Complex Human Behavior Laboratory, Fondazione Bruno Kessler, Trento, Italy.
Northeastern University, London, UK.
Sci Rep. 2024 Aug 14;14(1):18902. doi: 10.1038/s41598-024-69687-8.
The COVID-19 pandemic has disrupted people's lives and caused significant economic damage around the world, but its impact on people's mental health has not been paid due attention by the research community. According to anecdotal data, the pandemic has raised serious concerns related to mental health among the masses. However, no systematic investigations have been conducted previously on mental health monitoring and, in particular, detection of post-traumatic stress disorder (PTSD). The goal of this study is to use classical machine learning approaches to classify tweets into COVID-PTSD positive or negative categories. To this end, we employed various Machine Learning (ML) classifiers, to segregate the psychotic difficulties with the user's PTSD in the context of COVID-19, including Random Forest Support Vector Machine, Naïve Bayes, and K-Nearest Neighbor. ML models are trained and tested using various combinations of feature selection strategies to get the best possible combination. Based on our experimentation on real-world dataset, we demonstrate our model's effectiveness to perform classification with an accuracy of 83.29% using Support Vector Machine as classifier and unigram as a feature pattern.
新冠疫情大流行扰乱了人们的生活，给全世界造成了巨大的经济损失，但研究界没有充分关注其对人们心理健康的影响。根据传闻数据，疫情引起了公众对心理健康的严重关切。然而，以前没有对心理健康监测进行系统调查，特别是没有对创伤后应激障碍（PTSD）进行检测。本研究旨在使用经典机器学习方法将推文分为 COVID-PTSD 阳性或阴性类别。为此，我们采用了各种机器学习（ML）分类器，将与 COVID-19 背景下用户 PTSD 相关的精神障碍进行分类，包括随机森林支持向量机、朴素贝叶斯和 K-最近邻。使用各种特征选择策略的组合来训练和测试 ML 模型，以获得最佳的组合。基于我们对真实数据集的实验，我们展示了我们的模型使用支持向量机作为分类器和一元模型作为特征模式进行分类的有效性，准确率为 83.29%。
