Leveraging AI and Machine Learning for Early Detection and Analysis of Mental Health Trends

Mental health has become a significant concern as many people today struggle to maintain a healthy work-life balance. Addressing the root causes of this issue is crucial since its effects are often felt in early life, such as in educational or professional contexts. The widespread effects of mental health disorders highlight the urgent need for public education and preventative measures to create environments that are healthy for all. To tackle this problem, we propose to use state-of-the-art machine learning (ML) and artificial intelligence (AI) techniques. This study compares and analyses a number of machine learning algorithms with the goal of accurately forecasting human moods, which is a critical aspect of understanding trends in mental health. A comprehensive evaluation of multiple models is conducted to determine and enhance their accuracy. By improving a transformer-based model, BERT, and researching its variations, such SieBERT, the goal of 96% accuracy rate is achieved. First, methods including LSTM, BERT, SVM, and Naive Bayes is used to train the model. Subsequently, advanced BERT variations such as SieBERT and RoBERTa are applied to optimize precision and efficiency. This study aims to improve the early detection and understanding of mental health problems in order to create the foundation for workable solutions.