I’m a huge fan of the sci-fi cartoon Rick and Morty. I was interested in replicating some of the personalities in the show, so I went out to design a Twitter bot to do exactly that.
Collecting the data
For sample text, I used episode transcripts collected from Rickipedia. I used BeautifulSoup in Python to parse through the website and scrape the transcript pages. With these, I compiled a list of quotes for individual characters. For this project, I chose to base the bot around Morty, one of the title characters.
For the quote generation model, I used a Markov chain. Simply put, a Markov chain is a random process with states changing over time, in which each state is only dependent on the state immediately before it.
For text, each individual word in a sentence or paragraph can be thought of as a state in a Markov chain. So starting with a seed word, the next words are chosen randomly based on their frequency of presence in the corpus of text (the list of quotes). The model was built in Python using markovify.
Deploying on Twitter
After collecting the data and building a text generation model, the next step is to broadcast Morty’s thoughts to the world. I deployed my bot on an AWS EC2 instance, where it sends tweets through the Twitter API and tweepy.
You can see the results here. The tweets are generated using a somewhat random process, so not all of them are guaranteed to make sense. Nonetheless, I hope that they can still generate a few laughs.