Prediction of Mental Health Risk Using Sentiment Analysis and Long Short-Term Memory (LSTM) Network

Traditional mental health assessments rely on questionnaires and interviews, which require manual data collection and cannot support real-time monitoring or early warning, especially for individuals with significant mood fluctuations. This paper proposes a model that combines sentiment analysis with LSTM networks to monitor emotional states in real time and capture temporal dependencies. The model collects and cleans user text data from multiple platforms and electronic health records, then applies natural language processing techniques such as word segmentation, vectorization, and sentiment analysis. Sentiment features are extracted using SentiWordNet and Bidirectional Encoder Representations from Transformers (BERT), and further classified with a convolutional neural network. The resulting sentiment scores are arranged chronologically and fed into the LSTM model to learn long-term patterns. After cross-validation and optimization, the model achieved 92% accuracy in predicting mental health risks, with an AUC between 0.9 and 0.95, demonstrating strong performance for real-time mental health risk prediction.