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Sentimental Analysis of COVID-19 Tweets Using Deep Learning Models.

Nalini Chintalapudi1, Gopi Battineni1, Francesco Amenta1,2

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Summary

Analyzing Indian tweets during COVID-19 lockdown revealed significant public sentiment. The Bidirectional Encoder Representations from Transformers (BERT) model accurately classified emotions like fear and joy, outperforming other machine learning models.

Keywords:
BERTCOVID-19lockdownsentimental analysisword cloud

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Area of Science:

  • Social Sciences
  • Computer Science
  • Public Health

Background:

  • The COVID-19 pandemic heightened global attention and social media discourse.
  • Misinformation on platforms like Twitter contributed to public anxiety surrounding the disease.
  • Understanding public sentiment during the pandemic is crucial for public health initiatives.

Purpose of the Study:

  • To analyze the sentiments expressed in tweets by Indian users during the COVID-19 lockdown.
  • To evaluate the performance of the Bidirectional Encoder Representations from Transformers (BERT) model in classifying tweet sentiments.
  • To compare BERT's performance against traditional machine learning models for text analysis.

Main Methods:

  • Collected and analyzed tweets from Indian netizens between March 23 and July 15, 2020.
  • Labeled tweet text with sentiments: fear, sad, anger, and joy.
  • Employed the Bidirectional Encoder Representations from Transformers (BERT) deep-learning model for sentiment analysis.
  • Compared BERT's accuracy with Logistic Regression (LR), Support Vector Machines (SVM), and Long-Short Term Memory (LSTM) models.

Main Results:

  • The BERT model achieved 89% accuracy in sentiment classification.
  • Comparative models (LR, SVM, LSTM) showed lower accuracies at 75%, 74.75%, and 65%, respectively.
  • Sentiment classification accuracy ranged from 75.88% to 87.33%, with a median accuracy of 79.34% for BERT.
  • Identified a high prevalence of specific keywords and associated terms in Indian tweets during the COVID-19 period.

Conclusions:

  • The BERT model demonstrates superior performance for analyzing public sentiment in social media data related to pandemics.
  • Findings offer insights into public opinion and emotional responses during the COVID-19 pandemic in India.
  • This research can inform public health authorities for targeted interventions and improved societal well-being.