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

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.

Automated summary

Social media serves as a platform for communication, content sharing, and relationship building. This study utilizes text messages on Twitter to analyze the sentiments of Indian citizens towards the COVID-19 pandemic and vaccination drive. Deep learning and lexicon-based techniques are employed to classify the sentiments. The developed models, utilizing Bi-LSTM and GRU techniques, achieve high accuracies of 92.70% and 91.24% for the COVID-19 dataset, and 92.48% and 93.03% for the vaccination tweets classification. These models can provide valuable insights for healthcare workers and policymakers to make informed decisions during future pandemic outbreaks.