MULTICLASS MENTAL ILLNESS PREDICTION USING LSTM AND NATURAL LANGUAGE PROCESSING TECHNIQUES

Abstract

Mental health disorders represent a growing global concern, with millions of individuals expressing their psychological conditions through digital platforms such as social media. Detecting mental illness from textual data is a complex task due to the emotional depth, informal language, metaphorical expressions, and context-specific cues often embedded within such posts. Traditional machine learning methods and generic language models frequently struggle to capture these subtleties, leading to limited prediction accuracy. To overcome these challenges, this study presents a robust and intelligent framework for multiclass mental illness prediction using advanced deep learning and natural language processing (NLP) techniques. The proposed system integrates domain-adapted transformer models with deep neural networks to improve understanding and classification of mental health-related expressions. Specifically, MentalBERT, a variant of BERT pretrained on mental health corpora, is employed to extract rich contextual features from psychologically sensitive text. Alongside this, MelBERT, a metaphor-aware model, is used to interpret figurative and symbolic language—a common characteristic in users with emotional distress. These transformerbased models are complemented by Convolutional Neural Networks (CNNs) for hierarchical feature extraction and a Bidirectional Long Short-Term Memory (BiLSTM) network to capture long-range semantic dependencies from both past and future contexts of the sequence. This hybrid architecture enables the system to predict multiple classes of mental illness—including depression, anxiety, PTSD, bipolar disorder, and more—with improved accuracy and reliability. The model was trained and evaluated on a labeled dataset of social media posts, with performance metrics indicating superior results over traditional methods. The findings of this research highlight the importance of domain-specific modeling, figurative language interpretation, and sequential deep learning techniques in building an effective and scalable mental health detection system. This work contributes to the broader goal of mental health awareness by providing a tool that can assist professionals in early detection and intervention strategies through digital linguistic analysis. Index Terms – Mental Illness Prediction, LSTM, Natural Language Processing, Multiclass Classification, Deep Learning, Sentiment Analysis, Mental Health Monitoring, Text Mining, Machine Learning, Artificial Intelligence.