Global Mental Health Network Analysis 🧠

Computational Topology of Mental Health Narratives: A Multi-Dimensional Framework for Analyzing Symptom Flow, Structural Cores, and Psycholinguistic Focus.

This project goes beyond simple keyword counting. We transform unstructured mental health research into a structured knowledge graph, allowing us to map the complex "flow" of symptoms and identify the dense cores of various mental health disorders.

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🛠 The Stack

• Language:
• NLP:
• Graph Theory:
• Data Ops:
• Visualization:

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📖 The Story: From Text to Topology

The Problem

Most mental health text analysis treats words as isolated units ("Bag-of-Words"). This misses the direction (does stress lead to insomnia or vice-versa?) and the structure of how symptoms cluster together.

The Solution: Graphs & Math

We model the literature as a Directed Weighted Graph $G = (V, E, W)$:

• Nodes ($V$): Concepts like Anxiety, Depression, or Trauma.
• Edges ($E$): The narrative flow—how often one symptom transitions into another.
• Weights ($W$): The strength of these relationships based on frequency and TF-IDF relevance.

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🚀 Research Highlights

1. Symptom Flow: Identifying "Source" symptoms (triggers) vs. "Sink" outcomes using PageRank.
2. The Diamond Core: Using Maximum Clique Detection to find the most inseparable, dense clusters of symptoms.
3. Thematic Universes: Breaking down the global network into Communities (using Louvain Modularity) to see local symptom "ecosystems."
4. Self-Focus: Measuring internal vs. external focus using the Self-Attentional Ratio.

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⚡ Quick Start (Simplified)

Follow these 3 steps to get the project running locally:

1. Clone & Enter

git clone https://github.com/VAL-Jerono/Mental_health_NLP.git
cd Mental_health_NLP

2. Setup Environment

python -m venv venv && source venv/bin/activate  # macOS/Linux
# OR: venv\Scripts\activate                     # Windows

3. Install Everything

pip install -r requirements.txt || pip install pandas numpy scikit-learn nltk spacy networkx community matplotlib seaborn PyPDF2 pycountry
python -m spacy download en_core_web_sm

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📂 Dataset & Team

Source Data: Mental Health Study/ folder containing 180+ localized research PDFs covering students, athletes, and pandemic-era mental health.

👥 The Team

• Assumpta Mwikali (134022)
• Olive Mideva Muloma (135792)
• Rutendo Julia Kandeya (168332)
• Trevor Anjeyo Vuhyah (224038)
• Valerie Jerono (222331)

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