Analyzing BTC's Trend During COVID-19 Using A Sentiment Consensus Clustering (SCC)

A. Ibrahim
{"title":"Analyzing BTC's Trend During COVID-19 Using A Sentiment Consensus Clustering (SCC)","authors":"A. Ibrahim","doi":"10.1109/iemcon53756.2021.9623182","DOIUrl":null,"url":null,"abstract":"Tweets from social media can help in providing an early sign of market mood in the business sector. Opinion mining and machine learning can be used to discover the underlying sentiment. There's a link between Twitter sentiment and Bitcoin price changes in the future. Using the concept of Consensus clustering, this paper leverages Tweets collected during the COVID-19 timeframe to forecast early Bitcoin movements following the outbreak. Results from text datasets such as Twitter with various attributes, settings, and degrees show the superiority of the proposed consensus approach in predicting the BTC trend during and after the COVID-19 pandemic.","PeriodicalId":272590,"journal":{"name":"2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/iemcon53756.2021.9623182","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Tweets from social media can help in providing an early sign of market mood in the business sector. Opinion mining and machine learning can be used to discover the underlying sentiment. There's a link between Twitter sentiment and Bitcoin price changes in the future. Using the concept of Consensus clustering, this paper leverages Tweets collected during the COVID-19 timeframe to forecast early Bitcoin movements following the outbreak. Results from text datasets such as Twitter with various attributes, settings, and degrees show the superiority of the proposed consensus approach in predicting the BTC trend during and after the COVID-19 pandemic.
基于情绪共识聚类(SCC)的COVID-19期间BTC走势分析
来自社交媒体的推文可以帮助提供商业领域市场情绪的早期迹象。意见挖掘和机器学习可以用来发现潜在的情绪。推特人气与未来比特币价格变化之间存在联系。本文利用共识聚类的概念,利用在COVID-19时间框架内收集的推文来预测疫情爆发后比特币的早期走势。来自Twitter等具有不同属性、设置和程度的文本数据集的结果表明,所提出的共识方法在预测COVID-19大流行期间和之后的比特币趋势方面具有优势。
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 求助全文
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:481959085
Book学术官方微信