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Vaccine sentiment analysis using BERT + NBSVM and geo-spatial approaches.

Areeba Umair¹, Elio Masciari¹, Muhammad Habib Ullah¹

¹Department of Electrical Engineering and Information Technology, University of Naples Federico II, Via Claudio 21, 80125 Naples, Campania Italy.

The Journal of Supercomputing

|June 26, 2023

Summary

Sentiment analysis of COVID-19 vaccine opinions on social media reveals public reactions. AI models like BERT+NBSVM accurately classify sentiments, informing public health strategies and vaccination center recommendations.

Keywords:

Artificial intelligence BERT BERT + NBSVM Buffering COVID vaccines NBSVM Sentiment analysis Spatial analysis Vaccine hesitancy

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Published on: November 28, 2018

Area of Science:

Computational Social Science
Natural Language Processing
Public Health Informatics

Background:

The COVID-19 pandemic necessitated rapid vaccine development and widespread adoption.
Public vaccine hesitancy emerged as a significant challenge, hindering immunization efforts.
Understanding public sentiment towards vaccines is crucial for effective public health communication.

Purpose of the Study:

To analyze public sentiment regarding COVID-19 vaccines using social media data.
To develop and evaluate an AI-driven framework for sentiment classification of vaccine-related tweets.
To provide insights for improving public understanding and informing health policies.

Main Methods:

Collected and pre-processed Twitter data related to COVID-19 vaccines.
Utilized artificial intelligence (AI) for word-cloud generation of sentiment-bearing words.
Employed a hybrid BERT + NBSVM model for classifying tweet polarity (positive, negative, neutral).

Main Results:

The BERT + NBSVM model achieved 73% accuracy for positive sentiment classification and 73% for negative.
Precision, recall, and F-measure scores demonstrated the model's effectiveness in sentiment identification.
Spatial analysis integrated with sentiment data provided recommendations for optimal vaccination center locations.

Conclusions:

AI-powered sentiment analysis of social media is effective in gauging public reactions to vaccines.
Findings can guide policymakers in designing targeted communication and vaccination strategies.
Geospatial integration enhances the practical application of sentiment analysis for public health services.