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Coronavirus01:29

Coronavirus

Coronaviruses, including the severe acute respiratory syndrome coronavirus (SARS-CoV), are enveloped viruses characterized by their single-stranded, positive-sense RNA genome and helical nucleocapsid structure. The hallmark of these viruses is their club-shaped spike (S) glycoproteins that protrude from the viral envelope, facilitating attachment to host cells. Typically, coronaviruses infect the upper respiratory tract, often causing mild or asymptomatic disease. However, certain strains like...

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Deep Learning Model for COVID-19 Sentiment Analysis on Twitter.

Salvador Contreras Hernández1, María Patricia Tzili Cruz1, José Martín Espínola Sánchez1

  • 1Department of Informatics, Universidad Politécnica del Valle de México, 54910 Tultitlán Estado de México, Mexico.

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|May 25, 2023
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Summary
This summary is machine-generated.

This study analyzed Mexican Twitter sentiment during COVID-19 surges. A specialized Spanish Transformer model achieved higher precision in sentiment analysis compared to multilingual models and traditional classifiers.

Keywords:
BERTCOVID-19 TwitterSentiment analysis

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Area of Science:

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

Background:

  • Social media platforms like Twitter offer valuable insights into public sentiment during health crises.
  • The COVID-19 pandemic significantly influenced public mood, necessitating methods to gauge population-level emotional responses.
  • Mexico experienced severe COVID-19 waves, making sentiment analysis crucial for understanding public perception.

Purpose of the Study:

  • To analyze the sentiment of the Mexican population on Twitter during a major COVID-19 wave.
  • To develop and evaluate a high-precision sentiment analysis model for Spanish-language COVID-19 discussions.
  • To compare the performance of a dedicated Spanish Transformer model against multilingual models and other classifiers.

Main Methods:

  • A mixed, semi-supervised approach utilizing lexical-based data labeling.
  • Training of two Spanish-language Transformer models specifically for COVID-19 sentiment analysis.
  • Comparative analysis involving ten multilingual Transformer models and traditional classifiers (SVM, Naive Bayes, Logistic Regression, Decision Trees).

Main Results:

  • The dedicated Spanish-language Transformer model demonstrated superior precision in sentiment analysis compared to multilingual and traditional models.
  • The developed model effectively captured public sentiment regarding COVID-19 on the Mexican Twitter network.
  • Performance evaluation confirmed the efficacy of specialized language models for domain-specific sentiment analysis.

Conclusions:

  • A specialized Spanish Transformer model is highly effective for analyzing COVID-19 related public sentiment on social media.
  • This approach provides a valuable tool for monitoring public opinion during health emergencies in Spanish-speaking regions.
  • The findings underscore the importance of language-specific models in Natural Language Processing tasks for accurate social science research.