阿拉伯推特关于新冠肺炎全球疫情的情绪分析

IF 0.5 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Areej Alshutayri, Amal Alghamdi, Nouran Nassibi, N. Aljojo, Eman A. Aldhahri, O. Aboulola
{"title":"阿拉伯推特关于新冠肺炎全球疫情的情绪分析","authors":"Areej Alshutayri, Amal Alghamdi, Nouran Nassibi, N. Aljojo, Eman A. Aldhahri, O. Aboulola","doi":"10.33436/v32i2y202210","DOIUrl":null,"url":null,"abstract":"The purpose of this article was to highlight the sentiment analysis for specific Arabic tweets related to the COVID-19 Worldwide Epidemic. The technique proposed in this paper focused on using the machine learning algorithm with the purpose of applying sentiment analysis on a dataset which contained 4,575 Arabic tweets on the COVID-19 pandemic while also employing the Logistic Regression and Naive Bayes algorithms as classifiers for comparing the achieved results between them. This study showed the suitability and efficiency of a system using machine learning models for the analysis of Arabic tweets. The experimental outcomes revealed that the highest accuracy was reached by employing the Logistic Regression algorithm\", namely, 97%\". Twitter is one of the most widely used gateways of social media for the people who want to express their opinions and emotions. This study contributes to highlighting the task of sentiment analysis for the Arabic tweets about the COVID-19 pandemic by predicting the people's awareness about the Coronavirus in the Arab World.","PeriodicalId":53877,"journal":{"name":"Romanian Journal of Information Technology and Automatic Control-Revista Romana de Informatica si Automatica","volume":null,"pages":null},"PeriodicalIF":0.5000,"publicationDate":"2022-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Sentiment analysis for Arabic tweet about the COVID-19 Worldwide Epidemic\",\"authors\":\"Areej Alshutayri, Amal Alghamdi, Nouran Nassibi, N. Aljojo, Eman A. Aldhahri, O. Aboulola\",\"doi\":\"10.33436/v32i2y202210\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The purpose of this article was to highlight the sentiment analysis for specific Arabic tweets related to the COVID-19 Worldwide Epidemic. The technique proposed in this paper focused on using the machine learning algorithm with the purpose of applying sentiment analysis on a dataset which contained 4,575 Arabic tweets on the COVID-19 pandemic while also employing the Logistic Regression and Naive Bayes algorithms as classifiers for comparing the achieved results between them. This study showed the suitability and efficiency of a system using machine learning models for the analysis of Arabic tweets. The experimental outcomes revealed that the highest accuracy was reached by employing the Logistic Regression algorithm\\\", namely, 97%\\\". Twitter is one of the most widely used gateways of social media for the people who want to express their opinions and emotions. This study contributes to highlighting the task of sentiment analysis for the Arabic tweets about the COVID-19 pandemic by predicting the people's awareness about the Coronavirus in the Arab World.\",\"PeriodicalId\":53877,\"journal\":{\"name\":\"Romanian Journal of Information Technology and Automatic Control-Revista Romana de Informatica si Automatica\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.5000,\"publicationDate\":\"2022-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Romanian Journal of Information Technology and Automatic Control-Revista Romana de Informatica si Automatica\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.33436/v32i2y202210\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Romanian Journal of Information Technology and Automatic Control-Revista Romana de Informatica si Automatica","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.33436/v32i2y202210","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

摘要

本文的目的是强调对与新冠肺炎全球流行相关的特定阿拉伯语推文的情绪分析。本文提出的技术侧重于使用机器学习算法,目的是对包含4575条关于新冠肺炎大流行的阿拉伯语推文的数据集应用情绪分析,同时还使用逻辑回归和朴素贝叶斯算法作为分类器,以比较它们之间取得的结果。这项研究表明了使用机器学习模型分析阿拉伯语推文的系统的适用性和效率。实验结果表明,采用Logistic回归算法达到了最高的准确率“,即97%”。推特是社交媒体中使用最广泛的门户之一,适合那些想表达自己观点和情绪的人。这项研究通过预测阿拉伯世界人们对冠状病毒的认识,有助于突出阿拉伯推文对新冠肺炎大流行的情绪分析任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sentiment analysis for Arabic tweet about the COVID-19 Worldwide Epidemic
The purpose of this article was to highlight the sentiment analysis for specific Arabic tweets related to the COVID-19 Worldwide Epidemic. The technique proposed in this paper focused on using the machine learning algorithm with the purpose of applying sentiment analysis on a dataset which contained 4,575 Arabic tweets on the COVID-19 pandemic while also employing the Logistic Regression and Naive Bayes algorithms as classifiers for comparing the achieved results between them. This study showed the suitability and efficiency of a system using machine learning models for the analysis of Arabic tweets. The experimental outcomes revealed that the highest accuracy was reached by employing the Logistic Regression algorithm", namely, 97%". Twitter is one of the most widely used gateways of social media for the people who want to express their opinions and emotions. This study contributes to highlighting the task of sentiment analysis for the Arabic tweets about the COVID-19 pandemic by predicting the people's awareness about the Coronavirus in the Arab World.
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
60.00%
发文量
32
审稿时长
4 weeks
×
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