Exploring Sentiment Analysis on Arabic Tweets about the COIVD-19 Vaccines

被引:0
作者
Alsabban, Wesam H. [1 ]
机构
[1] Umm Al Qura Univ, Coll Comp & Informat Syst, Al Taif Rd, Makkah Al Mukarramah, Saudi Arabia
来源
TEHNICKI GLASNIK-TECHNICAL JOURNAL | 2022年 / 16卷 / 02期
关键词
COVID-19; Sentiment Analysis; Social Media; Text Analysis; Twitter; Vaccine; TWITTER;
D O I
10.31803/tg-20220124144912
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The COVID-19 pandemic has imposed a public health crisis across the world. The global efforts lead to the development and deployment of multiple vaccines. The success of ending the COVID-19 pandemic relies on the willingness of people to get the vaccines. Social media platforms prove to be a valuable source to perform experiments on sentiment and emotion towards COVID-19 vaccination in many languages, mainly focusing on English. The people express their opinions and emotion on Twitter briefly, which can have tracked almost instantaneously. This helps the governments, public health officials, and decision-makers to understand public opinions towards vaccines. The goal of this research is to investigate public sentiment on COVID-19 vaccines. Twitter social media extracted all Arabic-language tweets mentioning seven vaccines in 7 months from 1 November 2020 to 31 May 2021. A set of Arabic sentiment lexicons were prepared to perform the sentiment analysis. The tweets' monthly average sentiment were calculated from the collected dataset and evaluated comparatively for each vaccine throughout the 11 months. Out of 5.5 million tweets that have been retrieved using the most frequent keywords and hashtags during the COVID-19 pandemic, 202,427 tweets were only considered and included in the monthly sentiment analysis. We considered tweets that mentioned only one vaccine name of the text. The distribution of tweets shows that 47.5% of the considered tweets mentioned the Pfizer vaccine. It is reported that 64% of the total tweets are non-negative while 35% are negative, with a significant difference in sentiment between the months. We observed an increase of non-negative tweets in parallel with increasing negative tweets on May 2021, reflecting the public's rising confidence towards vaccines. Lexicon-based sentiment analysis is valuable and easy to implement the technique. It can be used to track the sentiment regarding COVID-19 vaccines. The analysis of social media data benefits public health authorities by monitoring public opinions, addressing the people's concerns about vaccines, and building the confidence of individuals towards vaccines.
引用
收藏
页码:268 / 272
页数:5
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