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Limitations

Technical and Methodological Constraints

While the framework demonstrates strong classification performance and structured longitudinal monitoring, several limitations must be acknowledged to ensure responsible interpretation.

1. Synthetic Dataset Constraints

  • Simulated Data Distribution

The dataset consists entirely of AI-generated synthetic entries.
Real-world linguistic variability may be greater than simulated patterns.

  • Potential Over-Separation

Controlled generation may produce clearer class boundaries than naturally occurring mental health narratives.

  • Self-Report Simulation

Severity labels are inspired by PHQ-9 and GAD-7 guidelines but are not clinically validated diagnoses.

Although synthetic design ensures privacy preservation, it limits direct generalization to real-world populations.

2. Limited Dialect and Cultural Coverage

Arabic is linguistically diverse. The dataset does not explicitly model:

  • Regional dialect variation
  • Code-switching behavior
  • Informal or highly colloquial expressions

Future validation on diverse dialect corpora is required for broader applicability.

3. Entry-Level Independent Classification

The current system classifies each text entry independently.

  • Temporal dependencies are not modeled within the classifier itself.
  • Longitudinal alerts are applied post-prediction through rule-based logic.

Sequence-aware architectures could better capture progression dynamics.

4. Embedding Generalization

The multilingual embedding model is not fine-tuned on mental health–specific Arabic corpora.

  • Subtle emotional nuance may not be fully captured.
  • Indirect or metaphorical distress signals may require domain adaptation.

5. Model Scope and Complexity

Support Vector Machines with RBF kernels provide strong generalization and interpretability. However:

  • Large transformer-based sequence models may capture deeper contextual interactions.
  • Ordinal severity modeling could be enhanced with specialized ranking or regression frameworks.

6. Non-Clinical Positioning

The system:

  • Is not clinically validated
  • Is not approved for diagnostic or treatment decisions
  • Should not replace professional mental health evaluation

It is designed exclusively as a research-oriented decision-support prototype.

Interpretation of High Performance

The near-perfect classification results reflect:

  • Controlled synthetic data generation
  • Clear severity separation in embedding space
  • Stratified evaluation design

Real-world performance may differ when applied to authentic clinical or user-generated text.

For further technical detail and experimental discussion, refer to the IEEE Paper.