Here's a few things I've gleamed from experimenting with it:
- It uses a unigram language model. You can take the same text, randomly permute the words, and you get the same score. This means it also can't be using things like POS tagging, phrases, etc.
- It normalizes words by making all letters lowercase. The exact same text in all upper case has the same score.
- The score is eventually normalized by the length of the text. The same text copied multiple times gets the same score.
- It does not form a valid probability distribution, as someone's managed to get some 1.16's. This makes me believe it's not a Naive Bayes classifier giving you the P(Bullshit|Text). Though this is what I originally thought it would be.
- It uses a unigram language model. You can take the same text, randomly permute the words, and you get the same score. This means it also can't be using things like POS tagging, phrases, etc.
- It normalizes words by making all letters lowercase. The exact same text in all upper case has the same score.
- The score is eventually normalized by the length of the text. The same text copied multiple times gets the same score.
- It does not form a valid probability distribution, as someone's managed to get some 1.16's. This makes me believe it's not a Naive Bayes classifier giving you the P(Bullshit|Text). Though this is what I originally thought it would be.