Sentiment analysis is fiendishly hard to solve well, but easy to solve to a first approximation. I've been frustrated that there have been no easy free libraries that make the technology available to non-specialists like me. The problem isn't with the code, there are some amazing libraries like NLTK out there, but everyone guards their training sets of word weights jealously. I was pleased to discover that SentiWordNet is now CC-BY-SA, but even better I found that Finn Årup has made a drop-dead simple list of words available under an Open Database License!
With that in hand, I added some basic tokenizing code and was able to implement a new text2sentiment API endpoint for the Data Science Toolkit:
http://www.datasciencetoolkit.org/developerdocs#text2sentiment
Give it a try, it's as simple as a CURL call from the terminal:
curl -d "I hate this hotel" "http://www.datasciencetoolkit.org/text2sentiment"
{"score": -3.0}
I've been having a blast with it, simple-minded as it is, so I hope you do too!
Pete Warden's blog 