Sentiment Analysis of COVID-19 Tweets Using Deep Learning and Lexicon-Based Approaches

Bharati Sanjay Ainapure, Reshma Nitin Pise, Prathiba Reddy, Bhargav Appasani, Avireni Srinivasulu, Mohammad S. Khan and Nicu Bizon ()
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Bharati Sanjay Ainapure: Department of Computer Engineering, Faculty of Science and Technology, Vishwakarma University, Pune 411056, Maharashtra, India
Reshma Nitin Pise: Department of Computer Engineering, Faculty of Science and Technology, Vishwakarma University, Pune 411056, Maharashtra, India
Prathiba Reddy: Department of Electronics and Telecommunication Engineering, G. H. Raisoni College of Engineering and Management, Pune 412207, Maharashtra, India
Bhargav Appasani: School of Electronics Engineering, Kalinga Institute of Industrial Technology, Patia 751024, Bhubaneswar, India
Avireni Srinivasulu: Department of Electronics & Communication Engineering, Mohan Babu University, Tirupati 517102, Andhra Pradesh, India
Mohammad S. Khan: Department of Computer & Information Sciences, East Tennessee State University, Johnson City, TN 37614, USA
Nicu Bizon: Faculty of Electronics, Communication and Computers, University of Pitesti, 110040 Pitesti, Romania

Sustainability, 2023, vol. 15, issue 3, 1-21

Abstract: Social media is a platform where people communicate, share content, and build relationships. Due to the current pandemic, many people are turning to social networks such as Facebook, WhatsApp, Twitter, etc., to express their feelings. In this paper, we analyse the sentiments of Indian citizens about the COVID-19 pandemic and vaccination drive using text messages posted on the Twitter platform. The sentiments were classified using deep learning and lexicon-based techniques. A lexicon-based approach was used to classify the polarity of the tweets using the tools VADER and NRCLex. A recurrent neural network was trained using Bi-LSTM and GRU techniques, achieving 92.70% and 91.24% accuracy on the COVID-19 dataset. Accuracy values of 92.48% and 93.03% were obtained for the vaccination tweets classification with Bi-LSTM and GRU, respectively. The developed models can assist healthcare workers and policymakers to make the right decisions in the upcoming pandemic outbreaks.

Keywords: deep learning; Bi-LSTM; GRU; tweets; lexicon; sentiment analysis; social network analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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