Collect tech news articles and tweets in one place and filter out the interesting content by tagging items as either interesting or boring (vote up/down).
Ensure you have Python 3 installed. The application is tested with Python 3.6.1 on macOS Sierra.
If you are on macOS and use homebrew:
brew install python3
Create virual environment and activate it:
python3 -m venv .env
source .env/bin/activate
Install dependencies:
python -m pip install -r requirements.txt
You can activate different data sources in config.yml. If you want to activate Twitter you need to create a Twitter application with your own account on https://dev.twitter.com. Copy the required keys and secrets into the config.
The application will automatically collect data from all the sources that are configured in the config under source.
Execute run.sh (or see its content how to do it manually).
Point your browser to http://localhost:5000.
Content of the enabled sources will appear in the corresponding sections. By up- and downvoting items the systems learns the user's interests. For items to appear in the interesting section for a particular data source one needs to have at least one upvote and one downvote. The more data is gathered the more accurate the prediction becomes (200+ tagged items for a data source is a good start).
There is an extensible list of classifier pipelines defined in classifier.py that is used for evaluation. Run python eval.py to get cross validation metrics for each pipeline (precision, recall, etc.).