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COVIDHealth: A novel labeled dataset and machine learning-based web application for classifying COVID-19 discourses
Mahathir Mohammad Bishal1, Md Rakibul Hassan Chowdory1, Anik Das2
1Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chattogram, 4349, Bangladesh.
Heliyon
|August 5, 2024
Summary
This study introduces a machine learning tool to classify COVID-19 health discussions on Twitter (now X). Convolutional Neural Networks (CNNs) achieved 90.4% accuracy, outperforming other models for public health data analysis.
Area of Science:
- Computational linguistics and public health informatics.
- Application of machine learning in analyzing social media health discourse.
Background:
- The COVID-19 pandemic generated extensive health-related conversations on social media platforms like Twitter (now X).
- A significant challenge in analyzing this data is the lack of labeled datasets for theme-based classification and aggregation.
- This hinders effective public health monitoring and response strategies.
Purpose of the Study:
- To develop an automated machine learning-based web application for classifying COVID-19 health discourses.
- To categorize tweets into five key themes: health risks, prevention, symptoms, transmission, and treatment.
- To provide a valuable tool for public health researchers and practitioners.
Main Methods:
- Collected and manually labeled 6,667 COVID-19-related tweets using the Twitter API.
- Employed various feature extraction techniques for data preprocessing.
- Compared the performance of seven classical machine learning algorithms (Decision Tree, Random Forest, SGD, Adaboost, KNN, Logistic Regression, Linear SVC) and four deep learning techniques (LSTM, CNN, RNN, BERT).
Main Results:
- The Convolutional Neural Network (CNN) model achieved the highest performance metrics: 90.41% precision, 90.4% recall, 90.4% F1 score, and 90.4% accuracy.
- Among classical machine learning algorithms, Linear Support Vector Classification (Linear SVC) demonstrated the best performance with 85.71% precision, 86.94% recall, and 86.13% F1 score.
- The developed web application provides a functional and accessible platform for analyzing COVID-19 health data.
Conclusions:
- Machine learning, particularly deep learning models like CNNs, can effectively classify and categorize large volumes of social media data related to public health crises.
- The study provides a validated dataset and a practical web tool to aid in understanding and addressing public health challenges during pandemics.
- The findings contribute to advancing health-related data analysis and classification methods for improved public health awareness and response.
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