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Social media-based COVID-19 sentiment classification model using Bi-LSTM.

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Summary
This summary is machine-generated.

This study introduces a Bidirectional Long Short-Term Memory (Bi-LSTM) method for analyzing COVID-19 public opinions on social media. The natural language processing model effectively classifies sentiment, aiding health institutions in understanding concerns and combating misinformation.

Keywords:
Bi-LSTMCOVID-19Deep learningNatural language processingSentiment classificationSocial media

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Area of Science:

  • Computational linguistics
  • Public health informatics
  • Social media analytics

Background:

  • Social media platforms are crucial for public health information exchange, including misinformation.
  • Understanding public sentiment during health crises like COVID-19 is vital for effective response.

Purpose of the Study:

  • To develop and evaluate a natural language processing (NLP) method for sentiment classification of COVID-19 public opinions.
  • To uncover public concerns and identify misinformation patterns on social media.

Main Methods:

  • Utilized a Bidirectional Long Short-Term Memory (Bi-LSTM) neural network architecture.
  • Experimented with real-world datasets from Twitter and Reddit social media platforms.
  • Compared Bi-LSTM performance against conventional Long Short-Term Memory (LSTM) models and existing literature.

Main Results:

  • The proposed Bi-LSTM model demonstrated improved sentiment classification metrics.
  • Effectively captured both left and right contextual information for enhanced analysis.
  • Outperformed conventional LSTM models and recent studies in analyzing public opinion.

Conclusions:

  • NLP techniques, specifically Bi-LSTM, are effective for analyzing public opinion on health issues.
  • The model can assist official institutions in mitigating negative messages and addressing public concerns.
  • Findings support the use of NLP for combating misinformation and guiding public health decision-making.