This is a library to analyze the political sentiment of text. We use Deep Learning models to evaluate the speaker's political leaning (liberal/democrat or conservative/republican). This was done as the final project to an MIT course, 17.835 in 2018. Trained models can be found in the bin.
After cloning this repository, setup a python virtual environment. This can be done with
source setup
this can also be used after initially setting up the environment to activate the virtual environment.
The project is structured in the following fashion.
.
+-- _setup
+-- _bin
| +-- this includes binary files like weights
| or cached data files.
+-- _data
| +--_convote
| +-- this is the cornell vote database
| that includes transcriptions of congressional
| debates from 2005 along with labels for
| both for the political party of the speaker and
| whether the statement is for/against the bill.
| +--_twitter
| +-- data we have collected from the public twitter api.
+-- _preprocessing
| +-- code to preprocess the various datasets into usable formats
| for the various models
+-- _models
| +-- contains python code that exports keras models