sentiment

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 and the lead authors's github repos are: https://github.com/vivekn/sentiment 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-... ...which I found useful as a jumping off point. Good luck!

While 95% accuracy would be a really phenomenal achievement, an accuracy in the range of 85-90% is achievable using methods simpler than deep neural nets. I have done some work on sentiment analysis in the past. I used a Naive Bayes model with some enhancements like n-grams, negation handling and information filtering and was able to get more than 88% accuracy on a similar dataset based on movie reviews. You can find more details here -http://arxiv.org/ftp/arxiv/papers/1305/1305.6143.pdf and the code over here - https://github.com/vivekn/sentiment/blob/master/info.py