  The COVID-19 pandemic has had adverse effects on both physical and mental
health. During this pandemic, numerous studies have focused on gaining insights
into health-related perspectives from social media. In this study, our primary
objective is to develop a machine learning-based web application for
automatically classifying COVID-19-related discussions on social media. To
achieve this, we label COVID-19-related Twitter data, provide benchmark
classification results, and develop a web application. We collected data using
the Twitter API and labeled a total of 6,667 tweets into five different
classes: health risks, prevention, symptoms, transmission, and treatment. We
extracted features using various feature extraction methods and applied them to
seven different traditional machine learning algorithms, including Decision
Tree, Random Forest, Stochastic Gradient Descent, Adaboost, K-Nearest
Neighbour, Logistic Regression, and Linear SVC. Additionally, we used four deep
learning algorithms: LSTM, CNN, RNN, and BERT, for classification. Overall, we
achieved a maximum F1 score of 90.43% with the CNN algorithm in deep learning.
The Linear SVC algorithm exhibited the highest F1 score at 86.13%, surpassing
other traditional machine learning approaches. Our study not only contributes
to the field of health-related data analysis but also provides a valuable
resource in the form of a web-based tool for efficient data classification,
which can aid in addressing public health challenges and increasing awareness
during pandemics. We made the dataset and application publicly available, which
can be downloaded from this link
https://github.com/Bishal16/COVID19-Health-Related-Data-Classification-Website.
