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Engineering Applications of Neural Networks, Eaaai/Eann 2022 ; 1600:517-528, 2022.
Article in English | Web of Science | ID: covidwho-2311292

ABSTRACT

During the COVID-19 pandemic many countries were forced to implement lockdowns to prevent further spread of the SARS-CoV-2, prohibiting people from face-to-face social interactions. This unprecedented circumstance led to an increase in traffic on social media platforms, one of the most popular of which is Twitter, with a diverse spectrum of users from around the world. This quality, along with the ability to use its API for research purposes, makes it a valuable resource for data collection and analysis. In this paper we aim to present the sentiments towards the COVID-19 pandemic and vaccines as it was imprinted through the users' tweets when the events were actually still in motion. For our research, we gathered the related data from Twitter and characterized the gathered tweets in two classes, positive and negative;using the BERT model, with an accuracy of 99%. Finally, we performed various time series analyses based on people's sentiment with reference to the pandemic period of 2021, the four major vaccine's companies as well as on the vaccine's technology.

2.
23rd International Conference on Engineering Applications of Neural Networks, EANN 2022 ; 1600 CCIS:517-528, 2022.
Article in English | Scopus | ID: covidwho-1919718

ABSTRACT

During the COVID-19 pandemic many countries were forced to implement lockdowns to prevent further spread of the SARS-CoV-2, prohibiting people from face-to-face social interactions. This unprecedented circumstance led to an increase in traffic on social media platforms, one of the most popular of which is Twitter, with a diverse spectrum of users from around the world. This quality, along with the ability to use its API for research purposes, makes it a valuable resource for data collection and analysis. In this paper we aim to present the sentiments towards the COVID-19 pandemic and vaccines as it was imprinted through the users’ tweets when the events were actually still in motion. For our research, we gathered the related data from Twitter and characterized the gathered tweets in two classes, positive and negative;using the BERT model, with an accuracy of 99%. Finally, we performed various time series analyses based on people’s sentiment with reference to the pandemic period of 2021, the four major vaccine’s companies as well as on the vaccine’s technology. © 2022, Springer Nature Switzerland AG.

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