The repository contains modules to extract features from text and web pages. The features can be uses as a training data for machine algorithms or to improve your applications. Some of the methods are geared towards news articles but they also work with other domains. If you are not a Python programmer or need to do feature engineering at a larger scale, you can use the API
git clone [email protected]:webeng/feature_engineering.git
cd feature_engineering
virtualenv env
source env/bin/activate
pip install -r requirements.txt
Run python link.py to see an example.
If you want fast keyword extraction, you will have to install HDFS. Also, you might have to install pytables by running this command sudo HDF5_DIR=/usr/local/hdf5/ pip install tables. Also add /usr/local/hdf5/lib/ to LD_LIBRARY_PATH. I'll try develop a slower version without the HDFS.
You can run each module individually to see examples.
Extracts the author of an article given a link.
Classify a document
Named entity recognition.
Extracts feed urls given a link.
Extracts images in a HTML document and ranks them by surface.
Extracts the main text of page given a url.
Extracts main keywords in a text document using term frequency-inverse document frequency.
Analyses the sentiment of a text or keyword.
Extracts page titles.