Covid-19 Vaccine Public Opinion Analysis on Twitter Using Naive Bayes

被引:0
作者
Ibrahim, Samar [1 ]
Abdallah, Sheriff [1 ]
机构
[1] British Univ Dubai, Dubai, U Arab Emirates
来源
PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND INTELLIGENT SYSTEMS, ICETIS 2022, VOL 2 | 2023年 / 573卷
关键词
Covid-19; Vaccine; Twitter; Public Opinion; Datasets; Naive Bayes;
D O I
10.1007/978-3-031-20429-6_55
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Twitter is a viable data source for studying public opinion. The study aims to identify public opinion and sentiments toward Covid-19 vaccine and examine conversations posted on Twitter. The study examined two Datasets; one of 7500 tweets collected using RapidMiner from June 7-17, 2021, and 9865 tweets collected from Kaggle on the 3rd of January 2021. It used Naive Bayes model to classify, analyze and visualize tweets according to polarity, K-means clustering, and key tweet topics. The study showed that positive sentiments were dominant in both times; it also realized that positive polarity increased over time from January to June 2021. In addition, vaccine acceptance became more prevalent in the tweets' discussions and topics. Understanding sentiments and opinions toward Covid-19 vaccine usingTwitter is critical to supporting public health organizations to execute promotions plans and encourage positive messages towards Covid-19 to improve vaccination mitigation and vaccine intake.
引用
收藏
页码:613 / 626
页数:14
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