Mental Health Evaluation Through Text Analysis

Assessment of a person's mental health is a complex phenomenon that affects many people around the world. This study aims to develop an accurate and efficient method to identify possible mental health concerns through text analysis, such as written messages and social media posts. This paper uses the capabilities of Amazon SageMaker Autopilot and KNIME to deliver improved model performance. This approach yields an improvement in accuracy compared to baseline models, demonstrating the potential to take advantage of cloud-based machine learning platforms and data science tools to streamline workflows. The results of the experiment demonstrate the effectiveness of the proposed approach. Specifically, XGBoost achieved the highest accuracy of 93.6%, outperforminag Linear Learner and Multilayer Perceptron.