TL;DR

A Markov bot trained using Jeb Bush’s emails is tweeting at @jebsemails. The code is open source.

What is this?

One night I was bored. So, like any intellectually curious, red blooded American, I thought to myself

What’s Jeb Bush up to?

Well, it turns out, quite a lot. Did you know he might run for president? He’s also published all of his emails online.

So I thought, why not use the resulting text to train a Markov bot and put it online? This is the code that generated the content which is being posted on Twitter.

I asked the bot if it had any plans to run for president, it replied (emphasis added):

HURRICANE FLOYD!

So, as you can see, it’s keeping it’s options open.

How do I use it

You’ll need Scala and Ruby. You’ll also need to download and unpack the pst files from Jeb’s website.

Building the Scala Library

Scala is used to parse the pst files and output a text file. To build the jar file use the following command:

sbt assembly

This project uses ruby, so you’ll need to bundle:

bundle install

Running

You interact with the library using a command line application (Thor app).

You can find all the from addresses in the pst files using the following command:

bundle exec ./bin/markov from > output/from.txt

If you want to find the most email addresses that appear the most frequently in the from address, use the following command:

sort output/from.txt | uniq -c | sort -r > output/unique_from.txt
head -3 output/unique_from.txt

Now, it extract the bodies, use the following command:

bundle exec ./bin/markov bodies --emails /O=BUSH-BROGAN\ 2002/OU=FIRST\ ADMINISTRATIVE\ GROUP/CN=RECIPIENTS/CN=JEB /O=JEB\ BUSH/OU=FIRST\ ADMINISTRATIVE\ GROUP/CN=RECIPIENTS/CN=JEB jeb@jeb.org > output/bodies.txt

Next, you can build the model:

bundle exec ./bin/markov build --content output/bodies.txt

And finally, you can generate some sentences:

bundle exec ./bin/markov generate --count 5 > output/sentences.txt

Buffer your responses

I use buffer to post to twitter at regular intervals. You will need your buffer token, which is left as an excercise for the user.

First, you can get the profile ids of the accounts you’d like to post to:

bundle exec markov buffer_profiles --token $BUFFER_TOKEN

Next, given the sentences.txt file we generated before, buffer those sentences to two buffer profiles:

bundle exec markov buffer output/sentences.txt --token $BUFFER_TOKEN --profiles PROFILE_ID1 PROFILE_ID2

That’s it, you’re Markov bot is now live.