Mental Health Data Analysis using Deep learning based Methods

Social media is rapidly reflecting mental health issues, hence offering useful yet unstructured data for early detection and intervention. Based on social media material, this paper offers an AI-powered framework for multi-class sentiment analysis to address mental health issues. From several mental healthrelated sources on Kaggle, a thorough dataset of $\mathbf{5 3, 0 4 3}$ tagged entries spanning seven emotion categories-Normal, Depression, Suicidal, Anxiety, Stress, Bi-Polar, and Personality Disorder-was assembled. Extensive preprocessing designed for loud, informal social media language was applied to the data. Using different embedding approaches including TF-IDF, GloVe, FastText, and BERT, we assessed a broad spectrum of Transformer-based models, Deep Learning (DL), and Machine Learning (ML). Multi-Class Multi-Level approach is also implemented using ML and DL models. While DL models employing GRU with FastText reached 73.63% accuracy and an F1-score of 0.66, classic ML models with TF-IDF showed up to 75% accuracy. Of all methods, the BERT-based Transformer model beat others with a test accuracy of $\mathbf{7 9. 3 7 \%}$ and an F1-score of $\mathbf{0. 7 6}$. These results highlight the possibility of using Transformer structures for complex sentiment categorization in the mental health field, hence providing smart and scalable early-warning systems for emotional well-being.