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COVID-19 Public Opinion: A Twitter Healthcare Data Processing Using Machine Learning Methodologies.

Shweta Agrawal1, Sanjiv Kumar Jain2, Shruti Sharma3

  • 1Institute of Advanced Computing, SAGE University, Indore 452010, India.

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This study analyzed public sentiment on COVID-19 using machine learning models trained on Twitter data. The Support Vector Machine model achieved the highest accuracy in classifying healthcare-related sentiments.

Keywords:
accuracyartificial intelligenceclassificationhealthcaremachine learningpolaritysentiment analysissocial media

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Area of Science:

  • Computational Social Science
  • Public Health Informatics
  • Natural Language Processing

Background:

  • The COVID-19 pandemic generated vast amounts of public sentiment on social media.
  • Analyzing this data is crucial for understanding public opinion on health issues.
  • Twitter data offers a rich source for sentiment analysis in the healthcare sector.

Purpose of the Study:

  • To analyze machine learning models for sentiment classification of public opinion on COVID-19.
  • To determine the polarity of sentiments regarding vaccines, post-COVID-19 health issues, and healthcare providers.
  • To inform policy and strategic decisions for authorities.

Main Methods:

  • Utilized Twitter API data focusing on the healthcare sector.
  • Developed and compared various machine learning models: Support Vector Machine (SVM), Logistic Regression, Random Forest, Multinomial Naive Bayes, and Long Short-Term Memory (LSTM).
  • Evaluated models based on classification accuracy for sentiment polarity.

Main Results:

  • The Support Vector Machine (SVM) model demonstrated superior performance with an average accuracy of 82.67%.
  • Other models achieved accuracies including Logistic Regression (78%), Random Forest (77%), LSTM (75%), and Multinomial Naive Bayes (68.67%).
  • Identified dominant public sentiments regarding key healthcare sub-domains.

Conclusions:

  • Machine learning, particularly SVM, is effective for analyzing public sentiment on health topics from social media.
  • Sentiment analysis provides valuable insights for healthcare policy and decision-making.
  • Understanding public views on vaccines and healthcare services is essential during a pandemic.