Twitter Network Sentimental Analysis on Vaccination
Keywords:
LSTM, CNN, Decision Tree, Random forest Classifier, Naïve bayes, SVM Logistic Regression, Gradient Boosting classifier, Sentiment Analysis and fake news detection.Abstract
Globally, the COVID-19 pandemic has had an impact on daily life. Since the start of the pandemic, numerous research teams at significant pharmaceutical corporations and academic institutions throughout the world have been creating vaccines. Gender has an effect on vaccine responses, acceptability, and results. Additionally, the global advertising of the COVID-19 vaccine sparks a lot of conversations on social media outlets regarding the protection and effectiveness of vaccines, among other things. Twitter is viewed as one of the most popular social media sites that has been extensively used to communicate the public's thoughts on issues with the COVID-19 pandemic vaccination. However, there hasn't been enough research done to examine the analysis of the general public's view of the COVID-19 vaccine from a feminist perspective. The COVID-19 pandemic has been widely covered in social media, conventional print media, and electronic media since it first surfaced in December 2019. These sites provide data from reliable and unreliable medical sources. Additionally, the news from these mediums disseminates quickly. Spreading false information can cause anxiety, unintended exposure to medical treatments, digital marketing scams, and even lethal consequences. Therefore, it is imperative to develop a model for identifying bogus news in the news pool. The dataset employed in this work, which combines news about COVID-19 from various social media and news sources, is used for categorization.
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