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Related Experiment Video

Updated: Jan 11, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Context-based sentiment analysis using a BiGRU DistilBERT fusion model for COVID-19 tweets.

Utkarsh Sharma¹, Prateek Pandey², Shishir Kumar³

¹Department of Computer Science & Engineering, Jaypee University of Engineering and Technology, Guna, India. utkarsh_shar@yahoo.co.in.

Scientific Reports

|November 11, 2025

Summary

This study introduces a novel fusion model for analyzing COVID-19 public sentiment on Twitter. The model accurately tracks sentiment shifts during the pandemic, offering valuable insights for policymakers.

Keywords:

COVID-19 Contextual NLP Sentiment analysis Social media analytics Twitter data

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Area of Science:

Computational Social Science
Natural Language Processing
Public Health Informatics

Background:

The COVID-19 pandemic generated extensive public discourse on social media platforms like Twitter.
Understanding public sentiment during health crises is crucial for effective policymaking and crisis management.

Purpose of the Study:

To develop and evaluate a hybrid deep learning model for accurate sentiment analysis of COVID-19-related tweets.
To investigate the temporal evolution and regional variations of public sentiment during the early stages of the pandemic.

Main Methods:

A fusion model combining Bidirectional Gated Recurrent Unit (BiGRU) and DistilBERT transformer was developed.
Features from BiGRU and DistilBERT were concatenated and fed into an XGBoost meta-classifier.
The model was trained and validated on over one million English-language tweets from eight countries (January-April 2020).

Main Results:

The fusion model achieved 85.8% classification accuracy, outperforming individual models like DistilBERT (85.5%).
Public sentiment mirrored pandemic phases, with negative sentiment peaking during case/death surges and positive sentiment during recovery.
Significant regional differences in sentiment trends were observed across countries.

Conclusions:

The proposed context-infused fusion model effectively captures public sentiment dynamics during health crises.
Findings provide actionable insights for policymakers regarding social media monitoring and public communication strategies.
The study highlights the importance of considering temporal and regional factors in sentiment analysis.