A collection of scripts to parse and analyze exported Google Voice data.

So far it just parses the data and runs a few Naive Bayes classifiers, but I'm looking to expand.

0. Download and extract your Google Voice data.

1. Install the dependencies with something like pip install -r requirements.txt.

2. Copy settings_example.py to settings.py and configure appropriately.

3. Run gv_to_db.py to load everything into a SQLite database.

4. Run who_from.py to run a few simple analyzers using Naive Bayes.

• people_with_many_texts(n) classifies the texts of people who have sent more than n texts.

• recipient_is(name) classifies texts into either name or not_name.

• split_me_not_me() classifies texts into either me or not_me depending on the sender.

1. Get your training and test sets from one of the classifiers.

2. Run build_classifier(training_set).

3. Run interactive on your classifier.

4. Enter some messages.

MIT/X11 licensed, except for emoticons.py and twokenize.py.

emoticons.py and twokenize.py are copyright Brendan O'Connor, Michel Krieger, and David Ahn and licensed under the Apache License 2.0.