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Semantic Analysis and Topic Modelling of Web-Scrapped COVID-19 Tweet Corpora through Data Mining Methodologies.

Mahendra Kumar Gourisaria1, Satish Chandra1, Himansu Das1

  • 1School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar 751024, Odisha, India.

Healthcare (Basel, Switzerland)
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Summary
This summary is machine-generated.

This study analyzed COVID-19 public sentiment on Twitter using topic modeling and sentiment analysis. The Bidirectional Long Short-Term Memory (BiLSTM) model achieved 96.7% accuracy in classifying tweet polarity, identifying psychological reactions during the pandemic.

Keywords:
BiLSTMCOVID-19 sentiment analysisLatent Dirichlet Allocation (LDA)natural language processingtopic modeling

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Area of Science:

  • Social Sciences
  • Computer Science
  • Public Health

Background:

  • The COVID-19 pandemic significantly impacted global social, economic, and psychological well-being.
  • Twitter emerged as a key platform for public discourse and sentiment expression regarding COVID-19 and related measures.
  • Understanding public psychological reactions is crucial for effective public health communication and policy.

Purpose of the Study:

  • To analyze the psychological reactions and discourse of Twitter users concerning COVID-19.
  • To compare the effectiveness of various machine learning models for sentiment analysis of pandemic-related tweets.
  • To identify the most accurate model for classifying tweet polarity and understanding public sentiment.

Main Methods:

  • Latent Dirichlet Allocation (LDA) for topic modeling of tweets.
  • Bidirectional Long Short-Term Memory (BiLSTM) and other classifiers (Random Forest, SVM, Logistic Regression, Naive Bayes, Decision Tree, SGD, Voting) for sentiment polarity analysis.
  • Dual dataset approach incorporating word clouds for enhanced analysis and model validation.

Main Results:

  • The Bidirectional Long Short-Term Memory (BiLSTM) model demonstrated superior performance in sentiment analysis.
  • BiLSTM achieved a high accuracy of 96.7% in classifying the polarity of COVID-19 related tweets.
  • LDA effectively identified key topics within the public discourse on Twitter.

Conclusions:

  • Machine learning, particularly BiLSTM, offers a robust method for analyzing public sentiment and psychological responses to health crises like COVID-19.
  • The findings highlight the utility of social media data in understanding public perception during pandemics.
  • Accurate sentiment analysis can inform public health strategies and interventions.