I found this paper useful for a side project I worked on a few months ago, one that made use of n-grams in a naive bayesian classifier: http://arxiv.org/pdf/13

I found this paper useful for a side project I worked on a few months ago, one that made use of n-grams in a naive bayesian classifier: http://arxiv.org/pdf/1305.6143v2.pdf http://arxiv.org/pdf/1305.6143v2.pdf and the lead authors's github repos are: https://github.com/vivekn/sentiment https://github.com/vivekn/sentiment https://github.com/vivekn/sentiment-web https://github.com/vivekn/sentiment-web He's implemented 'negative bi-gram detection' (my phrasing, not his) with this function: https://github.com/vivekn/sentiment/blob/master/info.py#L26-L56 https://github.com/vivekn/sentiment/blob/master/info.py#L26-... ...which I found useful as a jumping off point. Good luck!