/mental_health_analysis

sentiment analysis of mental health to predict Normal, Depression, Suicidal, Anxiety, Stress, Bi-Polar, Personality Disorder

Primary LanguageJupyter NotebookGNU General Public License v3.0GPL-3.0

Streamlit App link[https://mentalstatecheck.streamlit.app/]

Mental Health Sentiment Analysis App

This Streamlit application provides a simple tool for analyzing the sentiment of text input and predicting potential mental health statuses like Normal, Depression, Suicidal, Anxiety, Stress, Bi-Polar, and Personality Disorder.

How it works

  • The app utilizes a pre-trained machine learning model that has been fine-tuned on a dataset of labeled statements related to mental health.
  • The model uses TF-IDF vectorization to convert text input into numerical features and then makes a prediction based on these features.
  • The predicted mental health status is displayed to the user.

Usage

  1. Clone the repository:

    git clone https://your-repository-url.git

  2. Install dependencies:

    pip install -r requirements.txt

  3. Run the app:

    streamlit run app.py

  4. Enter a statement in the text area and click "Analyze".

  5. The app will display the predicted mental health status.

Important Notes

  • This app is intended for informational and educational purposes only. It is not a substitute for professional medical advice or diagnosis.

  • The model's predictions are based on patterns learned from the training data and may not always be accurate, acc= 0.77.

  • If you or someone you know is struggling with mental health issues, please seek help from a qualified mental health professional.

  • Contact Information:

  • Support Mental Health Initiatives:

Model Details

  • The model is a Logistic Regression classifier trained on a combined dataset of mental health statements.
  • The model and TF-IDF vectorizer are saved in the models folder as mental_health_sentiment_model.pkl and tfidf_vectorizer.pkl, respectively.
  • You can retrain the model with new data or experiment with different algorithms if needed.

Contributing

Contributions to improve the app or the underlying model are welcome! Please feel free to open issues or pull requests on the repository.

License

This project is licensed under the MIT License.

Disclaimer:

This app is not intended to be a diagnostic tool or a replacement for professional medical advice. If you are concerned about your mental health, please consult a qualified healthcare provider.