Worldwide COVID-19 Vaccines Sentiment Analysis Through Twitter Content

被引:30
|
作者
Ansari, Md Tarique Jamal [1 ]
Khan, Naseem Ahmad [2 ]
机构
[1] Babasaheb Bhimrao Ambedkar Univ, Dept Informat Technol, Lucknow, Uttar Pradesh, India
[2] Babasaheb Bhimrao Ambedkar Univ, Univ Inst Engn & Technol, Lucknow, Uttar Pradesh, India
来源
关键词
COVID-19; vaccination; SARS-CoV-2; vaccination refusal; NLP; Twitter;
D O I
10.29333/ejgm/11316
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
One year during the pandemic of COVID 19, numerous viable possibilities have been created in worldwide efforts to create and disseminate a viable vaccine. The rapid development of numerous vaccinations is remarkable; generally, the procedure takes 8 to 15 years. The vaccination of a critical proportion of the global population, which is vital for containing the pandemic, is now facing a new set of hurdles, including hazardous new strains of the virus, worldwide competition over a shortage of doses, as well as public suspicion about the vaccinations. A safe and efficacious vaccine COVID-19 is borne fruit globally. There are presently more than a dozen vaccinations worldwide authorized; many more continue to be developed. This paper used COVID-19 vaccine related tweets to present an overview of the public's reactions on current vaccination drives by using thematic sentiment and emotional analysis, and demographics interpretation to people. Further, experiments were carried out for sentiment analysis in order to uncover fresh information about the effect of location and gender. Overall Tweets were generally negative in tone and a huge vaccination trend can be seen in global health perspectives, as evidenced by the analysis of the role of comprehensive science and research in vaccination.
引用
收藏
页数:10
相关论文
共 50 条
  • [1] Public perception of COVID-19 vaccines through analysis of Twitter content and users
    Saleh, Sameh N.
    McDonald, Samuel A.
    Basit, Mujeeb A.
    Kumar, Sanat
    Arasaratnam, Reuben J.
    Perl, Trish M.
    Lehmann, Christoph U.
    Medford, Richard J.
    [J]. VACCINE, 2023, 41 (33) : 4844 - 4853
  • [2] Sentiment Analysis and Opinion Mining about COVID-19 vaccines of Twitter Data
    Jahanbin, Kia
    Rahmanian, Vahid
    Sharifi, Nader
    Rahmanian, Fereshteh
    [J]. PAKISTAN JOURNAL OF MEDICAL & HEALTH SCIENCES, 2021, 15 (01): : 694 - 695
  • [3] Sentiment Analysis on COVID-19 Twitter Data
    Vijay, Tanmay
    Chawla, Ayan
    Dhanka, Balan
    Karmakar, Purnendu
    [J]. 2020 5TH IEEE INTERNATIONAL CONFERENCE ON RECENT ADVANCES AND INNOVATIONS IN ENGINEERING (IEEE - ICRAIE-2020), 2020,
  • [4] Twitter Sentiment Analysis of Covid Vaccines
    Zhu, Wenbo
    Hu, Tiechuan
    [J]. 2021 5TH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND VIRTUAL REALITY, AIVR 2021, 2021, : 118 - 122
  • [5] Sentiment Analysis on COVID-19 Twitter Data: A Sentiment Timeline
    Karagkiozidou, Makrina
    Koukaras, Paraskevas
    Tjortjis, Christos
    [J]. ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS, AIAI 2022, PART II, 2022, 647 : 350 - 359
  • [6] Twitter Sentiment Analysis towards COVID-19 Vaccines in the Philippines Using Naive Bayes
    Villavicencio, Charlyn
    Macrohon, Julio Jerison
    Inbaraj, X. Alphonse
    Jeng, Jyh-Horng
    Hsieh, Jer-Guang
    [J]. INFORMATION, 2021, 12 (05)
  • [7] Twitter sentiment analysis for COVID-19 associated mucormycosis
    Singh, Maneet
    Dhillon, Hennaav Kaur
    Ichhpujani, Parul
    Iyengar, Sudarshan
    Kaur, Rishemjit
    [J]. INDIAN JOURNAL OF OPHTHALMOLOGY, 2022, 70 (05) : 1773 - +
  • [8] Twitter sentiment and stock market: a COVID-19 analysis
    Katsafados, Apostolos G.
    Nikoloutsopoulos, Sotirios
    Leledakis, George N.
    [J]. JOURNAL OF ECONOMIC STUDIES, 2023, 50 (08) : 1866 - 1888
  • [9] COVID-19 vaccines in twitter ecosystem: Analyzing perceptions and attitudes by sentiment and text analysis method
    Kahraman, Elif
    Demirel, Sadettin
    Gunduz, Ugur
    [J]. JOURNAL OF PUBLIC HEALTH-HEIDELBERG, 2023,
  • [10] TSM-CV: Twitter Sentiment Analysis for COVID-19 Vaccines Using Deep Learning
    Albahli, Saleh
    Nawaz, Marriam
    [J]. ELECTRONICS, 2023, 12 (15)