mindsense-bert

🧠 MindSense-BERT — AI-Powered Mental Health Detection

MindSense-BERT is a fine-tuned BERT-based model designed to classify text into 7 mental health categories using real-world Reddit data. It combines state-of-the-art NLP, class imbalance handling, and explainable AI (SHAP) to create a transparent and practical mental health analysis system.

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📌 Model Overview

• Model Type: Transformer (BERT)

• Base Model: bert-base-uncased

• Task: Multi-class Text Classification

• Classes (7):

  • Normal
  • Depression
  • Anxiety
  • Bipolar
  • PTSD
  • Stress
  • Personality Disorder

• Framework: PyTorch + Hugging Face Transformers

• Deployment: Streamlit + Hugging Face Hub

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🎯 Problem Statement

Mental health conditions affect 1 in 4 people globally, but early detection is difficult due to stigma and lack of awareness.

This model aims to:

• Analyze user-written text
• Detect potential mental health conditions
• Enable early awareness and intervention

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📊 Dataset

• Source: Kaggle — Sentiment Analysis for Mental Health
• Size: 53,000+ Reddit posts
• Input: statement (text)
• Label: status (mental health category)

Categories:

• Normal
• Depression
• Anxiety
• Bipolar
• PTSD
• Stress
• Personality Disorder

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⚙️ Preprocessing

• Removed URLs, special characters, duplicates
• Handled missing values
• Tokenized using BERT tokenizer
• Lowercasing (uncased model)
• Padding & truncation applied

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🔬 Training Methodology

Phase 1 — Baseline Models

• TF-IDF (10k features, uni + bi-grams)
• Logistic Regression, Random Forest, SVM

Phase 2 — BERT Fine-tuning

• Pretrained bert-base-uncased
• 3 epochs
• Learning rate: 1e-5
• Batch size: 16
• Trained on Google Colab T4 GPU

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Phase 3 — Class Imbalance Handling

• Weak classes identified: Stress, Personality Disorder

• Techniques used:

  • Word swap
  • Random deletion
  • Key phrase duplication

• Applied custom class weights

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📈 Model Performance

Accuracy Comparison

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Per-Class F1 Score

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🔍 Explainability (SHAP)

This model integrates SHAP (SHapley Additive Explanations) for transparency:

• 🔴 Red words → push prediction toward a class
• 🔵 Blue words → push prediction away

This improves trust and interpretability — critical in healthcare AI.

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🚀 Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "maitry30/mindsense-bert"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

text = "I feel completely hopeless and empty."

inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
outputs = model(**inputs)

prediction = torch.argmax(outputs.logits, dim=1).item()
print(prediction)

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🧪 Example Predictions

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🛠 Tech Stack

• Python 3.10
• PyTorch
• Hugging Face Transformers
• Scikit-learn
• SHAP
• Pandas, NumPy
• Matplotlib, Seaborn
• Streamlit (UI)
• Hugging Face Hub (model hosting)

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⚠️ Limitations

• Limited to English text
• May struggle with sarcasm or slang
• Depends on dataset quality
• Not suitable for real clinical diagnosis

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⚖️ Ethical Considerations

• This model is for educational purposes only
• Not a replacement for mental health professionals
• Should not be used for medical decisions
• Predictions require human interpretation

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🔐 Safety Notice

• ❌ Not a diagnostic tool
• ❌ Not for emergency use
• ✅ Can assist awareness and research

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🚀 Future Work

• Multilingual support (Hindi + regional languages)
• Voice-based mental health detection
• Multi-modal AI (text + physiological signals)
• Explainable AI improvements
• Production deployment (AWS/Azure)

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👤 Author

Maitry

• GitHub: https://github.com/Maitry09/mindsense-mental-health
• Hugging Face: https://huggingface.co/maitry30
• Live App: https://mindsense.streamlit.app

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📄 License

MIT License

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