Aim is to read texts from twitter and facebook files and use linguistic markers present in them to analyse mood. The mood analysed over the last 10 posts (or over time, if it is a frequent user) is the current mood. This shall also measure post interaction, i.e. if a user has been engaging with posts tagged as "sad" more recently the current mood will be hinting towrds sad. The project aims at creating a ai system that analyses social media posts. A continually occuring 'sad' indicator will be hinting depression.
- Python - Install Python
- Text Editor
- Pandas (
$ pip install pandas) - Tensorflow (
$ pip install tensorflow)
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License. See LICENSE.txt for more information.