Project Objectives¶

Research Goals and Technical Direction¶

This project aims to design, evaluate, and document a longitudinal AI framework for depression and anxiety severity assessment in Arabic text.

The objectives extend beyond simple classification and focus on structured modeling, ethical deployment, and research reproducibility.

Develop Dual Severity Models

Train independent Support Vector Machine classifiers for depression and anxiety severity prediction using embedding-based feature representations.
Advance Arabic NLP Research

Apply multilingual embedding models to Arabic text and explore challenges related to underrepresented languages in mental health NLP.
Enable Longitudinal Monitoring

Move from static cross-sectional classification toward temporal modeling of mental health trajectories using structured alert logic.

Quantitative Performance Analysis

Evaluate models using accuracy, precision, recall, F1-score, and AUC metrics with stratified data splits.
Error and Reliability Assessment

Analyze borderline cases and misclassifications to assess model robustness and generalization stability.

Ethical Data Usage

Utilize synthetic longitudinal datasets to ensure privacy preservation and minimize sensitive data exposure.
Transparency and Boundaries

Position the system strictly as a research-driven decision-support tool rather than a diagnostic instrument.
Interpretability and Construct Validity

Align linguistic patterns and model behavior with established psychological indicators of depression and anxiety.

IEEE-Style Research Paper

Document methodology, experiments, results, and alert framework design.
Reproducible Experimental Pipeline

Provide structured preprocessing, embedding generation, training, and evaluation workflows.
Technical Presentation

Summarize findings, contributions, and future directions in a structured academic format.

Expanded Datasets

Incorporate real-world longitudinal data and clinical validation.
Sequential Modeling

Explore temporal deep learning architectures such as LSTM or transformer-based sequence models.
Domain-Specific Embeddings

Refine Arabic mental health embeddings to improve semantic sensitivity.