Inner Health: Combating Mental Health Challenges with Social Media

Sentiment analysis can be used to analyze the emotional content of social media posts and identify individuals who may be at risk for mental health issues.
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Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. Roadmap
5. Contributing
6. License
7. Acknowledgements

About The Project

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Built With

• Cohere
• Streamlit
• Python

Getting Started

To get a local copy up and running follow these simple steps.

Prerequisites

This is an example of how to list things you need to use the software and how to install them.

• python 3
• pip

Installation

1. Clone the repo

   git clone https://github.com/Tosin5S/team-coherent

2. Install with pip

   pip install -r requirements.txt

Usage

Using Cohere's API makes everything very easy. We analyze our text and classify it using Cohere's classifier API. Then, the result of our analysis will be used by the GPT API to generate a text that matches the post and uses it to comfort, encourage, or support the individual.

This will help to combat mental health challenges associated with social media.

Roadmap

See the open issues for a list of proposed features (and known issues).

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Acknowledgements

• hallelx2 - Halleluyah
• Timadey - Timothy Adeleke
• Tosin5S - Qudus Tosin Aileru

Made with 💙 by Team Coherent