Dankenstein
Dankenstein is a Markov Chain Twitter Bot generator, based on making a mashup of different corpora.
It is described in this blogpost.
Dependencies
The software itself is implemented using Bash, Make, and Python.
You'll need a Twitter Account and a Twitter application. From the latter, you'll need a consumer key, a consumer secret, an access token and an access token secret.
Enter these into twitterCredentials.sh
To generate the supported datasets you'll need :
- Poppler
brew install poppleron macOSsudo apt-get install -y poppler-utilson Ubuntu
After that, you can run the importEnv.sh script in the dev-folder. This will create a Python virtual environment (conda if you have it, virtualenv otherwise) called dankenstein, and install all python-dependencies.
If you'd rather install them separately, they are: Tweepy, darklyrics, wikiquote, markovify; (optionally nltk)
Usage
Usage is based around Make. The following options are available:
make corpora:
Builds all corpora
make list:
Lists all available corpora
make combinations:
Prints all possible combinations of two candidates - may include duplicates (with switched positions)
make model ARGS="corpus1 corpus2 [(scale1 scale2) stateSize overlapTotal overlapRatio tries sentences modelComplexity]":
Generates a model based on two corpora (the only required args).
If you define scale 1, you must define scale 2.
All options inside the square brackets are optional.
ARGS:
corpus1
string - the name of the first corpus
no default
corpus2
string - the name of the second corpus
no default
scale1
float/int - scale of corpus1 relative to corpus2
defaults to 1
scale2
float/int - scale of corpus2 relative to corpus1
defaults to 1
stateSize
int - state size of the Markov chain
defaults to 2
overlapTotal
int - maximum sequential words that overlap with a sentence from the corpora
defaults to 15
overlapRatio
int - maximum percentage of overlap with a sentence from the corpora
defaults to 70
tries
int - attempts to make an original sentence
defaults to 10
sentences
int - number of sentences
defaults to 5
modelComplexity
string - naive | expert
defaults to "naive"
EXAMPLE:
make model ARGS="tcm jobs 2 1 2 15 30 10 25 expert" && make sentences
make sentence:
Outputs one sentence based on an available (previously trained) model.
make sentences:
Outputs the amount of sentences given as an argument during model generation.
make clean:
Deletes all corpora.
make tweet:
Posts a tweet.
It is possible to se all combinations for a given candidate, e.g. make combinations | grep "picard +".
Deployment:
Twitter secrets are set via environment variables, e.g.
export CONSUMER_KEY="consumer_key"
export CONSUMER_SECRET="consumer_secret"
export ACCESS_KEY="access_token"
export ACCESS_SECRET="access_token_secret"An example deployment is described in the bottom of this blogpost.