TweetkovChain - the tweet Markov-chain text generator

Convert your tweets to awesome fun text.

1. retrieve your tweet from twitter (via the settings dialog)
2. extract them to a local directory
3. do like the class TweetkovRunner does

General process

1. map tweets from JSON to objects
2. get tweet text from objects
3. create dictionary with a window sized
4. generate funny sentences

What is a window size?

The window size (also known as the order of a Markov Chain) determines the number of tokens in the prefix which are examined for the search of an existing suffix. The mapping from prefix->suffix is called a dictionary.

While a window size of one suffices for a small text base the textual stringency rises with the window size because more prefix tokens are taken into consideration. And while this CAN lead to a better textual stringency it also means that the word histogram MAY look totally different in terms of probable suffix selection. Exactly one suffix for a prefix has a general probability of p=1.0 for selection which in turn leads to a very high probability to re-generate already existing sentences.

Given these sentences the window size the significance gets a bit clearer when you look at the two examples below.

Example for a window size of 2

prefix (window size=1)	suffixes
hello	[world, again, my]
world	[]
again	[]
my	[old, little]
little	[pony]
old	[friend]
pony	[]
friend	[]

Example for a window size of 2

prefix (window size=1)	suffixes
hello world	[]
hello again	[]
hello my	[old]
my little	[pony]
my old	[friend]
little pony	[]
old friend	[]