates the sentiment consistency and emotional contagion theories into the supervised learning process; and utilize sparse learning to tackle noisy texts in microblogging. An empirical study of two real-world Twitter datasets shows the superior performance of our framework in handling noisy and short tweets.\",\"PeriodicalId\":324799,\"journal\":{\"name\":\"Proceedings of the sixth ACM international conference on Web search and data mining\",\"volume\":\"2022 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-02-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"392\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the sixth ACM international conference on Web search and data mining\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2433396.2433465\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the sixth ACM international conference on Web search and data mining","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2433396.2433465","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 392

摘要

微博,如推特和新浪微博,已经成为一个流行的人类表达平台,用户可以通过它轻松地发布关于突发新闻、公共事件或产品的内容。海量的微博数据是一个有用且及时的来源,它承载了各种主题的大众情绪和观点。现有的情感分析方法通常假设文本是独立和同分布的(i.i.d),通常侧重于构建复杂的特征空间来处理嘈杂和简短的文本,而没有利用微博是网络数据的事实。受社会科学研究结果的启发,情绪一致性和情绪传染在社交网络中被观察到,我们通过提出一种社会学方法来处理嘈杂和短文本(SANT)进行情绪分类,研究社会关系是否可以帮助情绪分析。特别地,我们提出了一个数学优化公式,将情绪一致性和情绪传染理论纳入监督学习过程;并利用稀疏学习来处理微博中的噪声文本。对两个真实Twitter数据集的实证研究表明,我们的框架在处理有噪声和短推文方面表现出色。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploiting social relations for sentiment analysis in microblogging
Microblogging, like Twitter and Sina Weibo, has become a popular platform of human expressions, through which users can easily produce content on breaking news, public events, or products. The massive amount of microblogging data is a useful and timely source that carries mass sentiment and opinions on various topics. Existing sentiment analysis approaches often assume that texts are independent and identically distributed (i.i.d.), usually focusing on building a sophisticated feature space to handle noisy and short texts, without taking advantage of the fact that the microblogs are networked data. Inspired by the social sciences findings that sentiment consistency and emotional contagion are observed in social networks, we investigate whether social relations can help sentiment analysis by proposing a Sociological Approach to handling Noisy and short Texts (SANT) for sentiment classification. In particular, we present a mathematical optimization formulation that incorporates the sentiment consistency and emotional contagion theories into the supervised learning process; and utilize sparse learning to tackle noisy texts in microblogging. An empirical study of two real-world Twitter datasets shows the superior performance of our framework in handling noisy and short tweets.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信