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BERT-Based Deep Learning Models for Analyzing Sentiments of COVID-19-Related Social Media Tweets
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Author(s): N. Manikandan (The New College, India), Gnaneswari Gnanaguru (CMR Institute of Technology, India), V. Viswapriya (Vels Institute of Science, Technology, and Advanced Studies, India), S. Silvia Priscila (Bharath Institute of Higher Education and Research, India), Prasanna Ranjith Christodoss (Messiah University, USA)and S. Saranya (Dhaanish Ahmed College of Engineering, India)
Copyright: 2025
Pages: 16
Source title:
Multidisciplinary Approaches to AI, Data, and Innovation for a Smarter World
Source Author(s)/Editor(s): Sonia Singh (Toss Global Management, UK), Slim Hadoussa (Brest Business School, France), Thangaraja Arumugam (Vellore Institute of Technology, Chennai, India)and S. Suman Rajest (Dhaanish Ahmed College of Engineering, India)
DOI: 10.4018/979-8-3693-9375-8.ch002
PurchaseView on the publisher's website for pricing and purchasing information.
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Abstract
Social media data has become an important tool for understanding public attitudes. All over the world, the COVID-19 pandemic impacted people's lives in various ways. People worldwide utilize social media to express their thoughts and feelings about the pandemic. Because of the diversity of Twitter posts, researchers analyze sentiment and examine the public's numerous sentiments concerning COVID-19. In the meantime, people have shared their thoughts about immunization protection and efficacy on social media sites such as Twitter. Studies have demonstrated that it may strengthen ideas and impact the general opinion. This study focuses on analyzing the sentiment of Twitter data connected to the COVID-19 pandemic using bidirectional encoder representations from transformers (BERT) with random forest (RF), convolutional neural networks (CNN), and recurrent CNN (RCNN) classifiers.
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