Obagbuwa Ibidun Christiana, Danster Samantha, Chibaya Onil Colin
Department of Computer Science and Information Technology, School of Natural and Applied Sciences, Sol Plaatje University, Kimberley, South Africa.
Front Artif Intell. 2023 Jul 19;6:1230649. doi: 10.3389/frai.2023.1230649. eCollection 2023.
Globally, the prevalence of mental health problems, especially depression, is at an all-time high. The objective of this study is to utilize machine learning models and sentiment analysis techniques to predict the level of depression earlier in social media users' posts.
The datasets used in this research were obtained from Twitter posts. Four machine learning models, namely extreme gradient boost (XGB) Classifier, Random Forest, Logistic Regression, and support vector machine (SVM), were employed for the prediction task.
The SVM and Logistic Regression models yielded the most accurate results when applied to the provided datasets. However, the Logistic Regression model exhibited a slightly higher level of accuracy compared to SVM. Importantly, the logistic regression model demonstrated the advantage of requiring less execution time.
The findings of this study highlight the potential of utilizing machine learning models and sentiment analysis techniques for early detection of depression in social media users. The effectiveness of SVM and Logistic Regression models, with Logistic Regression being more efficient in terms of execution time, suggests their suitability for practical implementation in real-world scenarios.
在全球范围内，心理健康问题，尤其是抑郁症的患病率正处于历史最高水平。本研究的目的是利用机器学习模型和情感分析技术，在社交媒体用户的帖子中更早地预测抑郁程度。
本研究中使用的数据集来自推特帖子。四个机器学习模型，即极端梯度提升（XGB）分类器、随机森林、逻辑回归和支持向量机（SVM），被用于预测任务。
当应用于所提供的数据集时，支持向量机和逻辑回归模型产生了最准确的结果。然而，与支持向量机相比，逻辑回归模型的准确率略高。重要的是，逻辑回归模型显示出执行时间更短的优势。
本研究的结果突出了利用机器学习模型和情感分析技术在社交媒体用户中早期检测抑郁症的潜力。支持向量机和逻辑回归模型的有效性，其中逻辑回归在执行时间方面更高效，表明它们适用于现实世界场景中的实际应用。
