| --- |
| language: ["en"] |
| license: mit |
| tags: |
| - mental-health |
| - bert |
| - text-classification |
| - nlp |
| - transformers |
| - explainable-ai |
| - shap |
| datasets: |
| - kaggle-sentiment-analysis-mental-health |
| metrics: |
| - accuracy |
| - f1 |
| --- |
| |
| # 🧠 MindSense-BERT — AI-Powered Mental Health Detection |
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| 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 |
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| * **Model Type:** Transformer (BERT) |
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| * **Base Model:** `bert-base-uncased` |
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| * **Task:** Multi-class Text Classification |
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| * **Classes (7):** |
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| * Normal |
| * Depression |
| * Anxiety |
| * Bipolar |
| * PTSD |
| * Stress |
| * Personality Disorder |
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| * **Framework:** PyTorch + Hugging Face Transformers |
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| * **Deployment:** Streamlit + Hugging Face Hub |
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|
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| ## 🎯 Problem Statement |
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| Mental health conditions affect **1 in 4 people globally**, but early detection is difficult due to stigma and lack of awareness. |
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| This model aims to: |
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| * Analyze user-written text |
| * Detect potential mental health conditions |
| * Enable early awareness and intervention |
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|
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| ## 📊 Dataset |
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| * **Source:** Kaggle — *Sentiment Analysis for Mental Health* |
| * **Size:** 53,000+ Reddit posts |
| * **Input:** `statement` (text) |
| * **Label:** `status` (mental health category) |
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| ### Categories: |
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| * Normal |
| * Depression |
| * Anxiety |
| * Bipolar |
| * PTSD |
| * Stress |
| * Personality Disorder |
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|
|
| ## ⚙️ Preprocessing |
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| * Removed URLs, special characters, duplicates |
| * Handled missing values |
| * Tokenized using BERT tokenizer |
| * Lowercasing (uncased model) |
| * Padding & truncation applied |
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|
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| ## 🔬 Training Methodology |
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| ### Phase 1 — Baseline Models |
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| * TF-IDF (10k features, uni + bi-grams) |
| * Logistic Regression, Random Forest, SVM |
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| ### Phase 2 — BERT Fine-tuning |
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| * Pretrained `bert-base-uncased` |
| * 3 epochs |
| * Learning rate: `1e-5` |
| * Batch size: `16` |
| * Trained on Google Colab T4 GPU |
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|
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| ### Phase 3 — Class Imbalance Handling |
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| * Weak classes identified: Stress, Personality Disorder |
| * Techniques used: |
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| * Word swap |
| * Random deletion |
| * Key phrase duplication |
| * Applied custom class weights |
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| --- |
|
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| ## 📈 Model Performance |
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| ### Accuracy Comparison |
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| | Model | Accuracy | |
| | ------------------- | ----------- | |
| | Logistic Regression | ~78% | |
| | Random Forest | ~74% | |
| | SVM | ~82% | |
| | BERT (initial) | ~83% | |
| | **BERT (final)** | **~87–89%** | |
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| ### Per-Class F1 Score |
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| | Category | F1 Score | |
| | -------------------- | -------- | |
| | Normal | 0.91 | |
| | Depression | 0.89 | |
| | Anxiety | 0.86 | |
| | Bipolar | 0.84 | |
| | PTSD | 0.87 | |
| | Stress | 0.82 | |
| | Personality Disorder | 0.80 | |
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| --- |
|
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| ## 🔍 Explainability (SHAP) |
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| This model integrates **SHAP (SHapley Additive Explanations)** for transparency: |
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| * 🔴 Red words → push prediction toward a class |
| * 🔵 Blue words → push prediction away |
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| This improves trust and interpretability — critical in healthcare AI. |
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| --- |
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| ## 🚀 Usage |
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| ```python |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| import torch |
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| model_name = "maitry30/mindsense-bert" |
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| tokenizer = AutoTokenizer.from_pretrained(model_name) |
| model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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| 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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| --- |
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| ## 🧪 Example Predictions |
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| | Input Text | Predicted Class | |
| | --------------------------- | --------------- | |
| | "I feel hopeless and empty" | Depression | |
| | "I keep having nightmares" | PTSD | |
| | "My mood swings a lot" | Bipolar | |
| | "I feel fine today" | Normal | |
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| --- |
|
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| ## 🛠 Tech Stack |
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| * 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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| --- |
|
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| ## ⚠️ Limitations |
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| * Limited to English text |
| * May struggle with sarcasm or slang |
| * Depends on dataset quality |
| * Not suitable for real clinical diagnosis |
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| --- |
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| ## ⚖️ Ethical Considerations |
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| * 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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| --- |
|
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| ## 🔐 Safety Notice |
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| * ❌ Not a diagnostic tool |
| * ❌ Not for emergency use |
| * ✅ Can assist awareness and research |
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| --- |
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| ## 🚀 Future Work |
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| * 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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| --- |
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| ## 👤 Author |
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| **Maitry** |
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| * 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 |
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| MIT License |
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| > ⭐ If you found this model useful, consider giving the project a star! |
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