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Twitter sentiment analysis: An Arabic text mining approach based on COVID-19.

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Summary
This summary is machine-generated.

This study analyzed Arabic social media data from the COVID-19 pandemic. Machine learning revealed predominantly negative public sentiment in Gulf countries, highlighting the need for sentiment analysis in public health communication.

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Area of Science:

  • Natural Language Processing
  • Public Health Communication
  • Social Media Analytics

Background:

  • Social media platforms have become primary channels for information dissemination and public discourse.
  • The COVID-19 pandemic exacerbated the spread of misinformation and public anxiety, necessitating methods to gauge public sentiment.
  • Analyzing public emotions expressed on social media is crucial for understanding societal needs and informing policy.

Purpose of the Study:

  • To develop and evaluate a sentiment analysis model for detecting genuine news related to the COVID-19 pandemic in Arabic text.
  • To analyze public sentiment towards the COVID-19 pandemic in Gulf countries using Twitter data.
  • To provide a tool for authorities to understand public emotions and inform public health strategies.

Main Methods:

  • Utilized a sentiment analysis model incorporating Machine Learning techniques.
  • Employed the Synthetic Minority Over-sampling Technique (SMOTE) to address imbalanced datasets.
  • Focused on Arabic text data from Twitter specifically from Gulf countries during the COVID-19 pandemic.

Main Results:

  • The sentiment analysis model successfully processed Arabic text data from Twitter.
  • Analysis indicated a predominantly negative public sentiment among people in Gulf countries during the COVID-19 pandemic.
  • The findings underscore the utility of sentiment-based data mining for understanding public reactions during health crises.

Conclusions:

  • Sentiment analysis of social media data provides valuable insights into public emotions during health crises like the COVID-19 pandemic.
  • Government authorities can leverage this approach to directly understand public sentiment and implement targeted interventions.
  • The developed model offers a method for monitoring and mitigating the impact of misinformation and public anxiety.