Natural Language Processing Algorithm for mood of an email

One simple question (but I haven't quite found an obvious answer in the NLP stuff I've been reading, which I'm very new to):

I want to classify emails with a probability along certain dimensions of mood. Is there an NLP package out there specifically dealing with this? Is there an obvious starting point in the literature I start reading at?

For example, if I got a short email s开发者_JAVA技巧omething like "Hi, I'm not very impressed with your last email - you said the order amount would only be $15.95! Regards, Tom" then it might get 8/10 for Frustration and 0/10 for Happiness.

The actual list of moods isn't so important, but a short list of generally positive vs generally negative moods would be useful.

Thanks in advance!

--Trindaz on Fedang #NLP

You can do this with a number of different NLP tools, but nothing to my knowledge comes with it ready out of the box. Perhaps the easiest place to start would be with LingPipe (java), and you can use their very good sentiment analysis tutorial. You could also use NLTK if python is more your bent. There are some good blog posts over at Streamhacker that describe how you would use Naive Bayes to implement that.

Check out AlchemyAPI for sentiment analysis tools and scikit-learn or any other open machine learning library for the classifier.

if you have not decided to code the implementation, you can also have the data classified by some other tool. google prediction api may be an alternative.

Either way, you will need some labeled data and do the preprocessing. But if you use a tool that may help you get better accuracy easily.