COVID-19大流行在Twitter(现命名为“X”)等社交媒体平台上引发了广泛的健康相关讨论。然而,缺乏标记的Twitter数据对基于主题的分类和推文聚合提出了重大挑战。为了解决这个差距,我们开发了一个基于机器学习的网络应用程序,自动将COVID-19的话语分为五类:健康风险,预防,症状,传输,和治疗。我们使用TwitterAPI收集并标记了6,667条与COVID-19相关的推文,并应用各种特征提取方法提取相关特征。然后,我们比较了七种经典机器学习算法的性能(决策树,随机森林,随机梯度下降,Adaboost,K-最近的邻居,Logistic回归,和线性SVC)和四种深度学习技术(LSTM,CNN,RNN,和BERT)进行分类。我们的结果表明,CNN达到了最高的精度(90.41%),召回(90.4%),F1得分(90.4%),和准确性(90.4%)。线性SVC算法表现出最高的精度(85.71%),召回(86.94%),在经典机器学习方法中,F1得分(86.13%)。我们的研究推进了健康相关数据分析和分类领域,并为公共卫生研究人员和从业人员提供可公开访问的基于网络的工具。该工具有可能支持应对大流行期间的公共卫生挑战和提高认识。数据集和应用程序可在https://github.com/Bishal16/COVID19-Health-Related-Data-Classification-Website访问。
The COVID-19 pandemic has sparked widespread health-related discussions on social media platforms like Twitter (now named \'X\'). However, the lack of labeled Twitter data poses significant challenges for theme-based classification and tweet aggregation. To address this gap, we developed a machine learning-based web application that automatically classifies COVID-19 discourses into five categories: health risks, prevention, symptoms, transmission, and treatment. We collected and labeled 6,667 COVID-19-related tweets using the Twitter API, and applied various feature extraction methods to extract relevant features. We then compared the performance of seven classical machine learning algorithms (Decision Tree, Random Forest, Stochastic Gradient Descent, Adaboost, K-Nearest Neighbor, Logistic Regression, and Linear SVC) and four deep learning techniques (LSTM, CNN, RNN, and BERT) for classification. Our results show that the CNN achieved the highest precision (90.41%), recall (90.4%), F1 score (90.4%), and accuracy (90.4%). The Linear SVC algorithm exhibited the highest precision (85.71%), recall (86.94%), and F1 score (86.13%) among classical machine learning approaches. Our study advances the field of health-related data analysis and classification, and offers a publicly accessible web-based tool for public health researchers and practitioners. This tool has the potential to support addressing public health challenges and enhancing awareness during pandemics. The dataset and application are accessible at https://github.com/Bishal16/COVID19-Health-Related-Data-Classification-Website.