User Stress Detection Using Social Media Text: A Novel Machine Learning Approach.

This paper introduces a novel Attention-based Bidirectional Long Short-Term Memory (BiLSTM) model for detecting stress in social media text, aiming to enhance mental health monitoring in the digital age. Utilizing the unique communicative nature of social media, this study employs user-generated content to analyze emotional and stress levels. The proposed model incorporates an attention mechanism with the Bi-LSTM architecture to improve the identification of temporal features and context relationships in text data, which is crucial for detecting stress indicators. This model stands out by dynamically focusing on text segments that significantly denote stress, thereby boosting the detection sensitivity and accuracy. Through rigorous testing against baseline models such as Text-CNN, LSTM, GRU, and standard Bi-LSTM, our method demonstrates superior performance, achieving the highest F1-score of 81.21%. These results underscore its potential for practical applications in mental health monitoring where accurate and timely detection of stress is essential. [ABSTRACT FROM AUTHOR]