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https://github.com/eivindarvesen/dankenstein
Markov Chain Twitter Bot generator
https://github.com/eivindarvesen/dankenstein
bash bot markov-chain markov-text python twitter twitter-bot
Last synced: about 3 hours ago
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Markov Chain Twitter Bot generator
- Host: GitHub
- URL: https://github.com/eivindarvesen/dankenstein
- Owner: EivindArvesen
- License: bsd-3-clause
- Created: 2018-06-20T14:39:43.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2022-12-08T02:14:16.000Z (almost 2 years ago)
- Last Synced: 2023-11-07T16:16:21.453Z (about 1 year ago)
- Topics: bash, bot, markov-chain, markov-text, python, twitter, twitter-bot
- Language: Shell
- Homepage: https://www.eivindarvesen.com/blog/2018/06/20/paging-dr--dankenstein
- Size: 567 KB
- Stars: 119
- Watchers: 8
- Forks: 6
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Dankenstein
Dankenstein is a Markov Chain Twitter Bot generator, based on making a mashup of different corpora.
It is described in [this blogpost](https://www.eivindarvesen.com/blog/2018/06/20/paging-dr--dankenstein).
## 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](https://poppler.freedesktop.org/)
- `brew install poppler` on macOS
- `sudo apt-get install -y poppler-utils` on 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.
```bash
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](https://www.eivindarvesen.com/blog/2018/06/20/paging-dr--dankenstein).