https://github.com/11sshukla/mental_health_story_analyzer
This project is an AI-powered NLP pipeline that analyzes user-written stories to detect emotions.
https://github.com/11sshukla/mental_health_story_analyzer
ann artificial-intelligence deep-learning keras machine-learning nlp python streamlit tensorflow
Last synced: 4 months ago
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This project is an AI-powered NLP pipeline that analyzes user-written stories to detect emotions.
- Host: GitHub
- URL: https://github.com/11sshukla/mental_health_story_analyzer
- Owner: 11SShukla
- Created: 2025-06-27T19:47:10.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-06-27T20:32:05.000Z (4 months ago)
- Last Synced: 2025-06-27T20:36:17.460Z (4 months ago)
- Topics: ann, artificial-intelligence, deep-learning, keras, machine-learning, nlp, python, streamlit, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 0 Bytes
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Mental_Health_Story_Analyzer
This project is an AI-powered NLP pipeline that analyzes user-written stories to detect emotions.
A powerful NLP-based tool designed to **analyze personal mental health stories**, detect the **underlying emotion**, identify **cognitive distortions**, and provide **supportive advice**. This project aims to combine **deep learning**, **rule-based logic**, and a **user-friendly Streamlit interface** to offer empathetic feedback and promote mental well-being.
## 💡 Key Features
- 🧾 **Emotion Detection** — Uses a trained ANN model to classify emotions like joy, sadness, anger, etc.
- 🧠 **Cognitive Distortion Detection** — Detects thought patterns (e.g., overgeneralization, catastrophizing) via rule-based or ML methods.
- 💬 **Advice Generation** — Offers constructive and supportive advice based on detected emotions.
- 🎛️ **Interactive UI** — Streamlit-based interface to enter, analyze, and visualize results in real time.
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## 📂 Project Structure
mental-health-analyzer/
├── app.py # Streamlit app (Frontend)
├── Mental_Health_Story_Analyzer_model_pipeline.py # Core NLP + ML pipeline (Backend)
├── emotion_model.h5 # Trained emotion classification model
├── advice_model.h5 # Trained advice classification model
├── tfidf_vectorizer.pkl # TF-IDF vectorizer for text features
├── emotion_encoder.pkl # Label encoder for emotion labels
├── advice_encoder.pkl # Label encoder for advice labels
├── requirements.txt # List of required libraries
├── README.md # Project documentation